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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 countries improve agricultural processes. The research study will also explore the major limitations that have impeded 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 will list 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 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. To ensure that the research study is focused on emerging countries, the proposed 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 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 in turn have put agricultural irrigation at risk has affected the water needed for irrigating the agricultural farms (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 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 sentence would fit better in the ¶ above where you mention groundwater levels/ This is because Mmost of the youths of these countries do not view agriculture as a viable option for sustenance or growth (Green, 2014). 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 Aagriculture in emerging and low economy countries is are carried out by thean older population. The generational gap This poses several problems for the economy. The lack of a younger in agricultural labor population makes the industry all the more unsustainable. 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 the next 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. In the long run, such problems may cause arise such as social unrest or political instability among the people. 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 (Adeleke, 2018). Because of modernization, the younger population in most of the countries does not appreciate 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./ 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 as one of the main sources of in an economy. Instead, they turn to and focus on 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., which is a necessity to mitigate the existing threat to agriculture in most of these countries (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 the same can help needy nations develop their agriculture. 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./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. in the same. 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/ 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 breakthroughs and 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 made in agricultural technology has been greatly increasing. In many of the nations where the production has increased, Tthe 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, Tthe theoretical framework of this research will focus on exploring 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. 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 in alleviating 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/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 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/this sentence merely repeats what you said right before/ (Karnawat et al., 2020).

In addition to challenges that threaten the AI agriculture framework, there are also 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 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)./This ¶ needs more specific matter. You have many repetitions throughout the draft/

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). /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 and the research methodologies are based on 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 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/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 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 a given an emerging 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. Through the proposed study, Tthe researcher aims at identifying ways to increaseing 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 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 in emerging countries is in have started to decline because of its the diminishing popularity in developing countries. 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 farm lands for of crop plants available for agriculture (Garske et al., 2021). In developed countries, AI agriculture supports has shown a positive influence in providing support to farmers in the farming sector by automating farming practices. Countries with inadequate agricultural production at present can adopt this approach and relieve themselves from food crisis and environmental problems, 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 (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 by learning how 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 to understand how the AI can be used to improve and 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 by literature that it 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 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.

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 will be done through a comprehensive search of the literature using keywords such as: machine learning, deep learning, autonomous farming, and AI (Sonaiya, 2019).

 

Review of Literature/You have heading above which reads: Lit Review, p.17

This literature review section will review various peer-reviewed literary sources that are relevant to the scope of the research./I don’t think you need to say that the review will review the peer reviewed lit sources/ This 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 Aa 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) will be included. The current state of the AI and its application in the agriculture industry as well as its progress will be covered reviewed through a bibliography search. The research hypothesis will be derived from the proposed objectives and the conclusions drawn from the literature review. The literature review will be The study will be used to define the research question to be answered by the study, also 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). In each of the topics below, the review of literature shall get conducted and promote understanding of how automation gets involved in agriculture.

1. 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./Is the logic of this sentence a bit puzzling? Please do another read/ 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 resources to pursue agricultural research (Singh, 2020).

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/is 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 there. 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).

Most of the 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, Also, most of the agricultural research is based on research and development initiatives are supported by the United States and other developed countries (Adeleleke, 2021). 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).

Overall, 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, most of the agriculture-related research is predicated on what happens 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. They instead that must rely on locally based research that may not provide more sustainable solutions (Farooq et al., 2020). These warrants continued global effort to identify and develop agricultural production systems in developing countries that are sustainable and economically viable./ not sure if you need this sentence. If you do, revise it for clarity/ Thus, a study on the need for AI agriculture and its future potential is a relevant and 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).

 

2. Reasons for Low Popularity

There are also several literary sources that describe the factors which have caused contributed to the weakening decline in the number of agricultural productivity and popularity. Some of the cited reasons that have been quoted in relevant literary sources are industrial 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 low 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 reasons for the lack lack of agricultural production and popularity for agriculture, research is still needed for its in-depth understanding the extent of impact of these factors is not explored in-depth (Alreshidi, 2019).

TFor example, there is a consensus that the decrease in agricultural production and popularity due to inadequate can be attributed to the lack of agricultural education, especially among the in the case of people from the younger populationdynamic. 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 on the to determine the extent of the contribution of various agricultural education factors towards declining trends in agricultural production (Alreshidi, 2019).

