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23

Increasing AI Agriculture in Emerging Countries and Countries with Low Economy

Submitted by

Sateesh Rongali

A Proposed Study Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Education/Philosophy in Leadership

with a specialization in Computer Science

Judson University

Elgin, Illinois

08-15-2021


Abstract

This research study focuses on exploring the field of AI agriculture from an emerging countries’ standpoint. The goal of the research study is understanding the reason for the decline in agricultural productivity and popularity in emerging countries and exploring how AI agriculture can help the countries improve agricultural processes. The research study will also explore the major limitations that have restricted the adoption of AI agriculture in these emerging countries. After providing a brief introduction into the current state of agriculture in emerging countries, the research study defines the core research questions that would drive the study. To gain further insights into agriculture in emerging countries and the limitations of AI adoption, the research study provides an in-depth literature review that explores literary sources focused on the relevant topics. The main research methodology of the proposed research study will be document analysis that will identify the relevant themes in both historical and current peer-reviewed literary sources exploring the topics of AI agriculture, agriculture in emerging countries, and agricultural limitations. In addition, the research study will also conduct qualitative interviews to participants selected from the AI agriculture industry. To ensure that the research study is focused on emerging countries, the proposed study will ensure that the document selection is strictly based on topic and thematic relevance. The participants for the interviews will be selected through snowball sampling. In addition, the proposed study also provides brief insights into the expected limitations and ethical considerations surrounding the research. Through the research methodology, the proposed study aims to arrive at valid and reliable results that helps identify AI agricultural methods that can improve agricultural production and popularity in emerging countries. Comment by Mellissa Gyimah: No indent on abstracts as per APA

Table of Contents


Chapter 1: Introduction
2

Background
2

Problem Statement and Significance
4

Theoretical Framework 4

Researcher’s Positionality
8

Purpose
8

Research Question(s)
9

Significance
10

Definition of Terms
11

Summary
11

Chapter 3: Introduction
13

Statement of the Problem
14

Research Question(s)
14

Research Methodology
15

Research Design
15

Study Population & Sample Selection
16

Data Collection Methods
17

Sequential Document Selection 18

Qualitative Interview 18

Data Collection Procedures
18

Data Analysis & Procedures
19

Validity & Reliability
20

Ethical Consideration
21

Limitations
22

Summary
23


Chapter 1: Introduction

Background

Agriculture has been a field that is gradually declining in popularity in several countries around the world. The rate of growth of the global demand for agricultural products has also started to decline in the recent past. This is particularly significant in countries that are referred to as developing and having low economy that were dependent on agriculture (Sivarethinamohan et al., 2020). The number of agricultural lands in developing countries like India have started to decrease. This decrease can be attributed to several factors including an increase in modernization which has changed the way of life of people from doing agriculture as a way of earning their living to other modernized means and the decrease of groundwater levels in several regions which has affected the water needed for irrigating the agricultural farms. Although this decrease in popularity might feel insignificant, it might result in disastrous effects in the long run (Sivarethinamohan et al., 2020). Comment by Mellissa Gyimah: Nice! Comment by Mellissa Gyimah: cite

A decline in agricultural production can significantly impact countries with low economy because it further reduces their economy. An increase in agricultural production helps lower food prices and increases the country’s ability to do commerce based on the agriculture products. Therefore, it is important for these countries to improve their economic condition. In addition to increased modernization and decreasing water levels, most countries also face a decrease in agricultural labor (Sivarethinamohan et al., 2020). This is because most of the youths of the countries do not view agriculture as a viable option for sustenance or growth. Agriculture is also not viewed in a positive light in most of these societies, which also adds to the factor. They are more attracted to other fields that provide them more money and increase their status in the society. Since this mentality is inbred into most of the societies, the reformation of such ideas will take significantly more time (Sivarethinamohan et al., 2020). Comment by Mellissa Gyimah: do you have evidence for this?

Due to these factors, most of the agriculture in emerging and low economy countries are carried out by an older population. This poses several problems for the economy. The lack of a younger agricultural labor population makes agriculture a non-sustainable option for economic growth. As mentioned earlier, the lack of agriculture could cause economic disruptions. There is also the fact that the older population is unable to pass on their knowledge to other generations because of the lack of interest (Sivarethinamohan et al., 2020; Tzachor, 2021). Thus, farmers in these countries are less able to take advantage of other areas that produce food or products. If these issues are not solved, further problems may arise such as social unrest or political instability within the populations. This poses a threat to emerging economies that are dependent on agricultural production (Sivarethinamohan et al., 2020).

Problem Statement and Significance

The main problem behind the decrease in agriculture in emerging and low economy countries is the decrease in the significance and popularity of agriculture. Because of modernization, the younger population in most of the countries do not understand the value of agriculture in their economy. This could be partially attributed to the growth of various industries and their marketing ability (Tzachor, 2021). This has attracted many youths in the countries to ignore farming as a viable option for their economic or social growth. As more and more people gyrate towards modern fields and industries, they have started occupying more land in the countries. This has resulted in the transformation of valuable agricultural lands into factories, companies and residential areas in most of the countries (Tzachor, 2021). Comment by Mellissa Gyimah: This is a big claim..cite to support it Comment by Mellissa Gyimah: Gyrate? Or gravitate

The lack of agricultural knowledge is also a significant factor in developing countries. Knowledge of farming is extremely important for developing countries to manage an agricultural process. Since most emerging and low economy countries need to grow their economy rapidly, they are forced to disregard agriculture as one of the main sources of economy and focus on modern industries and companies that provide opportunities for rapid growth (Tzachor, 2021). To improve agricultural growth, these countries need revolutionary methods that can increase production at lower costs. But this is a challenge as older people contribute to most of the active population of farmers. This has impacted technological and technical advancements in the agricultural field, which is a necessity to mitigate the existing threat to agriculture in most of these countries (Tzachor, 2021). This paper will therefore seek to induldge ins a extensive discussion looking at the use of AI in agricultural sector and consider how the same can be used in looking at how countries can develop their production activities Comment by Mellissa Gyimah: I would use another word here

Theoretical Framework Comment by Mellissa Gyimah: This should be centred

The term “AI” refers to information processing and intelligence. The general idea is that this technology is used to learn and master, and to build applications with that knowledge. In most cases, the information processing and intelligent nature of such a system is what is taught in the different literatures that will be referenced and discussed in this proposed study. The main goal of this proposed study is to explore agriculture in emerging and low economy countries and find ways to induce the use of Artificial Intelligence (AI) (Jha et al., 2019). The theoretical framework for the proposed study will focus on compiling instances of AI usage in global agriculture and explore the possibilities and challenges that are involved in the same, some of the theories include metric embedding, cryptography, computational geometry etc. The proposed study will research the concepts through the exploration of various literary resources that are based on AI Agriculture to develop a comprehensive and comprehensive understanding of the field. Furthermore, the research will look at the practical and social challenges that arise from the use of such technologies, with the aim of encouraging the use of AI technologies in agriculture (Jha et al., 2019). Comment by Mellissa Gyimah: Are these things you can discuss in this actual section?

This study will focus on the development and adoption of AI as a means of agriculture, which is crucial for future economic development and to make large scale agricultural production more efficient in emerging countries and countries with lower economies. The use of Artificial Intelligence system in the field of agriculture is rapidly increasing (Jha et al., 2019). There have been several breakthroughs and advances in AI and some countries have been able to leverage the technology through the development of AI programs and systems. In many of the countries, the economic output as a result of the advances made in agricultural technology has been greatly increasing. In many of the nations where the production has increased, the development of AI has been a critical help in substantially increasing agricultural productivity and production (Jha et al., 2019). This is evidenced in several literary papers. Comment by Mellissa Gyimah: Cite…I would love to hear more about these breakthroughs

The growth of agricultural technology as a field provides great opportunities for emerging and low economy countries that are struggling to improve their agricultural production. Thus, the theoretical framework will focus on exploring the use of technology, particularly AI technology in the global agricultural field. While exploring the opportunities for AI-induced agriculture in emerging countries, it is important to understand the different types of AI technology that are being used in agriculture (Jha et al., 2019). With the aid of literary papers, we can learn that there are several different types of AI systems including machine learning algorithms, deep learning, and computer vision for increasing agricultural productivity and economic growth. A variety of AI systems are being tested and used in today’s agro-industry and, as such, the concept of using AI-enhanced agriculture is a field that has great potential and the use of the field as a solution to poverty alleviation and other environmental problems will be explored further in the future (Jha et al., 2019). Example of AI systems being used in agro-industry include predictive analytics, crop and soil monitoring, agricultural robots, etc. Predictive analytics helps farmers predict weather and crop yield to help them improve their perpetual performance. Agricultural robots have started to replace farmers and they are able to autonomously farm, irrigate and collect crops with the aid of Machine Learning. Farmers in many countries have started to use predictive analysis and precision farming techniques with the help of the aforementioned AI technology. It is important to understand that precision farming has started to increase in popularity, and has held the largest market size in 2019. The use of precision farming and predictive analysis has resulted in high crop yields and lower food costs in several developed countries (Karnawat et al., 2020). The proposed study will focus on using peer-analyzed literary resources to evidence the same and add further proof that supports AI-induced agriculture. While some emerging countries like India, China and Brazil have started to adopt AI agriculture systems, the use of AI technologies in agriculture has still not an integral part in several emerging countries. There are two primary challenges that are responsible for this drawback, namely the lack of ability to automate traditional agricultural processes, and the lack of awareness about AI agriculture. These factors prove to be the main internal factors that have hindered the penetration of AI agriculture in emerging and low economy countries (Karnawat et al., 2020). Comment by Mellissa Gyimah: Yes, but from what lens exactly?

In addition to challenges that threaten the AI agriculture framework, there are also several external factors that hinder the adoption of AI in the agricultural model of some developing countries. It is important to understand that each country has a unique climate and environment, and follow different agricultural frameworks to maximize agricultural production (Karnawat et al., 2020). Therefore, AI systems need to accommodate external factors, and also accommodate local cultures and languages. For example, the monsoons in countries like India and the dry and& hot climate in countries like Africa will prove challenging for the induction of AI agriculture frameworks, therefore these AI cannot be used in every conditions, there is the need to modify them for them to fit the climates and the conditions of the areas in which they will be functioning in. It is for this reason therefore that each emerging country might have the need for different AI applications for specific agricultural needs. Therefore, there is more work and research required to determine the best and most efficient solutions in each specific scenario (Karnawat et al., 2020). Comment by Mellissa Gyimah: Africa is a continent…not a country…..

As AI continues to grow at a rapid pace and become important in agricultural production, it is crucial that the agronomic applications become well supported, well understood, and supported in the AI agriculture framework. Countries with low economy need to implement superior AI agriculture systems that can be implemented as efficient and quick as possible with a focus on supporting local food production and local culture (El-Gayar & Ofori, 2020). The main goal of the theoretical framework is analyzing the theoretical and practical applications of several AI technology that is applicable for increased agricultural production. By using the methodology from the perspective of AI agriculture, the proposed study aims to identify several relevant features that will allow agronomic applications to be implemented using the most advanced technologies available in AI agricultural systems. This will be supported by the global AI agriculture data that is collected through the literary research of several peer-reviewed literary sources (El-Gayar & Ofori, 2020). Comment by Mellissa Gyimah: This person is Ghanaian—from my parents’ country!

Researcher’s Positionality Comment by Mellissa Gyimah: This should be centred

The topic that was used for this proposed study is influenced by my passion for increasing agriculture production in developing countries. The research is to be conducted primarily using document analysis as the main data collection methodology. The research is conducted with the support of Judson University and the research methodologies are based on qualitative research. The main participants of the research are agricultural AI technicians and agricultural farmers from several countries (El-Gayar & Ofori, 2020). The research will not be directly focused on understanding the opinions through interviews, and rather use document analysis and other indirect methods to quantify the use of AI technology in agriculture and determine the efficient technology that could help some of the emerging technology improve their agricultural production (El-Gayar & Ofori, 2020).

Comment by Mellissa Gyimah: centred

Purpose

The purpose of the study is to learn the opportunities for integrating AI technologies to improve the agricultural production of various emerging countries and countries of lower economy (Araújo et al., 2021). The proposed study uses literary research and document analysis to explore the various methods of AI technology used in global agriculture and understanding the challenges in emulating the same. The relationship between AI-based agricultural framework and the various internal and external factors will provide the desired result, which is understanding the appropriate AI technology necessary for the increase in agricultural production (Araújo et al., 2021). Comment by Mellissa Gyimah: may provide….don’t use absolutes otherwise people will think there is no need for the study

Research Question(s
) Comment by Mellissa Gyimah: centre

Global agricultural development is gradually changing and the integration of AI technology in agriculture has helped several countries improve their agricultural production. However, the popularity of agriculture has gradually declined in emerging countries and countries with lower economies (Araújo et al., 2021). The decrease in the production and popularity of agriculture in emerging countries is due to several important factors ranging from increased modernization to decrease in groundwater. The lack of a young agricultural workforce is also another factor that negatively affects agricultural production enhancement and development (Araújo et al., 2021).

Moreover, these countries also face a further decrease in agricultural production due to the gradual loss of agricultural land. Therefore, emerging countries need to revolutionize agricultural frameworks to increase agricultural production and improve their economic standards (Araújo et al., 2021). This can be done through the induction of AI technology in agricultural frameworks as this has been a proven method in several developed countries. This proposed study is focused on the integration of AI technology into agricultural processes in emerging countries. Therefore, it looks to answer some important research questions that would help develop a method of AI integration (Araújo et al., 2021):.

R1: How can AI technology be used to improve the popularity of aAgriculture in eEmerging Countries?

R2: How can AI technology be used to improve aAgricultural production in eEmerging Countries?

R3: What are the challenges and& training necessities involved in the implementation of such AI aAgriculture processes?

Significance

The importance of agricultural revolution has been the topic of several studies, especially in recent times where several countries are facing economic crises. There has also been significant research into the use of AI tools and technology in global agriculture and its positive effects on the same (Tzachor, 2021). However, there is much to be explored on the integration of AI technology into the agricultural processes of emerging countries. Since agriculture is gradually declining in popularity in several emerging countries, this is an important avenue for research. This will help emerging countries revolutionize their agricultural processes and future-proof their agricultural frameworks (Tzachor, 2021).

Using literary documents on AI integration in global agriculture, the reasons for agricultural production decline in emerging countries, and the opportunities and challenges present in integrating different types of AI technology, the proposed study will focus on understanding the best way to create AI-induced agricultural processes in emerging countries. The proposed study will use document analysis as the main data collection methodology and conduct a thematic analysis on the data collected from the research studies (Tzachor, 2021). This thematic analysis will be focused on the use of different types of AI technology and the external factors of several emerging countries like weather, local population, culture, etc. This will help us find the best technology that can be used to improve agricultural production based on an emerging country’s external factors (Tzachor, 2021).

Definition of Terms

i. AI-induced Agriculture – An agricultural framework that is based on the use of Artificial Intelligence. Comment by Mellissa Gyimah: maybe also just define agriculture in general, too?

ii. Machine Learning – Machine Learning is a type of Artificial Intelligence that is based on the idea that systems can learn from data, identify patterns and learn to make decisions with limited human intervention.

iii. Deep Learning – Deep Learning is a category of Machine Learning that uses the human brain as a model for processing data. Through Deep Learning, machines can process complex data without human intervention (Tzachor, 2021).

iv. Computer Vision – Computer Vision is a type of Artificial Intelligence that trains computers to understand and interpret the visual world using digital cameras, videos and other deep learning modules.

v. Precision Agriculture – Precision Agriculture is an agricultural management concept that uses technology to observe, measure and respond to various inter-field and intra-field variables to increase crop yields and agricultural profitability.

vi. Predictive Analysis – Predictive Analysis is a branch of advanced analytics that to analyzes current data using various methods like data mining, statistics, etc., to make future predictions (Tzachor, 2021).

Summary Comment by Mellissa Gyimah: centred

Agriculture has been declining in popularity in emerging countries. In a time when most of the developed countries are using AI to increase agricultural production, there is no clear indication of the same happening in various emerging and low economy countries. Thus, this proposed study was created to understand how agricultural processes in emerging countries can be improved through AI technology. Through literary review and document analysis, the proposed study aims at understanding the best AI technology that needs to be used to improve agricultural production in emerging countries. This is also the main research question that the proposed study aims to answer. The proposed study will also explore the various challenges that will hinder the integration of AI technology in the agricultural processes of emerging countries. Through the proposed study, the researcher aims at increasing the agricultural production and the economy of emerging and low-economy countries. This is the main goal of the thesis. Comment by Mellissa Gyimah: nice summary!


Chapter 3: Methodology

Introduction

The methodology section of the proposed study provides a comprehensive overview of the research methodology that will be to explore the integration of AI agriculture in emerging countries. The research methodology will be firmly based on literary review and document analysis that will focus on analyzing documents that discuss the different types of AI agriculture, the benefits/limitations of AI agriculture, and the challenges in incorporating AI agriculture in emerging countries (Weißhuhn et al., 2018). The goal of the research methodology will be to provide fact-based analyses and supporting qualitative research by using peer-reviewed literature and case studies to demonstrate the benefits and negative impacts of AI agriculture. By using historical literature in this way, the proposed study will aim to present AI agriculture as a credible and affordable alternative to conventional agriculture in emerging countries around the globe. This section will look at the research methodology used in the proposed study. The section will also explore the validity and reliability of the study along with any ethical considerations that need to be addressed (Weißhuhn et al., 2018). Comment by Mellissa Gyimah: For a proposal, your research design and methodology is not the lit review. Unless you were actually writing a metanalysis or a systematic review, which you’re not. So this is solely a document analysis.

Statement of the Problem

Agriculture is a critical field in many countries. However, the popularity of agricultural production is on the decline in several emerging countries. The decline in popularity can be attributed to rapid modernization and lack of education about agriculture. This limits the involvement of the younger generation in agriculture. In addition to the low quantity of active farmers, the lack of technological advancements in the field is also a major factor for the decline in agricultural production (Weißhuhn et al., 2018). With most developed countries focusing on incorporating AI systems in agriculture, the limitations of AI agriculture in emerging countries need to be understood and analyzed.

Comment by Mellissa Gyimah: centred

Research Question(s)

The proposed study will focus on addressing the following critical questions

R1: How can AI technology be used to improve the popularity of Agriculture in Emerging Countries?

R2: How can AI technology be used to improve Agricultural production in Emerging Countries?

R3: What are the challenges & training necessities involved in the implementation of such AI Agriculture processes?

Research Methodology Comment by Mellissa Gyimah: centred

The proposed study will primarily use qualitative research methodologies to study the potential limitations and benefits of AI agriculture in emerging countries. The primary research methodology is a systematic document analysis, i.e. thematic analysis that will be conducted on both historical and current literary sources pertaining to AI agriculture and the current roadblocks in developing countries (Terry et al., 2017). Thematic analysis is a qualitative research methodology that is centered on using identifying relevant themes in literary sources and grouping them for further analysis to identify factual evidences from literary sources. One of the strong points of the methodology is that it can be applied in many areas of research and is thus useful for the field of AI agriculture. Furthermore, it also complements the fact that AI agriculture is a field that is being discussed currently in several literary sources. The thematic analysis will be conducted on literary sources that focus on the field of AI agriculture. The goal of the thematic analysis is to quantify the primary research by providing unique perspectives on the field. This will help enhance the context and achieve a more comprehensive result (Terry et al., 2017). Comment by Mellissa Gyimah: do you have those specific sources already?

Research Design Comment by Mellissa Gyimah: centred

The design of the research methodologies is focused on sequential analysis of both the literary sources and the interviews through thematic analysis. The sequential research framework is based on the core research methodology of document analysis. The framework is focused on logical design that emphasizes efficient data collection. The literary sources for the proposed study will be selected from peer-reviewed research studies and case studies on the topic of AI agriculture (Terry et al., 2017). The thematic analysis will be conducted initially to identify relevant data about AI agriculture’s limitations and challenges. The sequential research design involves the synthesis of factual data from the selected literary sources about AI agriculture and its role in a changing world, using the current tools that AI agriculture provides us today. The design will be then be applied to the interviews with the focus on creating context within the work which assists farmers, business owners and other members in the field of AI agriculture and thereby deepen their understanding of the topic.

The goal of the qualitative research design is to give a broader context and an objective approach to a particular literature in order to determine its relevance in the current context of AI agriculture. The research design uses the thematic analysis of the interviews that were conducted to participants in the field of agriculture. The interview format will be digital interview and the participants will be selected by snowball sampling method. These interviews will help answer questions related to how AI agriculture can benefit emerging nations (Lane et al., 2018). This approach provides a unique view of the field from a social, cultural, environmental, technological, and philosophical perspective. Therefore, the research design is focused on providing a unique picture of the current AI agriculture field. The research framework will have a holistic approach and ensure that the thematic analysis of both the literary sources and the interviews will be integrated and studied in order to provide a comprehensive picture. The primary research will be based on the most up-to-date information in the field of AI agriculture and the qualitative interviews will be used to explore the topic from a …

Increasing AI Agriculture in Emerging Countries and Countries with Low Economy

Submitted by

Sateesh Rongali

A Proposed Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Education/Philosophy in Leadership

with a specialization in Computer Science

Judson University

Elgin, Illinois

08-22-2021


Chapter 2: Literature Review

This chapter will explore the field of AI Agriculture and provide insights on the need for further research in the field through an in-depth literature review. The focus of the literature review is to explore the existing literature and highlight the current trends in the development of AI usage for Agriculture and the possible future use in Agriculture. Particularly, the literature review will be a review of articles that focus on the field of AI agriculture. By discussing the potentials, challenges and limitations in the development of AI in Agriculture, the literature review hopes to provide a snapshot of the current state of AI usage in Agriculture. It is evident that agriculture in emerging countries have started to decline because of the diminishing popularity of the agriculture field in the developing nations and its consumers. The literature review will use peer-reviewed literary sources to understand the reason behind the same and the importance of AI agriculture in these developing nations (Beriya, 2020). Comment by Author: its Comment by Author: this chapter Comment by Author: review of relevant literature Comment by Author: it is hoped that this chapter will provide a snapshot…

AI agriculture has become a major topic of interest for scientific research in the last few years. This can be mainly attributed to the fact that the need for AI in the agricultural sector is rapidly increasing because of the growing population and diminishing land of crop plants available for agriculture. In developed countries, AI agriculture provides support to farmers in the farming sector by automating farming practices, which can be applied to the field of agriculture in countries that are suffering from the food crisis and facing environmental problems (Beriya, 2020). Although, the implementation of AI in the agriculture sector is still evolving, the potential of the use of AI in agriculture is promising. By integrating AI into the existing technological system, farmers can use various technologies that include remote-sensing, smart irrigation, and automatic fertilization to provide a high-quality crop. The use of remote-sensing technology to provide an accurate crop yield prediction using information from satellites is a notable example (Beriya, 2020). Although remote sensing technology uses a plethora of information from space to identify a crop, such a system is not yet accessible to developing nations due to the high- cost of satellite-based technology. Comment by Author: cite some of those studies to justify this claim Comment by Author: availability of land for agriculture. Comment by Author: Specify some of those practices Comment by Author: This can equally be applied to …. Comment by Author: Using it Comment by Author: High quality crops

In developed countries, the use of robots and smart technologies in Agriculture has helped boost Agricultural popularity and production. The objective of this research is to explore the potential of artificial intelligence (AI) in Agriculture and the application of AI in Agriculture, in particular, to improve Agricultural popularity and production in emerging countries like India and Africa (Garske et al., 2021). The literature review will be focused on identifying the state of research in AI Agriculture and highlight on potential applications of AI in Agriculture, including robotics in Agriculture. The scope of the literature review includes any research which used robotics and AI in Agricultural development, as the focus of the literature review will be the use of robots and AI in Agricultural development. By exploring existing literature in the field, the literature review will be able to identify the gaps in the knowledge and areas of further research in the field (Garske et al., 2021). Comment by Author: Elaborate on how this has been achieved and cite sources Comment by Author: Africa is not a country

The study will use peer-reviewed literature as the main source for the literature review. Peer-reviewed research papers can be divided into two groups: journal articles and research reports. Journal articles are scientific papers that are published in academic journals and have undergone peer review process. The peer review process ensures the scientific validity of the research paper, such as the research question posed by the researcher. Research reports are scientific reports written by the researcher (Garske et al., 2021). These reports are not peer-reviewed before publication, which allows the researcher to freely write about their research without much scrutiny. This chapter will focus on reviewing journal articles from the peer-reviewed research literature. Journal articles from the peer-reviewed literature will be the main source of the literature review. By focusing on peer-reviewed journal articles, the study will ensure that the literature review is valid and that there are no biases. Comment by Author: You’ve said this earlier. delete Comment by Author: Not necessary. Delete Comment by Author: I really donlt see the relevance of these… Comment by Author: Delete

This chapter will use the literature review for both the knowledge mapping and literature review, which will provide a comprehensive review of the literature in the field of agricultural AI applications. Both types of scientific papers can provide valuable information about how research on a particular topic has been conducted (Singh, 2020). The review process of journal articles and research reports on AI use in Agriculture can be divided into two steps. The first step will involve the selection of a specific topic of interest. Next, the review process will be continued by selecting appropriate bibliographic sources which may include peer-reviewed articles, articles, book chapters, or reports. Lastly, all information found from the selected bibliographic sources will be documented (Singh, 2020). Comment by Author: Do you also notiuce the way the same thing is being repeated? Avoid doing this Comment by Author: I’m struggling to see the relevance of this entire section above.
State very briefly what you want to focus on and promptly go on to the actual review. Trying to educate your reader about the difrences etc may be insulting. They already know.
Please redo this section

Theoretical Foundation

The literary review will also help create a concrete theoretical foundation for the proposed study. Some of the important concepts that needs to be studied in the literature review are the motivation for using AI in agriculture, the barriers for to implementing AI agriculture systems, and the significant benefits of using the same. An understanding of these concepts is necessary to understand how the AI can be used to improve and automate the existing technology in the agriculture sector (Farooq et al., 2020). Therefore, a review of existing literature that covers these topics will help narrow down the list of potential references and improve the strength of the study. While literature reviews are often conducted by analyzing the current literature on a certain topic, AI use in agriculture is a very new area of research and hence has limited exploration. Hence, to conduct a literature review, more information is required, which include the proposed research question, the topic, and the bibliographic sources of the research (Farooq et al., 2020). Comment by Author: delete Comment by Author: delete

It is also important to understand the assumptions associated with the field of AI agriculture and validate the same through the literature review. One of the main assumptions is that the AI will significantly increase the production rate in an agricultural sector and help in increasing its efficiency (Farooq et al., 2020). Hence, a study on how AI is being used to solve problems and automate some processes within the agriculture sector is also required. In the literature review, the use of the AI within the agriculture sector can also be explored by researching the current progress and barriers that prevents the sector from progressing. Another assumption is that the AI will improve the way farmers are operating their farms. The need for an understanding of this issue is that this might lead to new ideas in the field of the operation and management of the farms (Farooq et al., 2020). Comment by Author: Ok, her’s what you need to do here, for all the issues you are highlighting, find the literature that talk about them, read them and state what they say as regards these points.