Another factor which is attributed for the dwindling population in the agricultural sector is the introduction of a number of modern agricultural technologies. TFor 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 is are 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 the job market 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 those developing countries (Bannerjee et al., 2018).

3. 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/believe that the impact of the artificial intelligence on the food and agriculture industry is expected to be profound. 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).

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 /per what?/ 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).

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 can 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.

An overwhelming amount of literature recommends 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 difficult hard to process it by humans. 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).

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 a lot of the AI technology and systems that are used today in the agricultural sector have not been extensively tested in emerging countries? 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 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).

4. 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 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 because of climate change. While other countries like India have a much higher population and need more food/sentence fragment/ (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/we are saying the same thing over and over, Sateesh. (Bannerjee et al., 2018).

Another significant benefit of using The AI systems in the agricultural sector will is going to be the increase thed 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/fragment/ (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. Rural farmers will need to be familiarized with AI software with appropriate education programs. 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 difficult to benefit from these applications. This can be mitigated with the aid of AI education for farmers in different developing countries. Because these countries will be able to leverage on the available information, resources, and techniques, and use them to increase the productivity of the farmers in these areas. This will, in turn, lead to higher yields of the crops, increase the profit of the farmers, and more food for their families (Beriya, 2020)./Notice how most of these sentences keep on repeating what the previous sentences stated/

However, the implementation of AI systems in agriculture will be able to help the farmers learn about the diverse types of agricultural applications that are available for them, and the Ddifferent types of solutions that are needed to increase the production of the farmers in their respective farms (Beriya, 2020). Because, as mentioned earlier, the lack of such applications and solutions is going to be the main reason why farmers in rural areas don’t have access to modern farming technology and solutions/we have said this already, please!/. The increasing demand for food in developing economies is also going to have a significant impact on the agricultural sector (Beriya, 2020). As a result, the need for the implementation of technologies in agriculture to increase agricultural productivity is also going to be increasing. The implementation of the technology will be able to help the farmers improve the productivity and sustainability of the agriculture sector, which is going to have a significant impact on their respective economies. Because, after all, an effective agricultural sector will always have a huge impact on the economies of developing countries/rpt (Beriya, 2020).

As mentioned earlier, when a majority of people are unemployed and they don’t have enough to eat, it will have a significant impact on the economy of a developing country. The development of AI technologies in agriculture can significantly increase the agricultural productivity of the farmers, which can have a significant impact on the agricultural sector in these developing countries/same thing again (Garske et al., 2021). Therefore, the development of the AI technology is going to have a significant impact on the agriculture sector in these countries/stated already/. These are some of the major benefits of incorporating AI technologies in the agriculture sector. Therefore, the agriculture sector in developing countries will have the best opportunity to benefit from the implementation of these technologies in their agriculture sector (Garske et al., 2021).

In general, these countries are developing countries/yes, you said it already/. Therefore, they are among the most vulnerable countries for the use of AI technologies in the agricultural sector. The main reason for this is that most of the agricultural sector in developing countries rely on manpower (Garske et al., 2021). The manual implementation of the farming techniques used in these countries limit their productivity. It can never match the power of AI automation. is going to have a negative impact on the agriculture sector in these developing countries. This is because in developing countries, the implementation of AI technologies in the agriculture sector will make it a lot easier for the farmers to use them for agriculture than when it is manually done. As a result, the farmers will be able to achieve much more through the implementation of the automation. This is the consensus among several experts and researchers in the field and it is also outlined in various literary sources (Garske et al., 2021).

The development of the AI will also help the farmers improve their farming techniques. However, the adoption of newthe technologies will not be easy for these farmers. Their primary barrier for change will be lack of education and training. This is because they are likely to be lacking the technical skills and knowledge of the farming technologies. Therefore, the adoption of the agriculture technologies will be more complicated for the farmers who have limited knowledge and experience in the field (Singh, 2020). In addition, the development of the agriculture technologies also raises some concerns from the users of the technologies. The main reason for this is that the implementation of the technologies in the agriculture sector may be accompanied with some potential disadvantages/you might say what those disadvantages are and then move on to the next or point or section.s. Thus, there is a need to conduct a comprehensive study in the agricultural sector in order to address these concerns before the implementation of the technologies (Singh, 2020).