The literature review will also help verify whether the proposed AI system will help automate the traditional processes of the farming or not. Therefore, the assumption associated with the technology is crucial to be explored. The literature review hopes to identify and define the existing areas of research, gaps, issues, and challenges that are present in the AI agriculture field. This will form the foundation of the research design and help guide the methodology for the research process (Sonaiya, 2019). However, a careful evaluation of the scope of the problem is essential. This will be done through a careful analysis and review of the literary sources that study the existing fields of AI agriculture. This will help create a comprehensive theoretical foundation for the investigation and identification of the problems that are relevant to the selected field of study. To that end, the literature review will investigate several ideas or suggestions regarding the current scope of AI Agriculture, which will later form the basis for the research hypotheses that surround the field (Sonaiya, 2019). Comment by Author: Like I pointed out earlier, so far, I haven’t seen much of the actual review of the literature that you have consulted. Thius chapter should not be prescriptive. It should be an actual review of the relevant literature that talk about the issues you are raising,.

In addition, it will provide a summary of key concepts, definitions, and theories that will be needed for the research study. Following this, the methods of evaluation of AI in the agriculture field will be identified and categorized. Additionally, the current scope of the industry and its development as well as its progress, as well as its progress, will also be reviewed. This will be done through a comprehensive search of the literature using keywords such as: machine learning, deep learning, autonomous farming, and AI (Sonaiya, 2019). The review will also investigate the technological, financial, legal, and operational constraints faced by the farmers as well as challenges that the industry might face. This will help create the design and evaluation framework that will guide the rest of the investigation process.

Review of Literature

This literature review section will review various peer-reviewed literary sources that are relevant to the scope of the research. It will be a chronological review of the literature, beginning with the works that have the greatest effect on the state of the industry or the technology that has been developed. This review will attempt to provide a general overview of the field of study, the related theories, and concepts, and to identify the various technological developments and methods of investigation (Shokat & Großkinsky, 2019). The current state of the AI and its application in the agriculture industry as well as its progress will be reviewed through a bibliography search. The research hypothesis will be derived from the proposed objectives and the conclusions from the literature review. The literature review will be used to define the research question to be answered by the study, along with providing context, definitions, and terminology. It will also be used to define and evaluate the research design and the data analysis method used in the proposed research (Shokat & Großkinsky, 2019). Comment by Author: Where is the actusal review of the relevant literature?

Agriculture in Emerging Countries

Agriculture in emerging countries has decreased in importance due to a variety of factors including the increase in urbanization, the decreasing demand for agricultural products, and the shifting global commodity markets. There is a general belief in the research community that in order to meet the demands of the emerging markets, agricultural research must change and be conducted in an entirely different and new manner (Singh, 2020). Many factors contribute to this lack of change, including the belief that agricultural research is simply too difficult to conduct, the lack of agricultural funding in the emerging countries, the difficulty to recruit and retain researchers, and the lack of a research infrastructure. Although agricultural research conducted in developing countries is often aimed at improving the agricultural production systems in developing countries, the findings from this research often provide solutions that will benefit the agriculture in developing countries. There is a general belief that developing countries such as China and India, with their relatively lower per capita income, lower literacy rates, and smaller agricultural land base, have less fewer resources to pursue agricultural research (Singh, 2020). Comment by Author: cite Comment by Author: cite

In reality, there is some evidence that indicates that the developing countries, particularly China, have a larger agricultural research sector in comparison to the developed countries. Nevertheless, the magnitude of agricultural research activities and research institutions in emerging countries are relatively less than in the developed countries like the USA and the UK. However, there are some literary sources that assure that both developed countries and developing countries have some type of agricultural research that is improving the agriculture of those countries. This is in stark contrast to the major consensus in the field. Therefore, there is a need for further study of agricultural research in developing countries (Singh, 2020). Specifically, there is a need for a greater emphasis on conducting research on how to improve the agriculture in the developing countries, and an examination of the relative success of those agriculture-related research conducted in the developing countries. This will help identify the major problems hindering the agricultural research in the developing countries and provide a clear understanding of why there is such a disparity in agricultural research in developing countries compared with the developed countries (Singh, 2020).

The majority of agricultural research is conducted in the United States, Australia, the United Kingdom, and other developed countries. While there is some research that is being conducted in these countries, the majority of agricultural research is based on research and development initiatives supported by the United States and developed countries. While this research is not necessarily detrimental to the countries in which it is conducted, the research is often focused on improving the agricultural production systems in developed countries (Farooq et al., 2020). This research and development effort could provide new insights, but may not be sufficient to provide sustainable solutions for agricultural production in emerging countries. In addition, the lack of agricultural research and development in developing countries could be attributable to factors such as inadequate funding, a lack of trained agricultural personnel, the lack of sufficient access to data and technology, and the lack of opportunities to partner with other institutions (Farooq et al., 2020). Comment by Author: cite them Comment by Author: cite them discuss them

On the whole, agriculture in emerging countries seem to be on a decline, and this trend is being observed throughout the world. Although agriculture is still the single largest contributor of gross national product, the majority of agriculture-related research is based on research being conducted in the developed countries. The developed countries spend billions of dollars on research, and that cannot be replicated in developing countries that must rely on locally based research that may not provide more sustainable solutions (Farooq et al., 2020). This warrants continued global effort to identify and develop agricultural production systems in developing countries that are sustainable and economically viable. Thus, a study on the need for AI agriculture and its future potential is a relevant and much much-needed topic. It will help emerging countries worldwide to develop more sustainable agricultural production systems that ensure food security for people worldwide (Farooq et al., 2020). Comment by Author: cite sources for these claims

Reasons for Low Popularity

There are also several literary sources that describe the factors which have contributed to the decline in the number of agricultural productivity and popularity. Some of the reasons that have been quoted in relevant literary sources are the increase in modernization and the lack of agricultural education among the younger generation (Alreshidi, 2019). Other factors include poor funding, a lack of research infrastructure, and the lack of agricultural production. In fact, several developing countries have a lower output per unit of agricultural land. While there is a clear consensus among various authors about these factors being the reason for the lack of agricultural production and popularity, the extent of the impact of these factors is not explored in-depth (Alreshidi, 2019). Comment by Author: what are they, be specific, cite them Comment by Author: which ones? Be specicfic

For example, there is a consensus that the decrease in agricultural production and popularity can be attributed to the lack of agricultural education, especially in the case of people from the younger dynamic. While this is true in a way, there isn’t enough literature and research to confirm this with any degree of certainty. In fact, the number of people who are illiterate is increasing while the literacy rate is not commensurate with population growth (Alreshidi, 2019). The education systems in many countries have been affected by a number of challenges. For example, the increase in literacy and the rise of the literacy rate are not in sync with agricultural production. The increasing literacy rate has also not led to a corresponding increase in the number of agricultural producers and entrepreneurs. There is a clear need for further research to determine the extent of the contribution of various agricultural education factors towards declining agricultural production (Alreshidi, 2019). Comment by Author: cite source Comment by Author: is not Comment by Author: cite

Another factor which is attributed for the dwindling population in the agricultural sector is the introduction of a number of modern agricultural technologies. For example, the rise in mechanization and the improvement in agricultural research and development are attributed to the rise in production efficiency (Bannerjee et al., 2018). However, it would be helpful to investigate whether there are is any empirical or research-based evidence to support these claims. While it is true that the agricultural population in emerging countries is likely to decline due to the introduction of modernization, there is also a growing body of literature to indicate that the agricultural population is growing at an unprecedented pace in less developed countries. However, even if the agriculture population in the developing countries is declining, there is a need to understand why people are choosing to stay in the farming sector (Bannerjee et al., 2018). While the agriculture population is declining, there is also a corresponding decrease in the labor force in the agriculture sector. This could be due to the lack of available jobs in the agricultural sector and/or the changing nature of employment which makes the work force less attractive. More research needs to be done on these issues to determine the key reasons for declining agricultural production.

The question is whether, and to what extent, these factors have also contributed to the lack of agricultural production or research in emerging countries (Bannerjee et al., 2018). This question is based on the assumptions that the developing countries have the potential to increase their agricultural production if the factors that prevent such production, and the agricultural research in emerging countries, could be corrected. The question is also based on the assumption that the agriculture research in emerging countries could be conducted in the same manner as in developed countries. Therefore, it is important to examine the current factors that contribute to the current status of agricultural research in emerging countries (Bannerjee et al., 2018).

In many cases, the emerging countries research that is being conducted is mostly based on developed countries. The reasons behind this are not only the lack of technical, infrastructural, and monetary capacity, but also due to the difference in the environment, culture, and mentality of the countries. In order to understand the reasons that underlie the current status of agricultural research in developing countries, it is necessary to first explore and understand the current status of agricultural research and the potential of agricultural research in developing countries (Bannerjee et al., 2018). Comment by Author: repetition. delete

Importance AI Agriculture

Literary sources that explore AI agriculture offer a strong argument to support the research of AI integration in the agriculture field. AI agriculture has gained a lot of interest due to its wide range of potential applications in various fields in the agriculture industry (Eli-Chukwu, 2019). AI agriculture is also gaining popularity as the technology is becoming more advanced, cheaper, and easier to use. In fact, many authors and researchers in the field propose that the impact of the artificial intelligence on the food and agriculture industry is expected to be tremendous. They state that the technology has the potential to change the way farming is done and how food is harvested, and that it can be an advantage to farmers if they can harvest early, as the weather could be favorable for a certain crop and then bad for another crop. This showcases the importance of AI research and development in the agriculture industry and the importance of using AI as a tool to solve problems (Eli-Chukwu, 2019). Comment by Author: cite

There is also a strong consensus among researchers that AI agriculture is highly beneficial for both farmers and societies. One of the biggest uses of AI technology is to improve the efficiency of farming. It is possible that farmers could harvest a crop, store it, and harvest another crop using an AI technology system. These systems could potentially allow farmers to harvest in the middle of the night when the weather is not conducive to doing so. The importance of improving efficiency cannot be emphasized enough (Eli-Chukwu, 2019). A study by the Department of Agriculture (USDA) estimated that in an average farm, a farmer can save around 9 cents when using an improved AI agricultural technology. As the efficiency of farming improves, the cost of food production also decreases. This can provide a sustainable food source and reduce the amount of money required by the farmer to obtain a food source. However, AI technology is still in its infancy, and there are more research, development, and testing needs to be done before more people can use AI technology to improve their food and agriculture (Garske et al., 2021). Comment by Author: which ones? cite Comment by Author: delete

Another use of AI technology in agriculture is ‘predictive farming’ that helps determine harvest times and crop use. This can be especially important if there is an environmental concern because a farmer might not want to harvest when the environment is becoming unfriendly. This could provide an opportunity for more efficient and more cost effective farming. This is especially important for the use of crops and the usage of water because the use of the most efficient crops could require less water (Garske et al., 2021). The efficiency of a farm could also be defined by its net income. This would mean that the higher the efficiency of the farm is, the more the net income will be. Since the efficiency of the farm is directly linked to the net income, the farm is more efficient if it is able to attain a greater net income. With predictive farming, a farmer may learn what the soil is able to tolerate and when to plant. It can also determine what the crops are best for its environment at what time of the year (Garske et al., 2021). Therefore, AI technology helps farmers use these tests to optimize the quantity and quality of crops. Comment by Author: delete

An overwhelming amount of literature recommend integrating AI tools and systems like machine learning, IoT and data visualization in order to monitor and control farms as a whole. AI can work with the information gathered in the field to determine what crops need to be grown based on weather, soil, and other environmental factors (Gurumurthy & Bharthur, 2019). Because of the huge amount of data that can be gathered from this information, it is hard to process it by human. AI can process the data, determine what crops need to be grown, and then send instructions to grow the best crops. AI can also use IoT devices and sensors to determine how much of a fertilizer, or other chemicals need to be used to improve the overall health of the soil. This can help the farmer plan for the best possible results (Gurumurthy & Bharthur, 2019). Comment by Author: cite them

However, there are also concerns in the mind of researchers and experts in the field about the implementation of said AI systems in the field of agriculture. This is because of the fact that a lot of the AI technology and systems that are used today in the agricultural sector have not been extensively tested in emerging However, there are also concerns in the mind of researchers and experts in the field about the implementation of said AI systems in the field of agriculture (Gurumurthy & Bharthur, 2019). This is because of the fact that a lot of the AI technology and systems that are used today in the agricultural sector have not been extensively tested in emerging economies, like India. While these emerging economies have a lot to gain by utilizing AI systems in their agricultural sector. It is important to do further research and testing of the said AI systems in order to make sure that they are effective and that there are no side effects to the ecosystem. Therefore, several experts and researchers state that the implementation of AI technology in the agricultural sector of emerging countries needs to be done slowly and carefully (Gurumurthy & Bharthur, 2019).

Exploration of Benefits

There is an overwhelming consensus that there are several benefits in implementing AI agriculture systems in the field of agriculture. The economic benefits of using AI systems in the agricultural sector in emerging economies is pretty significant. Many countries in the world, including the USA, are struggling with many issues like unemployment, poverty, increasing food prices, and increasing costs of agriculture as a result of climate change. While other countries like India have a much higher population and need more food (Bannerjee et al., 2018). Therefore, in these emerging economies, the implementation of AI systems in the agricultural sector will significantly help in the improvement of the economy, not to mention the improvement of the overall welfare of the people and agriculture as a whole. The implementation of AI technology in agriculture is going to be the most effective way of overcoming the food shortage and increasing food security issues that are plaguing the world (Bannerjee et al., 2018). This is because a lot of the world’s population is living in rural areas. As a result, the majority of the population do not have access to food that is sufficient and healthy. Therefore, using AI systems in agriculture to help farmers in rural areas produce food in a sustainable way is going to be the best way to solve the growing issues of food shortages and food security in the world (Bannerjee et al., 2018). Comment by Author: You need to site them and also justify what you mean by ‘overwhelming.’

Another significant benefit of using AI systems in the agricultural sector is going to be the increased income of the farmers. Because there is a growing need for food in emerging economies, and a lot of the people in these countries are living in rural areas (Beriya, 2020). Therefore, it is pretty obvious that there is a huge need for increasing the productivity of the farmers in these countries. The implementation of AI technology in agriculture, and especially in the agricultural software, can significantly help these farmers to increase their income as well. Because a lot of the farmers that are in rural areas don’t know about any of the agricultural software applications that are available on the market (Beriya, 2020). Therefore, they are going to find it really difficult to benefit from these applications. This can be mitigated with the aid of AI education for …

literature review should have more points from different articles..

initial survey questions like appendix from attached file “Adenekan_Final Proposal (IRB2)” should be added to file “DissertationFinalCopy”

we need to add how we will identify domentratic partipications

Running head: IMMIGRANTS AND LANGUAGE USE 1

Immigrants and Language Use: Experiences and Reflections

Olabisi Adenekan

Judson University

Running head: IMMIGRANTS AND LANGUAGE USE 2

Abstract

Man has a naturally complex and multi-dimensional propensity to migrate. This tendency has

been further fueled by trends in global politics in the past few centuries. Given this trend,

language use presents peculiar challenges–while the native speaker grapples with seeking to

communicate with individuals who speak English differently, the immigrant is faced with

seeking to articulate English language in such a way as to interact effectively in a predominantly

native English-speaking environment. This multidimensionality presents an avenue that has the

potential to be replete with challenges and conflicts. Although fully capable of communicating

in their own languages, immigrants do typically experience language problems when they move

to relatively homogeneous and heavily monolingual environments. This is often because the

linguistic repertoires required for communicative survival in such new milieux become more

complicated. Utilizing the focus group research method, this research seeks to examine the

narrated experiences and reflections of immigrant participants in order to purposely shed light on

their experiences on issues of language use in America. In so doing, this research purposes to

identify the various common threads that emanate from collected data, and to evaluate these

common themes that will be distilled from each story-telling experience. The intention is to look

closely at the implications these themes may have on common immigrant experiences in the

American society, and how these can be used by policy makers, immigrants, educators, and other

stakeholders for the general betterment of society.

Running head: IMMIGRANTS AND LANGUAGE USE 3

CHAPTER 1:

BACKGROUND OF THE STUDY

From time immemorial, language has been at the center of human interconnectedness at

different levels. It has also been the cause of various relational conflicts and complications as

exemplified in the creationist belief system of the story of the Tower of Babel (Genesis 11).

Even in the third millennium, it constitutes a fundamental point of divergence between peoples,

policies and ideologies. In its use and misuse, language still creates such combustible effects

similar in magnitude and proportion to those experienced at the Babel. Not only are the effects

felt on familial, tribal and national levels, they also range between diverse people groups in

neighborhoods, school buildings and the work place.

Moreover, living in the third millennium has made the realities of globalization and

language use one we can no longer ignore. The interdependence of globalization and language is

manifested abundantly in the socio-economic, political, religious, technological, and educational

arenas, and in other market places of ideas (Kramsch, 2014). It has also pushed to the fore the

need for effective and sensitive plurilingual communication (Rost, 2014) with a broad-based

group of language participants.

The tendency to migrate is inherent in most animal kingdoms. It also manifests as a

human phenomenon. People obviously migrate from place to place for different cogent reasons.

These include, but are not limited to economic, social, or political contingencies. Countries like

the Great Britain, Canada, and the United States of America promote certain immigration

policies, and as a result, have experienced waves of immigration over the past few centuries.

These immigration policies, which were for the most part formulated to achieve a brain drain of

competent personnel to generate skilled workforce in these countries, follow a rigorous process

Running head: IMMIGRANTS AND LANGUAGE USE 4

in their selective criteria. As a result, hordes of professional immigrants move into these

countries with their families to occupy positions in specialized fields, or at other times, to study

more to bring their qualifications to par with the laid down standards in the host countries

(Baquedano-Lopez & Figueroa, 2012). These conditions have the propensity to create

language-related pressure felt by every stakeholder on both sides of the divide. Immigrant

individuals and families are thus thrust into such situations of needing to constantly negotiate

various tensions between their peculiar language circumstances, and the idealized

presuppositions and political rhetoric that brought them to these societies in the first place

(Creese & Kambere, 2003).

Bourdieu (1977) affirms that language is an instrument of power. This gives speakers of

given languages the propensity to wield power. Traditionally, native speakers of English

language enjoy a hegemonic advantage over speakers of other languages because of the

economic and political might enjoyed by countries where English is spoken as a native language

(Cervantes-Rodriguez & Lutz, 2003; Greene, 2011; Tomic, 2013).

In his campaign for World Englishes, Kachru (1996) uses the imagery of Janus in Roman

mythology (see Appendix 1) to describe the process of language change through contact. The

one face is that of “Englishization,” which he describes as changes that have occurred in other

languages as a result of their contact with English language. The other face is the multi-layered

changes undergone by the varieties of English as they interact with other cultures and

expressions. He describes this as the process of “Nativization and Acculturalization.” Based in

part on this process, English has become a Language of Wider Communication (LWC) (Schmitz,

2014), with non-native speakers outnumbering native speakers. Conversely also, the push to

preserve and maintain the sanctity of the English language amidst the influx of other language

Running head: IMMIGRANTS AND LANGUAGE USE 5

groups is more urgent than before among the proponents of that ideology.

Kachru (1996) also posits the argument for the linguistic power construct by highlighting

the three categories into which linguistic power can be divided. He calls the first category the

crude linguistic power. According to his argument, this is the way in which one language is

forcibly imposed on other less dominant languages, cultures and peoples. Kachru refers to the

second category as the subtle or indirect psychological power. He describes this category as

those situations when metaphysical attributes are essentially given to a language to make people

accept its usage almost without question. The third category, according to him, is the pragmatic

power of language. He describes this as the ability of the said language to gain control over a

multiplicity of domains spanning political, socio-economic, religious, and other functional

human preoccupations. The fact that native speakers of English language operate at the level of

linguistic pragmatism gives them the power and ability to define and authenticate the uses for,

and the other users of the language.

In addition to these, theoretical linguists are widely diversified in their hypotheses and

theories concerning the parameters surrounding language acquisition in general, and second

language acquisition in particular. Each linguistic school of thought holds true to the belief that

certain parameters hold more sway over the others in child and/or second language acquisition.

Be that as it may, the immigrant is unavoidably thrown into the argument while still trying to

navigate through the language barriers in which s/he is, as a matter of course, immersed.

Statement of the Problem

These conditions therefore present a fertile ground for peculiar kinds of challenges for all

interlocutors in any language event. On the one hand, native speakers have to grapple with

seeking to communicate with individuals who speak English differently; and on the other hand,

Running head: IMMIGRANTS AND LANGUAGE USE 6

immigrant speakers are faced with seeking to articulate English language in such a way as to

interact effectively in a predominantly native English-speaking environment. In the current

global and national political reality, this multidimensionality presents an avenue that has the

potential to be replete with challenges and conflicts (Alim, Rickford, & Ball, 2016; Ana, 2004;

Gabay, 2014; González, 2005; Greene, 2011; Lee, 2015). In giving voice to the stories of

immigrant participants in this research, it is hoped that more light may be shed on these issues to

effect a dialogue that paves the way for mutual understanding.

Theoretical Framework

Sociocultural Theory

Vygotsky’s (1986) Sociocultural Theory (SCT) is based on the premise that learning

occurs with the instrumentality of the whole mental process, which is inclusive of the society

(Macaro, 2013); and that a person’s development is as a result of both biological endowments

and sociocultural variables. The individual interacts socially with others within that individual

learner’s Zone of Proximal Development (ZPD). This is the zone where the learner is able to

perform at a higher level, and where knowledge can be co-constructed with another interlocutor,

and the society (Atkinson, 2011; DeBot, Lowie, & Verspoor, 2005). According to this theory,

language thus becomes a “mediating tool between the physical world” (Macaro, 2013. p. 176)

and the speaker, as developed from the individual’s zone of proximal development. As a tool, it

is passed down inter-generationally, and serves as man’s greatest symbolic asset for inter-

connectedness–his greatest asset for negotiating higher level cognitive process (Grabois, 2004).

According to SCT, interactions between individuals are the paramount foci, and language

learning is the “symbolic artifact to facilitate such (social) activities” (Atkinson, 2011, p. 25).

Vygotsky sets importance on conversations, and believes that language learning only happens

Running head: IMMIGRANTS AND LANGUAGE USE 7

through social interaction (Handsfield, 2016; Malone, 2012; McLaughlin, 1987).

SCT emphasizes the social nature of language use, its effectiveness, and social

appropriateness. In this theory, Vygotsky posits that the capability of a child’s first development

of higher social order functioning, although primarily based on the child’s interactions with

his/her significant care-givers in the immediate community, eventually branches out to the values

the child learner picks up from the society at large. Vygotsky refers to this concept as

interpsychological functions. He also argues that the learner further goes on to develop a

personal (intrapsychological) level of social functioning, based on the already acquired

interpsychological functions. This helps the individual to self-regulate, develop agency, and

function with a high degree of autonomy (Lee, 2015).

According to SCT, the linguistic choices one makes arise largely out of various social

inter-relationships between different interlocutors and community variables (Cuming-Potvin,

Renshaw, & Van-Kraayenoord, 2003; Jang & Jimenez, 2011; Lightbown & Spada, 2006). SCT

further posits that the symbiotic relationship between the society and the individual causes the

individual to constantly undergo a necessary state of fluidity and change because “the way we

think and act – and the ways we come to do so – are shaped by this dialectical relationship

between the individual and social processes” (Lee, 2015, p. 11).

By looking through the lenses of sociocultural perspectives, this research seeks to

examine the crucial role between the interconnectedness of linguistic ability and cognitive

development within the social contexts in which the immigrant non-native speaker interrelates.

Not only that, the research intends to examine the social ramifications of this interconnectedness

in the immigrant non-native speaker’s ability to effectively thrive and function in a given society.

Running head: IMMIGRANTS AND LANGUAGE USE 8

Language Socialization Theory

With regards to language acquisition and use, the theory of Language Socialization (LS)

spans across various disciplines such as Sociology, Psychology and Anthropology. It is

concerned with understanding how a people’s language and communicative practices evolve and

develop as they subsist in varieties of social communities by close examination of language use

on both “macro- and micro – contexts” (Duff & Talmy, 2011). LS researchers identify two

major domains of socialization in which individuals acquire cultural worldview. These are

“socialization through the use of language, and socialization to use language.” This is not only

limited to childhood language acquisition, but it “is open to investigating language socialization

throughout the human lifespan across a range of social experiences and contexts” (Schieffelin &

Ochs, 1986a, p. 163).

The perspective is that learning the language of a given community effectively is a

precursor to becoming a competent member of that community. Conversely also, learning the

language of the community is hinged upon participating in that community in all of its social

ramifications. Schieffelin & Ochs (1986a) explained this by stating that, “the process of

acquiring language is deeply affected by the process of becoming a competent member of

society. The process of becoming a competent member of society is realized to a large extent

through language, by acquiring knowledge of its functions, social distribution, and

interpretations in and across socially defined situations” (p. 168). This then goes to show that the

mutual inclusivity of both domains is articulated in the speaker’s participation in social contexts

(Duranti, Ochs & Schieffelin, 2012; Ortaçtepe, 2013).

The theory further argues that language socialization, as an interactive process, starts at

birth or at the point of social contact for the novice adult learner. It is a process that is as bi-

Running head: IMMIGRANTS AND LANGUAGE USE 9

directional as it is multi-directional (Atkinson, 2011). Continual socialization thus morphs the

passive learner into an active user who contributes to meanings and outcomes of interactions

within his/her given social community (Schieffelin & Ochs, 1986a; Watson-Greco & Nielsen,

2003).

Acquiring the cultural worldview associated with the non- native language presents a

difficulty for the immigrant since s/he is prevented from effective entry into appropriate

socialization through the use of the language. This, in turn, hinders effective socialization and

the ability to participate within the social context that the language demands. Language

socialization is therefore germane to this research, as it not only investigates issues of language

acquisition, but it takes these preoccupations a step further by seeking to understand the role

language plays in the process of an individual immigrant’s linguistic and social integration into

community.

Critical Theory

As a form of social theory, Critical Theory (CT) is concerned with the overall issues of

power and justice, with intent to effect change in the identified social reality. It focuses on how

structures like the cultural dynamics, the economy, ideologies, race, education, religion, gender,

and so on inter-relate in any given social system. It also attempts to analyze how competing

groups in such social systems determine who loses and who gains. As Tollefson (2006) puts it,

“in language policy research, the term “critical” has three interrelated meanings: (1) it refers to

work that is critical of traditional, mainstream approaches to language-policy research; (2) it

includes research that is aimed at social change; and (3) it refers to research that is influenced by

critical theory” (p. 45). The two broad categories that proponents of critical literacy usually fall

into are: (1) those who observe the oppressive aspect of power and how it results in suffering and

Running head: IMMIGRANTS AND LANGUAGE USE 10

inequity; and (2) those who observe the possibility of having marginalized people empowered to

re-think their position and strive for change (Freire, 1970). All of these can be articulated under

the large umbrella of power, struggle, colonization, hegemony and ideology, and resistance

(Cervetti, Pardales, & Damico, 2001).

Critical theory sees power as inherent in all human relationships. It is the drive to control

events for one’s own good, thereby positing a dynamic relationship between social structure and

individual agency. What ensues as a result of this, therefore, is the struggle between the

dominant group who seeks to control everything to serve its self-interests; and the oppressed

group. This struggle eventually morphs into colonization, as experienced in situations in which

various ethnic and cultural groups are subjugated by the policies made by the dominant system.

This gives rise to various challenges which, in turn, result in loss of cultural identity, and

economic imposition (Philipson, 1992). Hegemony and ideology are often described as those

policies and practices that serve to retain power in the hands of a select few by creating

unattainable standards for the marginalized. For immigrants, these policies could limit access to

needed economic survival, thereby fueling the feeling of subjugation (Bourdieu, 1977; Cook,

1999; Miller, 2012; Ortaçtepe, 2013).

According to critical theory, minority groups may create a resistance by defying their

imposed limitations and devising alternative survival mechanisms. This kind of resistance is

borne from the philosophy that “…no one group is exclusively entitled to the privilege of

representation, but that each has a right to tell its (own) story” (Knoblauch & Brannon, 1993, p.

6). Language and literacy are, in Freire’s (1970) theory, essential mechanisms for social

reconstruction. Every person is entitled to fairness in all ramifications. When this is guaranteed,

humanity can achieve a peaceful co-existence in spite of all our differences. The oppressed

Running head: IMMIGRANTS AND LANGUAGE USE 11

group therefore has the responsibility to develop its critical consciousness so that it can recognize

and take ownership of its own identities through meaning-making processes. Oppressed groups

must be able to “wage the struggle for their liberation, they must perceive the reality of

oppression not as a closed world from which there is no exit, but as a limiting situation which

they can transform” (Freire, 1970, p. 49). Although there are several versions of critical literacy,

generally speaking, they share the belief that literacy is a “social and political practice rather than

a set of neutral, psychological skills” (Siegel & Fernandez, 2000, p. 18).

Purpose of the Study

This study purposes to examine the narratives of various immigrants in order to shed light

on their experiences on issues of language use in America. In so doing, this research seeks to

identify the various common thematic threads that emanate from collected data, and to evaluate

those common themes that may be distilled from each story-telling experience. The intention is

to look closely at the implications these may have on common immigrant experiences in the

American society, and how these can be used by policy makers, immigrants, educators, and other

stakeholders for the general betterment of society.

Research Questions

To understand the implications of language use and immigration in America, this research will

focus on the following questions:

1. What are the impacts of language use on immigrants in America?

2. How does the immigrants’ cognitive ability mitigate these impacts?

3. What effects do these impacts have on the immigrant’s reality and perceived quality of

life in America?

Running head: IMMIGRANTS AND LANGUAGE USE 12

Significance of the Study

Since language is such a powerful and essential commodity which can be manipulated

and exploited by various users for purposes of inclusion and/or exclusion, it is important to give

voice to those who feel silenced. When such individuals are allowed to tell their personal stories,

it will serve as precursor to honest dialogues that will advance mutual understanding between all

stakeholders. This study serves the purpose of bridging that gap by elevating the stories of

immigrant participants in this research.

Delimitations and Limitations

This study intends to recruit five cohorts of participants for focus group sessions which

will be conducted in the summer and fall of 2017. In order to ensure utmost validity, participants

will be immigrants from different nationalities, varied age groups (eighteen years old and above),

mixed gender groups, different cultural backgrounds, (with possibly) different religious

affiliations, and different native languages (but who, for authentic communicative purposes in

this research, are able to easily sustain conversations in English). Selected participants will be of

different periods of arrival in the United States, varied professional levels and credentials, and

diverse family dynamics. Method of recruitment will be by a sample of convenience.

Questionnaires will be served to immigrants in such catchment communities as universities and

community colleges, ESL classes, and through contacts within the northwest Chicagoland area.

Assumptions

In a political terrain that is perceived as hostile to immigrants, and more so to

undocumented immigrants, participants could be less willing to truthfully and frankly share their

experiences for fear of facing the wrath of the law if their identities and stories were ever made

public. The goal is to ensure that the privacy of each participant and cohort is maintained. This

Running head: IMMIGRANTS AND LANGUAGE USE 13

will be unequivocally communicated through the Institution Review Board (IRB)-approved

documentation which will be provided to research participants to sign. Participants’ privacy will

also be maintained as the research team seeks to establish a community that is open and trusting

of each other enough to uphold the sanctity of each other’s experience and narrative without

compromising the integrity of the research objectives. Adequate guarantee will also be made to

ensure that participation is fully voluntary–without any coercion. It will also be communicated

that possible withdrawal from the research will be without repercussion.

Organization of the Study

This chapter has established the general frame of reference surrounding this research.

The background and purpose for the study were highlighted; the theoretical framework; the

purpose of the study; the research questions; the need for the study; the delimitations, limitations,

and assumptions were equally discussed. Chapter 2 will be a review of the research and studies

that form a framework for this research. The focus of Chapter 3 will be on methods and

procedures that will be used to investigate the research questions, while Chapter 4 will focus on

the findings. Chapter 5 will be a discussion of the findings and the implications these may have

for future research.

Running head: IMMIGRANTS AND LANGUAGE USE 14

CHAPTER II:

REVIEW OF LITERATURE

This study seeks to examine the role and impact that language plays in the integration of

adult immigrant speakers of English language into mainstream America. In doing this, focus

will be placed on language use, the immigrant as language user, and the society. Selected

theories will also be integrated to effectively incorporate the preoccupations of this research.

Conceptual Framework

The conceptual framework has been described as “an end result of bringing

together a number of related concepts to explain or predict a given event, or give a broader

understanding of the phenomenon of interest – or simply, of a research problem” (Imenda, 2014,

p. 189). Due to the essential inter-play between the immigrant’s English language experience

and various human inter-relationships, the overarching lens through which these issues will be

examined is, of necessity, sociocultural in nature. In addition, sociocultural theory, language

socialization theory, and critical theory overlap largely because of their foci on the impact of

language use on the language learner and/or user in society. This research will examine these

issues through the other lenses of language socialization and critical theories.

Sociocultural theory is premised on the interdependence between the individual and the

society in language learning (Handsfield, 2016; Malone, 2012; McLaughlin, 1987). Language

socialization theory also identifies effective language use as precursor to successful integration

into any given society. Schieffelin & Ochs (1986a) point out that both the successful

integration, and the individual’s effectiveness in language use are dependent on each other. With

its core value based on issues of power and justice, critical theory emphasizes ways in which

language can be utilized by one group to wield power and control over the other. These theories,

Running head: IMMIGRANTS AND LANGUAGE USE 15

by sharing an interconnection that reflects the thrust of this research, are germane to this study.

This research thus seeks to tease out the experiences of immigrant participants in order to

understand their individualized and collective processes of language use in the American society.

All of these will be viewed through the lenses of the highlighted theories to understand the

possible challenges and tensions in their individual and joint experiences.

Language and Immigration

Man has a naturally complex and multi-dimensional propensity to migrate; and global

politics has further fueled this necessity. By definition, both immigration and migration usually

refer to the mobility of people from one geographical location to another. A migrant’s mobility

is traditionally limited to locations within the same country, while the immigrant is one who

moves from one country to another.

The notion of who the “immigrant” is, has become a much debated concept amongst

migration experts and international human rights law practitioners. The two major categories

which are often commonly used to differentiate these individuals are “economic migrants” and

“forced migrants or refugees.” In distinguishing between the refugee and the economic migrant,

the office of the United Nations High Commissioner for Refugees (UNHCR), a UN Refugee

Agency, states that “…refugees do not choose to leave their countries, but are forced to do so out

of fear of persecution or as a result of armed conflict. By contrast, economic migrants do enjoy

the protection of their home but voluntarily decide to leave, for instance, to improve their

economic situation or because of family links” (UNHCR). The International Organization for

Migration (IOM), the UN Migration Agency, distinguishes between the two categories by

defining the economic migrant as one who relocates in order to “improve his/her standard of

living in a country other than his or her country of origin.” It is further added that, “this term

Running head: IMMIGRANTS AND LANGUAGE USE 16

differs from that of a “refugee” fleeing persecution or a de facto refugee fleeing from widespread

violence or massive violation of human rights” (IOM).

Both agencies are in agreement in demarcating between the two categories of individuals

with the qualifiers “voluntarily” and “forced.” However, human rights activists have raised

concerns over the minimizing nature of these choices of words and definitions, which has

resulted in “reductionist and erroneous” (Cernadas, 2016) policies. The argument is that there is

a greater multidimensionality to the choice to migrate, and that is trifled with by merely implying

that a “person crosses countries, deserts and oceans, or suffers different kinds of abuse only to be

able to change his or her television, obtain a wage increase or some other economic benefit”

(Cernadas, 2016). So poignantly has Warsan Shire put the perspective of the immigrant in the

following abridged version of the poem Home

no one leaves home unless

home is the mouth of a shark.

you only run for the border

when you see the whole city

running as well.

…you only leave home

when home won’t let you stay.

no one would leave home unless home

chased you, fire under feet,

hot blood in your belly.

…you have to understand,

no one puts their children in a boat

unless the water is safer than the land.

who would choose to spend days

and nights in the stomach of a truck

1

Increasing AI Agriculture in Emerging Countries and Countries with Low Economy

Submitted by

Sateesh Rongali

A Proposed Study Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Education/Philosophy in Leadership

with a specialization in Computer Science

Judson University

Elgin, Illinois

01-04-2022


Abstract

This research study focuses on exploring the field of AI agriculture from an emerging countries’ standpoint. The goal of the research study is understanding the reason for the decline in agricultural productivity and popularity in emerging countries and exploring how AI agriculture can help the countries improve agricultural processes. The research study will also explore the major limitations that have restricted the adoption of AI agriculture in these emerging countries. After providing a brief introduction into the current state of agriculture in emerging countries, the research study defines the core research questions that would drive the study. To gain further insights into agriculture in emerging countries and the limitations of AI adoption, the research study provides an in-depth literature review that explores literary sources focused on the relevant topics. The main research methodology of the proposed research study will be document analysis that will identify the relevant themes in both historical and current peer-reviewed literary sources exploring the topics of AI agriculture, agriculture in emerging countries, and agricultural limitations. In addition, the research study will also conduct qualitative interviews to participants selected from the AI agriculture industry. To ensure that the research study is focused on emerging countries, the proposed study will ensure that the document selection is strictly based on topic and thematic relevance. The participants for the interviews will be selected through snowball sampling. In addition, the proposed study also provides brief insights into the expected limitations and ethical considerations surrounding the research. Through the research methodology, the proposed study aims to arrive at valid and reliable results that helps identify AI agricultural methods that can improve agricultural production and popularity in emerging countries.


Table of Contents
Chapter 1: Introduction 5
Background 5
Problem Statement and Significance 6
Theoretical Framework 7
Researcher’s Positionality 10
Purpose 11
Research Question(s) 11
Significance 12
Definition of Terms 13
Summary 14
Chapter 2: Literature Review 15
Theoretical Foundation 17
Review of Literature 19
Agriculture in Emerging Countries 19
Reasons for Low Popularity 22
Importance AI Agriculture 24
Exploration of Benefits 26
Challenges in Implementation 29
Overview 32
Gaps in Literature 34
Conclusion 36
Chapter 3: Methodology 38
Introduction 38
Statement of the Problem 38
Research Question(s) 39
Research Methodology 39
Research Design 40
Study Population & Sample Selection 41
Data Collection Methods 42
Data Analysis & Procedures 44
Validity & Reliability 45
Ethical Considerations 46
Limitations 46
Summary 47
References 49



Chapter 1: Introduction


Background

Agriculture has been a field that is gradually declining in popularity in several countries around the world. The rate of growth of the global demand for agricultural products has also started to decline in the recent past. This is particularly significant in countries that are referred to as developing and having low economy that were dependent on agriculture (Sivarethinamohan et al., 2020). The number of agricultural lands in developing countries like India have started to decrease. This decrease can be attributed to several factors including an increase in modernization which has changed the way of life of people from doing agriculture as a way of earning their living to other modernized means and the decrease of groundwater levels in several regions which has affected the water needed for irrigating the agricultural farms. Although this decrease in popularity might feel insignificant, it might result in disastrous effects in the long run (Sivarethinamohan et al., 2020).

A decline in agricultural production can significantly impact countries with low economy because it further reduces their economy. An increase in agricultural production helps lower food prices and increases the country’s ability to do commerce based on the agriculture products. Therefore, it is important for these countries to improve their economic condition. In addition to increased modernization and decreasing water levels, most countries also face a decrease in agricultural labor (Sivarethinamohan et al., 2020). This is because most of the youths of the countries do not view agriculture as a viable option for sustenance or growth. Agriculture is also not viewed in a positive light in most of these societies, which also adds to the factor. They are more attracted to other fields that provide them more money and increase their status in the society. Since this mentality is inbred into most of the societies, the reformation of such ideas will take significantly more time (Sivarethinamohan et al., 2020).

Due to these factors, most of the agriculture in emerging and low economy countries are carried out by an older population. This poses several problems for the economy. The lack of a younger agricultural labor population makes agriculture a non-sustainable option for economic growth. As mentioned earlier, the lack of agriculture could cause economic disruptions. There is also the fact that the older population is unable to pass on their knowledge to other generations because of the lack of interest (Sivarethinamohan et al., 2020; Tzachor, 2021). Thus, farmers in these countries are less able to take advantage of other areas that produce food or products. If these issues are not solved, further problems may arise such as social unrest or political instability within the populations. This poses a threat to emerging economies that are dependent on agricultural production (Sivarethinamohan et al., 2020).


Problem Statement and Significance

The main problem behind the decrease in agriculture in emerging and low economy countries is the decrease in the significance and popularity of agriculture. Because of modernization, the younger population in most of the countries do not understand the value of agriculture in their economy. This could be partially attributed to the growth of various industries and their marketing ability (Tzachor, 2021). This has attracted many youths in the countries to ignore farming as a viable option for their economic or social growth. As more and more people gyrate towards modern fields and industries, they have started occupying more land in the countries. This has resulted in the transformation of valuable agricultural lands into factories, companies and residential areas in most of the countries (Tzachor, 2021).

The lack of agricultural knowledge is also a significant factor in developing countries. Knowledge of farming is extremely important for developing countries to manage an agricultural process. Since most emerging and low economy countries need to grow their economy rapidly, they are forced to disregard agriculture as one of the main sources of economy and focus on modern industries and companies that provide opportunities for rapid growth (Tzachor, 2021). To improve agricultural growth, these countries need revolutionary methods that can increase production at lower costs. But this is a challenge as older people contribute to most of the active population of farmers. This has impacted technological and technical advancements in the agricultural field, which is a necessity to mitigate the existing threat to agriculture in most of these countries (Tzachor, 2021). This paper will therefore seek to induldge is a extensive discussion looking at the use of AI in agricultural sector and consider how the same can be used in looking at how countries can develop their production activities


Theoretical Framework

The term “AI” refers to information processing and intelligence. The general idea is that this technology is used to learn and master, and to build applications with that knowledge. In most cases, the information processing and intelligent nature of such a system is what is taught in the different literatures that will be referenced and discussed in this proposed study. The main goal of this proposed study is to explore agriculture in emerging and low economy countries and find ways to induce the use of Artificial Intelligence (AI) (Jha et al., 2019). The theoretical framework for the proposed study will focus on compiling instances of AI usage in global agriculture and explore the possibilities and challenges that are involved in the same, some of the theories include metric embedding, cryptography, computational geometry etc. The proposed study will research the concepts through the exploration of various literary resources that are based on AI Agriculture to develop a comprehensive and comprehensive understanding of the field. Furthermore, the research will look at the practical and social challenges that arise from the use of such technologies, with the aim of encouraging the use of AI technologies in agriculture (Jha et al., 2019).

This study will focus on the development and adoption of AI as a means of agriculture, which is crucial for future economic development and to make large scale agricultural production more efficient in emerging countries and countries with lower economies. The use of Artificial Intelligence system in the field of agriculture is rapidly increasing (Jha et al., 2019). There have been several breakthroughs and advances in AI and some countries have been able to leverage the technology through the development of AI programs and systems. In many of the countries, the economic output as a result of the advances made in agricultural technology has been greatly increasing. In many of the nations where the production has increased, the development of AI has been a critical help in substantially increasing agricultural productivity and production (Jha et al., 2019). This is evidenced in several literary papers.

The growth of agricultural technology as a field provides great opportunities for emerging and low economy countries that are struggling to improve their agricultural production. Thus, the theoretical framework will focus on exploring the use of technology, particularly AI technology in the global agricultural field. While exploring the opportunities for AI-induced agriculture in emerging countries, it is important to understand the different types of AI technology that are being used in agriculture (Jha et al., 2019). With the aid of literary papers, we can learn that there are several different types of AI systems including machine learning algorithms, deep learning, and computer vision for increasing agricultural productivity and economic growth. A variety of AI systems are being tested and used in today’s agro-industry and, as such, the concept of using AI-enhanced agriculture is a field that has great potential and the use of the field as a solution to poverty alleviation and other environmental problems will be explored further in the future (Jha et al., 2019). Example of AI systems being used in agro-industry include predictive analytics, crop and soil monitoring, agricultural robots, etc. Predictive analytics helps farmers predict weather and crop yield to help them improve their perpetual performance. Agricultural robots have started to replace farmers and they are able to autonomously farm, irrigate and collect crops with the aid of Machine Learning. Farmers in many countries have started to use predictive analysis and precision farming techniques with the help of the aforementioned AI technology. It is important to understand that precision farming has started to increase in popularity, and has held the largest market size in 2019. The use of precision farming and predictive analysis has resulted in high crop yields and lower food costs in several developed countries (Karnawat et al., 2020). The proposed study will focus on using peer-analyzed literary resources to evidence the same and add further proof that supports AI-induced agriculture. While some emerging countries like India, China and Brazil have started to adopt AI agriculture systems, the use of AI technologies in agriculture has still not an integral part in several emerging countries. There are two primary challenges that are responsible for this drawback, namely the lack of ability to automate traditional agricultural processes, and the lack of awareness about AI agriculture. These factors prove to be the main internal factors that have hindered the penetration of AI agriculture in emerging and low economy countries (Karnawat et al., 2020).

In addition to challenges that threaten the AI agriculture framework, there are also several external factors that hinder the adoption of AI in the agricultural model of some developing countries. It is important to understand that each country has a unique climate and environment, and follow different agricultural frameworks to maximize agricultural production (Karnawat et al., 2020). Therefore, AI systems need to accommodate external factors, and also accommodate local cultures and languages. For example, the monsoons in countries like India and the dry & hot climate in countries like Africa will prove challenging for the induction of AI agriculture frameworks, therefore these AI cannot be used in every conditions, there is the need to modify them for them to fit the climates and the conditions of the areas in which they will be functioning in. It is for this reason therefore that each emerging country might have the need for different AI applications for specific agricultural needs. Therefore, there is more work and research required to determine the best and most efficient solutions in each specific scenario (Karnawat et al., 2020).

As AI continues to grow at a rapid pace and become important in agricultural production, it is crucial that the agronomic applications become well supported, well understood, and supported in the AI agriculture framework. Countries with low economy need to implement superior AI agriculture systems that can be implemented as efficient and quick as possible with a focus on supporting local food production and local culture (El-Gayar & Ofori, 2020). The main goal of the theoretical framework is analyzing the theoretical and practical applications of several AI technology that is applicable for increased agricultural production. By using the methodology from the perspective of AI agriculture, the proposed study aims to identify several relevant features that will allow agronomic applications to be implemented using the most advanced technologies available in AI agricultural systems. This will be supported by the global AI agriculture data that is collected through the literary research of several peer-reviewed literary sources (El-Gayar & Ofori, 2020).


Researcher’s Positionality

The topic that was used for this proposed study is influenced by my passion for increasing agriculture production in developing countries. The research is to be conducted primarily using document analysis as the main data collection methodology. The research is conducted with the support of Judson University and the research methodologies are based on qualitative research. The main participants of the research are agricultural AI technicians and agricultural farmers from several countries (El-Gayar & Ofori, 2020). The research will not be directly focused on understanding the opinions through interviews, and rather use document analysis and other indirect methods to quantify the use of AI technology in agriculture and determine the efficient technology that could help some of the emerging technology improve their agricultural production (El-Gayar & Ofori, 2020).


Purpose

The purpose of the study is to learn the opportunities for integrating AI technologies to improve the agricultural production of various emerging countries and countries of lower economy (Araújo et al., 2021). The proposed study uses literary research and document analysis to explore the various methods of AI technology used in global agriculture and understanding the challenges in emulating the same. The relationship between AI-based agricultural framework and the various internal and external factors will provide the desired result, which is understanding the appropriate AI technology necessary for the increase in agricultural production (Araújo et al., 2021).


Research Question(s)

Global agricultural development is gradually changing and the integration of AI technology in agriculture has helped several countries improve their agricultural production. However, the popularity of agriculture has gradually declined in emerging countries and countries with lower economies (Araújo et al., 2021). The decrease in the production and popularity of agriculture in emerging countries is due to several important factors ranging from increased modernization to decrease in groundwater. The lack of a young agricultural workforce is also another factor that negatively affects agricultural production enhancement and development (Araújo et al., 2021).

Moreover, these countries also face a further decrease in agricultural production due to the gradual loss of agricultural land. Therefore, emerging countries need to revolutionize agricultural frameworks to increase agricultural production and improve their economic standards (Araújo et al., 2021). This can be done through the induction of AI technology in agricultural frameworks as this has been a proven method in several developed countries. This proposed study is focused on the integration of AI technology into agricultural processes in emerging countries. Therefore, it looks to answer some important research questions that would help develop a method of AI integration (Araújo et al., 2021).

R1: How can AI technology be used to improve the popularity of Agriculture in Emerging Countries?

R2: How can AI technology be used to improve Agricultural production in Emerging Countries?

R3: What are the challenges & training necessities involved in the implementation of such AI Agriculture processes?


Significance

The importance of agricultural revolution has been the topic of several studies, especially in recent times where several countries are facing economic crises. There has also been significant research into the use of AI tools and technology in global agriculture and its positive effects on the same (Tzachor, 2021). However, there is much to be explored on the integration of AI technology into the agricultural processes of emerging countries. Since agriculture is gradually declining in popularity in several emerging countries, this is an important avenue for research. This will help emerging countries revolutionize their agricultural processes and future-proof their agricultural frameworks (Tzachor, 2021).

Using literary documents on AI integration in global agriculture, the reasons for agricultural production decline in emerging countries, and the opportunities and challenges present in integrating different types of AI technology, the proposed study will focus on understanding the best way to create AI-induced agricultural processes in emerging countries. The proposed study will use document analysis as the main data collection methodology and conduct a thematic analysis on the data collected from the research studies (Tzachor, 2021). This thematic analysis will be focused on the use of different types of AI technology and the external factors of several emerging countries like weather, local population, culture, etc. This will help us find the best technology that can be used to improve agricultural production based on an emerging country’s external factors (Tzachor, 2021).


Definition of Terms

i. AI-induced Agriculture – An agricultural framework that is based on the use of Artificial Intelligence.

ii. Machine Learning – Machine Learning is a type of Artificial Intelligence that is based on the idea that systems can learn from data, identify patterns and learn to make decisions with limited human intervention.

iii. Deep Learning – Deep Learning is a category of Machine Learning that uses the human brain as a model for processing data. Through Deep Learning, machines can process complex data without human intervention (Tzachor, 2021).

iv. Computer Vision – Computer Vision is a type of Artificial Intelligence that trains computers to understand and interpret the visual world using digital cameras, videos and other deep learning modules.

v. Precision Agriculture – Precision Agriculture is an agricultural management concept that uses technology to observe, measure and respond to various inter-field and intra-field variables to increase crop yields and agricultural profitability.

vi. Predictive Analysis – Predictive Analysis is a branch of advanced analytics that to analyzes current data using various methods like data mining, statistics, etc., to make future predictions (Tzachor, 2021).


Summary

Agriculture has been declining in popularity in emerging countries. In a time when most of the developed countries are using AI to increase agricultural production, there is no clear indication of the same happening in various emerging and low economy countries. Thus, this proposed study was created to understand how agricultural processes in emerging countries can be improved through AI technology. Through literary review and document analysis, the proposed study aims at understanding the best AI technology that needs to be used to improve agricultural production in emerging countries. This is also the main research question that the proposed study aims to answer. The proposed study will also explore the various challenges that will hinder the integration of AI technology in the agricultural processes of emerging countries. Through the proposed study, the researcher aims at increasing the agricultural production and the economy of emerging and low-economy countries. This is the main goal of the thesis.



Chapter 2: Literature Review

This chapter will explore the field of AI Agriculture and provide insights on the need for further research in the field through an in-depth literature review. The focus of the literature review is to explore the existing literature and highlight the current trends in the development of AI usage for Agriculture and the possible future use in Agriculture. Particularly, the literature review will be a review of articles that focus on the field of AI agriculture. By discussing the potentials, challenges and limitations in the development of AI in Agriculture, the literature review hopes to provide a snapshot of the current state of AI usage in Agriculture. It is evident that agriculture in emerging countries have started to decline because of the diminishing popularity of the agriculture field in the developing nations and its consumers. The literature review will use peer-reviewed literary sources to understand the reason behind the same and the importance of AI agriculture in these developing nations (Beriya, 2020).

AI agriculture has become a major topic of interest for scientific research in the last few years. This can be mainly attributed to the fact that the need for AI in the agricultural sector is rapidly increasing because of the growing population and diminishing land of crop plants available for agriculture. In developed countries, AI agriculture provides support to farmers in the farming sector by automating farming practices, which can be applied to the field of agriculture in countries that are suffering from the food crisis and facing environmental problems (Beriya, 2020). Although, the implementation of AI in the agriculture sector is still evolving, the potential of the use of AI in agriculture is promising. By integrating AI into the existing technological system, farmers can use various technologies that include remote-sensing, smart irrigation, and automatic fertilization to provide a high-quality crop. The use of remote-sensing technology to provide an accurate crop yield prediction using information from satellites is a notable example (Beriya, 2020). Although remote sensing technology uses a plethora of information from space to identify a crop, such a system is not yet accessible to developing nations due to the high-cost of satellite-based technology.

In developed countries, the use of robots and smart technologies in Agriculture has helped boost Agricultural popularity and production. The objective of this research is to explore the potential of artificial intelligence (AI) in Agriculture and the application of AI in Agriculture, in particular, to improve Agricultural popularity and production in emerging countries like India and Africa (Garske et al., 2021). The literature review will be focused on identifying the state of research in AI Agriculture and highlight on potential applications of AI in Agriculture, including robotics in Agriculture. The scope of the literature review includes any research which used robotics and AI in Agricultural development, as the focus of the literature review will be the use of robots and AI in Agricultural development. By exploring existing literature in the field, the literature review will be able to identify the gaps in the knowledge and areas of further research in the field (Garske et al., 2021).

The study will use peer-reviewed literature as the main source for the literature review. Peer-reviewed research papers can be divided into two groups: journal articles and research reports. Journal articles are scientific papers that are published in academic journals and have undergone peer review process. The peer review process ensures the scientific validity of the research paper, such as the research question posed by the researcher. Research reports are scientific reports written by the researcher (Garske et al., 2021). These reports are not peer-reviewed before publication, which allows the researcher to freely write about their research without much scrutiny. This chapter will focus on reviewing journal articles from the peer-reviewed research literature. Journal articles from the peer-reviewed literature will be the main source of the literature review. By focusing on peer-reviewed journal articles, the study will ensure that the literature review is valid and that there are no biases.

This chapter will use the literature review for both the knowledge mapping and literature review, which will provide a comprehensive review of the literature in the field of agricultural AI applications. Both types of scientific papers can provide valuable information about how research on a particular topic has been conducted (Singh, 2020). The review process of journal articles and research reports on AI use in Agriculture can be divided into two steps. The first step will involve the selection of a specific topic of interest. Next, the review process will be continued by selecting appropriate bibliographic sources which may include peer-reviewed articles, articles, book chapters, or reports. Lastly, all information found from the selected bibliographic sources will be documented (Singh, 2020).


Theoretical Foundation

The literary review will also help create a concrete theoretical foundation for the proposed study. Some of the important concepts that needs to be studied in the literature review are the motivation for using AI in agriculture, the barriers for implementing AI agriculture systems, and the significant benefits of using the same. An understanding of these concepts is necessary to understand how the AI can be used to improve and automate the existing technology in the agriculture sector (Farooq et al., 2020). Therefore, a review of existing literature that covers these topics will help narrow down the list of potential references and improve the strength of the study. While literature reviews are often conducted by analyzing the current literature on a certain topic, AI use in agriculture is a very new area of research and hence has limited exploration. Hence, to conduct a literature review, more information is required, which include the proposed research question, the topic, and the bibliographic sources of the research (Farooq et al., 2020).

It is also important to understand the assumptions associated with the field of AI agriculture and validate the same through the literature review. One of the main assumptions is that the AI will significantly increase the production rate in an agricultural sector and help in increasing its efficiency (Farooq et al., 2020). Hence, a study on how AI is being used to solve problems and automate some processes within the agriculture sector is also required. In the literature review, the use of the AI within the agriculture sector can also be explored by researching the current progress and barriers that prevents the sector from progressing. Another assumption is that the AI will improve the way farmers are operating their farms. The need for an understanding of this issue is that this might lead to new ideas in the field of the operation and management of the farms (Farooq et al., 2020).

The literature review will also help verify whether the proposed AI system will help automate the traditional processes of the farming or not. Therefore, the assumption associated with the technology is crucial to be explored. The literature review hopes to identify and define the existing areas of research, gaps, issues, and challenges …

1

Increasing AI Agriculture in Emerging Countries and Countries with Low Economy

Submitted by

Sateesh Rongali

A Proposed Study Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Education/Philosophy in Leadership

with a specialization in Computer Science

Judson University

Elgin, Illinois

01-04-2022


Abstract

This research study focuses on exploring the field of AI agriculture from an emerging country’s ies’ standpoint. The goal of the research study is understanding the reason for the decline in agricultural productivity and popularity in emerging countries and exploring how AI agriculture can help them improve agricultural processes. The research study will also explore the major limitations that have impeded the adoption of AI agriculture in these emerging countries. After providing a brief introduction into the current state of agriculture in emerging countries, the research study will list s the core research questions that would drive the study. Aan in-depth literature review will that explore thes literary sources focused on the relevant topics. The main research methodology of the proposed research study will be document analysis that will identify the relevant themes in both historical and current peer-reviewed literary sources exploring the topics of AI agriculture, agriculture in emerging countries, and agricultural limitations./unclear sentence; long and cluttered; cut it down to at least half/ The In addition, the research study will also conduct qualitative interviews with to participants selected from the AI agriculture industry. All study will ensure that the document selection will be is strictly based on topic and thematic relevance, with due attention to ethical considerations surrounding the research. The participants for the interviews will be selected through snowball sampling. In addition, the proposed study also provides brief insights into the expected limitations and ethical considerations surrounding the research. Through the research methodology, the proposed study aims to arrive at valid and reliable results that helps identify AI agricultural methods that can improve agricultural production and popularity in emerging countries.


Table of Contents
Chapter 1: Introduction 5
Background 5
Problem Statement and Significance 6
Theoretical Framework 7
Researcher’s Positionality 10
Purpose 11
Research Question(s) 11
Significance 12
Definition of Terms 13
Summary 14
Chapter 2: Literature Review 15
Theoretical Foundation 17
Review of Literature 19
Agriculture in Emerging Countries 19
Reasons for Low Popularity 22
Importance AI Agriculture 24
Exploration of Benefits 26
Challenges in Implementation 29
Overview 32
Gaps in Literature 34
Conclusion 36
Chapter 3: Methodology 38
Introduction 38
Statement of the Problem 38
Research Question(s) 39
Research Methodology 39
Research Design 40
Study Population & Sample Selection 41
Data Collection Methods 42
Data Analysis & Procedures 44
Validity & Reliability 45
Ethical Considerations 46
Limitations 46
Summary 47
References 49



Chapter 1: Introduction


Background

Agriculture has been a field that is gradually declining in popularity in many several countries around the world. The rate of growth of the global demand for agricultural products has been in decline also started to decline in the recent yearspast. This is particularly significant in countries that are referred to as developing and/or having low economiesy that had been were dependent on agriculture (Sivarethinamohan et al., 2020). AThe number of agricultural land areass in developing countries like India have begun to shrinkstarted to decrease. Due to This decrease can be attributed to several factors including an increase in modernization. Lifestyle changes in such nations have reduced groundwater levels which in turn have put agricultural irrigation at risk (Mapulanga & Naito, 2019). Although this decrease in popularity might feel insignificant, it might result in disastrous effects in the long run (Sivarethinamohan et al., 2020).

A decline in agricultural production can significantly impact countries with low economy because it further weakens their economy. An increase in agricultural production helps lower food prices and increases the country’s ability to do commerce based on the agriculture products. Therefore, it is important for these countries to improve their economic condition. In addition to increased modernization and decreasing water levels, most countries also face a decrease in agricultural labor (Sivarethinamohan et al., 2020)./this sentence would fit better in the ¶ above where you mention groundwater levels/ Mmost of the youths of these countries do not view agriculture as a viable option for sustenance or growth (Green, 2014). They are more attracted to other fields that provide them more money and increase their status in the society. Since this mentality is inbred into most of the societies, the reformation of such ideas will take significantly more time (Sivarethinamohan et al., 2020).

Aagriculture in emerging and low economy countries is are carried out by thean older population. The generational gap in agricultural labor population makes the industry all the more unsustainable. There is also the fact that the older population is unable to pass on their knowledge to the next other generations because of the lack of interest (Sivarethinamohan et al., 2020; Tzachor, 2021). In the long run, such problems may cause arise such as social unrest or political instability among the people.


Problem Statement and Significance

The main problem behind the decrease in agriculture in emerging and low economy countries is the decrease in the significance and popularity of agriculture (Adeleke, 2018). Because of modernization, the younger population in most of the countries does not appreciate the value of agriculture in their economy. This could be partially attributed to the growth of various industries and their marketing ability (Tzachor, 2021). already stated/ As more and more people revolve and change towards modern fields and industries, they have started occupying more land in the countries./not sure what this means; better if rewritten/ This has resulted in the transformation of valuable agricultural lands into factories, companies and residential areas in most of the countries (Tzachor, 2021). /this sentence seems to say what you intended to say in the previous one/

The lack of agricultural knowledge is also a significant factor in developing countries. Knowledge of farming is extremely important for developing countries to manage an agricultural process. Since most emerging and low economy countries need to grow their economy rapidly, they are forced to disregard the priority that agriculture should have in an economy. Instead, they turn to other modern alternatives industries and companies that provide opportunities for rapid growth (Tzachor, 2021). To improve agricultural growth, these countries need revolutionary methods that can increase production at lower costs. But this is a challenge as only older people now contribute to most of the active population of farmers. This has slowed down impacted technological and technical advancements in the agricultural field., (Tzachor, 2021). This paper/project? Dissertation?/ will therefore seek to perform an extensive discussion looking at the use of AI in the agricultural sector and consider how it can help needy nations develop their agriculture.


Theoretical Framework

The term “AI” refers to information processing and intelligence. The general idea is that this technology is used to learn and master, and to build applications with that knowledge./rewrite this sentence. The “general idea” of what? Who is learning and mastering and building?/ In most cases, the information processing and intelligent nature of such a system is what is taught/is it “taught” or just reported?/ in the different literatures that will be referenced and discussed in this proposed study. The main goal of this proposed study/avoid using the same words or phrases back to backk/ is to explore agriculture in emerging and low economies y countries and find ways to induce the use of Artificial Intelligence (AI) (Jha et al., 2019). The theoretical framework for the proposed study/rpt/ will focus on compiling instances of AI usage in global agriculture and explore the possibilities and challenges that are involved. The proposed study will research the concepts through the exploration of various literary resources that are based on AI Agriculture to develop a comprehensive understanding of the field. Furthermore, the research will look at the need this word?/ social challenges that arise from the use of such technologies, with the aim of encouraging the use of AI technologies in agriculture (Jha et al., 2019).

This study will focus on the development and adoption of AI as a means of agriculture, which is crucial for future economic development and to make large scale agricultural production more efficient in emerging countries and countries with lower economies. The use of Artificial Intelligence system in the field of agriculture is rapidly increasing (Jha et al., 2019). There have been several advances in AI and some countries have been able to leverage the technology through the development of AI programs and systems (Jain, 2020). According to Jain (2020), AI gets integrated to develop crop and soil health monitoring whereby an AI application called Plantix got used to detect nutrient deficiencies/needs rephrasing/. In many of the countries, the economic output as a result of the advances in agricultural technology has been greatly increasing. Tthe development of AI has been a critical help in substantially increasing agricultural productivity and production (Jha et al., 2019).

Tthe theoretical framework of this research will focus on the use of technology, particularly AI technology in the global agricultural field which is currently working towards promoting sustainability. While exploring the opportunities for AI-induced agriculture in emerging countries, it is important to understand the different types of AI technology that are being used in agriculture/this point has either been stated or is by now understood/ (Jha et al., 2019). With the aid of literary papers, we can learn that there are several different types of AI systems including machine learning algorithms, deep learning, and computer vision for increasing agricultural productivity and economic growth. AI-enhanced agriculture has great potential in alleviating to poverty and other environmental problems. (Jha et al., 2019). Example of AI systems being used in agro-industry include predictive analytics, crop and soil monitoring, agricultural robots, etc. Predictive analytics helps farmers predict weather and crop yield to help them improve their perpetual performance. Agricultural robots have started to replace farmers and they are able to autonomously farm, irrigate and collect crops with the aid of Machine Learning. Farmers in many countries have started to use predictive analysis and precision farming techniques with the help of the aforementioned AI technology. It is important to understand that precision farming has started to increase in popularity, and has held the largest market size in 2019. The use of precision farming and predictive analysis has resulted in high crop yields and lower food costs in several developed countries (Karnawat et al., 2020). The proposed study will focus on using peer-analyzed literary resources to evidence the same and add further proof that supports AI-induced agriculture. While some emerging countries like India, China and Brazil have started to adopt AI agriculture systems, the use of AI technologies in agriculture has still not an integral part/any word you wanted to use missing here?/ in several emerging countries. There are two primary challenges that are responsible for this drawback, namely, the inability to automate traditional agricultural processes, and the lack of awareness about AI agriculture. These factors prove to be the main internal factors that have hindered the penetration of AI agriculture in emerging and low economy countries/this sentence merely repeats what you said right before/ (Karnawat et al., 2020).

Sseveral external factors that hinder the adoption of AI in the agricultural model of some developing countries. It is important to understand that each country has a unique climate and environment and follows different agricultural frameworks to maximize agricultural production (Karnawat et al., 2020). Therefore, AI systems need to accommodate external factors, and also accommodate local cultures and languages. For example, the monsoons in countries like India and the dry & hot climate in countries in the African continent will be challenging for the induction of AI agriculture frameworks., Therefore, there is more work and research required to determine the best and most efficient solutions in each specific scenario (Karnawat et al., 2020)./This ¶ needs more specific matter. You have many repetitions throughout the draft/

Countries with low economy need to implement superior AI agriculture systems that can be implemented as efficient and quick as possible with a focus on supporting local food production and local culture (El-Gayar & Ofori, 2020). The main goal of the theoretical framework is analyzing the theoretical and practical applications of several AI technology that is applicable for increased agricultural production. By using the methodology from the perspective of AI agriculture, the proposed study aims to identify several relevant features that will allow agronomic applications to be implemented using the most advanced technologies available in AI agricultural systems. This will be supported by the global AI agriculture data that is collected through the literary research of several peer-reviewed literary sources (El-Gayar & Ofori, 2020). /See if you can condense this entire ¶ in two sentences. Without that you are repeating what was said already/


Researcher’s Positionality

The topic that was used for this proposed study is influenced by my passion for increasing agriculture production in developing countries. The research is to be conducted primarily using document analysis as the main data collection methodology. The research is conducted with the support of Judson University through qualitative research. The main participants of the research are agricultural AI technicians and agricultural farmers from several/can you be specific? How many?/ countries (El-Gayar & Ofori, 2020). The research will not be directly focused on understanding the opinions through interviews, and rather use document analysis and other indirect methods to quantify the use of AI technology in agriculture and determine the efficient technology that could help some of the emerging technology improve their agricultural production/rewrite this sentence in half its length/ (El-Gayar & Ofori, 2020).


Purpose

The purpose of the study is to learn the opportunities for integrating AI technologies to improve the agricultural production of various emerging countries and countries of lower economy (Araújo et al., 2021). The proposed study willu uses literary research and document analysis to explore the various methods of AI technology used in global agriculture and to understanding the challenges in emulating the same. The relationship between AI-based agricultural framework and the various internal and external factors shall provide the desired result, which is understanding the appropriate AI technology necessary for the increase in agricultural production (Araújo et al., 2021).


Research Question(s)

Global agricultural development is gradually changing and the integration of AI technology in agriculture has helped several countries improve their agricultural production. However, the popularity of agriculture has gradually declined in emerging countries and countries with lower economies (Araújo et al., 2021). The decrease in the production and popularity of agriculture in emerging countries is due to several important factors ranging from increased modernization to decrease in groundwater. The lack of a young agricultural workforce is also another factor that negatively affects agricultural production enhancement and development (Araújo et al., 2021).

Moreover, these countries also face a further decrease in agricultural production due to the gradual loss of agricultural land. Therefore, emerging countries need to revolutionize agricultural frameworks to increase agricultural production and improve their economic standards (Araújo et al., 2021). This can be done through the induction of AI technology in agricultural frameworks as this has been a proven method in several developed countries. This proposed study is focused on the integration of AI technology into agricultural processes in emerging countries. Therefore, it looks to answer the following research questions that would help develop a method of AI integration (Araújo et al., 2021).

R1: How can AI technology be used to improve the popularity of Agriculture in Emerging Countries?

R2: How can AI technology be used to improve Agricultural production in Emerging Countries?

R3: What are the challenges & training necessities/needs?/ involved in the implementation of such AI Agriculture processes?


Significance

The importance of agricultural revolution has been the topic of several studies, especially in recent times where several countries are facing economic crises. There has also been significant research into the use of AI tools and technology in global agriculture and its positive effects on the same (Tzachor, 2021). However, there is much to be explored on the integration of AI technology into the agricultural processes of emerging countries. Since agriculture is gradually declining in popularity in several emerging countries, this is an important avenue for research. This will help emerging countries revolutionize their agricultural processes and future-proof their agricultural frameworks (Tzachor, 2021).

Using literary documents on AI integration in global agriculture, the reasons for agricultural production decline in emerging countries, and the opportunities and challenges present in integrating different types of AI technology, the proposed study will focus on understanding the best way to create AI-induced agricultural processes in emerging countries. The study will use document analysis as the main data collection methodology and conduct a thematic analysis on the data collected from the research studies (Tzachor, 2021). This thematic analysis will be focused on the use of different types of AI technology and the external factors like weather, local population, culture, etc. This will help us find the best technology that can be used to improve agricultural production based on a given country’s external factors (Tzachor, 2021).


Definition of Terms

i. Agriculture – this is the science of faming and producing different types of crops

ii. AI-induced Agriculture – An agricultural framework that is based on the use of Artificial Intelligence.

iii. Machine Learning/is there an expectation that these terms will go alphabetically?/ – Machine Learning is a type of Artificial Intelligence that is based on the idea that systems can learn from data, identify patterns and learn to make decisions with limited human intervention.

iv. Deep Learning – Deep Learning is a category of Machine Learning that uses the human brain as a model for processing data. Through Deep Learning, machines can process complex data without human intervention (Tzachor, 2021).

v. Computer Vision – Computer Vision is a type of Artificial Intelligence that trains computers to understand and interpret the visual world using digital cameras, videos and other deep learning modules.

vi. Precision Agriculture – Precision Agriculture is an agricultural management concept that uses technology to observe, measure and respond to various inter-field and intra-field variables to increase crop yields and agricultural profitability.

vii. Predictive Analysis – Predictive Analysis is a branch of advanced analytics that to analyzes current data using various/rewrite for correction/ methods like data mining, statistics, etc., to make future predictions (Tzachor, 2021).


Summary

Agriculture has been declining in popularity in emerging countries. In a time when most of the developed countries are using AI to increase agricultural production, there is no clear indication of the same happening in various emerging and low economy countries. Thus, this proposed study was created to understand how agricultural processes in emerging countries can be improved through AI technology. Through document analysis, the proposed study aims at understanding the best AI technology that needs to be used to improve agricultural production in emerging countries. This is also the main research question that the proposed study aims to answer. The proposed study will also explore the various challenges that will hinder the integration of AI technology in the agricultural processes of emerging countries. Tthe researcher aims at identifying ways to increaseing the agricultural production and the economy of emerging and low-economy countries.



Chapter 2: Literature Review

This chapter will explore the field of AI Agriculture and provide insights on the need for further research in the field through an in-depth literature review. The focus of the literature review is to explore the existing literature and highlight the current trends in the development of AI usage for Agriculture and possible future use in Agriculture. Particularly, it will be a review of articles that focus on the field of AI agriculture. By discussing the potential challenges and limitations in the development of AI in Agriculture, it shall be possible to provide a snapshot of the current state of AI usage in Agriculture. It is evident that agriculture is in decline because of its the diminishing popularity in developing countries. The literature review will use peer-reviewed sources to understand the reason behind the same and the importance of AI agriculture in these developing nations (Beriya, 2020).

AI agriculture has become a major topic of interest for scientific research in the last few years. This can be mainly attributed to the fact that the need for AI in the agricultural sector is rapidly increasing because of the growing population and diminishing farm lands for for agriculture (Garske et al., 2021). In developed countries, AI agriculture supports farmers in the by automating farming practices. Countries with inadequate agricultural production at present can adopt this approach and relieve themselves from food crisis and environmental problems, (Beriya, 2020). Although, the implementation of AI in the agriculture sector is still evolving, the potential of the use of AI in agriculture is promising. By integrating AI into the existing technological system, farmers can use various technologies that include remote-sensing, smart irrigation, and automatic fertilization to provide a high-quality crop. The use of remote-sensing technology to provide an accurate crop yield prediction using information from satellites is a notable example (Beriya, 2020). Although remote sensing technology uses a plethora of information from space to identify a crop, such a system is not yet accessible to developing nations due to the high-cost of satellite-based technology.

In developed countries, the use of robots and smart technologies in Agriculture has helped boost Agricultural popularity and production (Adeleke, 2021). The author states that there have been advancements in terms of crop production techniques. Shacklett (2021) states that increase in farm productivity is possible after learning how to yield more crops in small areas. The objective of this research is to explore the potential of artificial intelligence (AI) in Agriculture and the application of AI in Agriculture, in particular, to improve Agricultural/why cap?/ popularity and production in emerging countries like India and Africa (Garske et al., 2021). The literature review will be focused/whose review are you referring to here? Is it not already existing?/ on identifying the state of research in AI Agriculture and highlight on potential applications of AI in Agriculture, including robotics in Agriculture. The scope of the literature review includes any research which used robotics and AI in Agricultural development, as the focus of the literature review will be the use of robots and/same question again: why do you say the focus will be when you have actually seen that it is?/ AI in Agricultural development. By exploring existing literature in the field, the literature review will be able to identify the gaps in the knowledge and areas of further research in the field (Garske et al., 2021).

Analysis of how different types of data can ensure accurate information collection which will provide a comprehensive review of the literature in the field of agricultural AI applications. Both types of scientific papers can provide valuable information about how research on a particular topic has been conducted (Singh, 2020). After learning about AI integration, it shall be possible to develop new ideas related to agricultural improvements and the possibility of ensuring change improvement in the current environment.

The agriculture sector can receive constant improvement in its operations as machine learning increases reliability and accuracy of results (Liakos et al., 2018). It is possible to perform accurate data access and then conduct review processes whereby researchers shall be able to analyze issues like soil health, weather forecasting, and farming techniques. AI allows use of technology like sensors and farm management/this phrase doesn’t fit here. You are talking about sensors “and,” which makes the reader expect another similar item/ that all work cohesively to handle agricultural production. According to Benos (2021), agriculture experts can use artificial neural networks to enhance the quality of soil output and thus increase reliability when projecting growth. Constant handling of agricultural data leads to better farm handling of information.


Theoretical Foundation

The literary review will also help create a concrete theoretical foundation for the proposed study. Some of the important concepts that needs to be studied in the literature review are the motivation for using AI in agriculture, the barriers for implementing AI agriculture systems, and the significant benefits of using the same. An understanding of these concepts is necessary for automatinge the existing technology in the agriculture sector (Farooq et al., 2020). While literature reviews are often conducted by analyzing the current literature on a certain topic, AI use in agriculture is a very new area of research and hence shows hasonly limited exploration.

It is also important to understand the assumptions associated with the field of AI agriculture and to validate the same through the literature review. One of the main assumptions is that the AI will significantly increase the production rate in an agricultural sector and help in increasing its efficiency (Farooq et al., 2020). Hence, a study on how AI is being used to solve problems and automate some processes within the agriculture sector is also required. In the literature review, the use of the AI within the agriculture sector can also be explored by researching the current progress and barriers that prevents the sector from progressing. Existing literature has AI has been determined that AI improves the way farmers are operating their farms. According to Farooq et al. (2020), it can be possible to improve accurate access to information and unique methods for increased that AI use can get used to increasein farm management.

The literature review will also help verify whether the proposed AI system will help automate the traditional processes of the farming or not. Therefore, the assumption associated with the technology is crucial to be explored. The literature review hopes to identify and define the existing areas of research, gaps, issues, and challenges that are present in the AI agriculture field. This will form the foundation of the research design and help guide the methodology for the research process (Sonaiya, 2019). However, a careful evaluation of the scope of the problem is essential. This will be done through the literary sources that study the existing fields of AI agriculture. This will help create a comprehensive theoretical foundation for the investigation and identification of the problems that are relevant to the selected field of study.

AI in agriculture has shown a positive improvement in its ability to the access to high quality farm management that promotes access to food supplies for the large human populations (Sonaiya, 2019). Limited knowledge about optimization and labor issues creates inappropriate method of balancing farm management. since it gets possible to form valid farm management techniques./can you cut this out without the risk of meaning loss? Automation creates faster access to farm materials which is a critical component of AI in agriculture. Crop harvesting techniques promote better access to farm tools and reliability when dealing with crop yielding methods. This …