5. Challenges in Implementation

As mentioned earlier, several literary articles point out that AI implementation in the field of agriculture will not be an easy task for the farmers. There are several reasons for this. Firstly, the technologies will bring about a number of challenges. The main reason for this is that these technologies are completely dependent on machine learning and artificial neural networks. There are several challenges in the usage of these technologies (Sonaiya, 2019). For instance, these technologies are difficult to use for a wide range of tasks in the farming sector. Apart from this, the farmers are expected to make a number of changes in their agriculture sector in order to make the usage of the technology easy. It is expected that the adoption of these technologies will not be easy for these farmers./check the first sentence of this ¶–you are repeating it here/ This is because the machines have high learning requirements (Sonaiya, 2019). The farmers are expected to spend a lot of time to learn the different farming techniques that the AI machines are capable of performing. In addition, the farmers are also expected to change their working environment in order to make the usage of the AI systems and machines easy (Sonaiya, 2019).

Another challenge for implementing AI systems and machines in the agriculture sector is the security issues that might occur during the implementation. The usage of AI systems and machines for the control of the water or for pest eradication may involve with some potential security issues. Due to the fact that these systems are completely dependent on machine learning, the developers are unable to control these machines due to various factors. Some of these factors include the environmental conditions (Sonaiya, 2019). This is because if these systems are to work under a particular environmental condition, then they will learn and adapt accordingly. The developers also cannot identify the reasons why the AI machines are not working as expected. Due to this, the developers cannot make any modifications or changes in order to make the machines working correctly. Due to these reasons, the machine is considered as unsafe or in a wrong place (Sonaiya, 2019).

Some researchers and experts feel that the benefits of using AI agriculture overshadow any and all challenges involved in the implementation of the technology. These experts see the use of AI systems and machines as a key to the future of farming (Shokat & Großkinsky, 2019). This is because if this technology is to be used, then it will help them get the products that they need at a cheaper price. This is because most farmers or growers are willing to spend more on the production of a single product. Therefore, the use of these machines will help them save on their expenses (Shokat & Großkinsky, 2019). This means that they are able to make sure that they get the maximum benefits of their products and also be able to make sure that they maximize their profits. In the long term, this technology will help them with the usage of a single system and machine. With the usage of one machine or one system, they will be able to control multiple or even all farms (Shokat & Großkinsky, 2019).

There are also literary sources that are concerned with the use of AI agriculture in emerging economies like Africa because these places are considered as most deprived with respect to the use of technology. These machines are the solution to many problems and issues. These issues involve human labor and its usage in the production of food (Shokat & Großkinsky, 2019). Therefore, AI agriculture will help these communities and countries overcome these problems that are currently in the pipeline. The lack of respective technology is a significant limitation to the usage of AI agriculture technology in Africa. It is noted that the main focus is on the improvement of the quality of life in these regions and countries. This means that a lot of efforts will be made in the improvement of the lifestyle, work culture, and the society of these countries (Shokat & Großkinsky, 2019). Furthermore, it is noted that agricultural and farm practices are considered as the major sources of poverty and hunger.

Other than this, we can also identify the major and critical challenges related to the use of AI agriculture in emerging countries. First, AI agriculture is mainly applied on the larger scale. This means that they can be applied by farmers or farmers groups (Garske et al., 2021). However, in some countries like Africa, the implementation of the technology is not as broad as in other countries. This is because it is applied in some regions only. However, the extent of the challenges involved in the system is rather theoretical in most cases because most literary sources do not approach the issues from a real-life standpoint (Garske et al., 2021). This limits the ability to identify and eliminate the challenges in the use of AI agriculture. Furthermore, it is noted that the focus is more on a theoretical approach that deals with the general concept rather than an in-depth analysis of the topic. With an in-depth analysis, we can identify the major issues that are related to the use of AI agriculture in emerging countries like Africa (Garske et al., 2021).

 

Overview

AI agriculture is a relatively new concept that has gained prominence because of its immense benefits to the field of agriculture. Through the literature review, it is clear to see that AI agriculture has become a major part of agricultural research and development in developed countries. This is because it helps address various global challenges that have led to the decline in agriculture (Singh, 2020). Some of these challenges include environmental challenges, natural disasters, labor issues, market related challenges, etc. Furthermore, research has also shown that if this technology is not applied effectively, it would lead to an increase in hunger and poverty in developing countries (Singh, 2020). The development of this technology is also very critical in developing countries where its adoption would create a new age of prosperity in these countries.

In developing countries, agriculture is often the main source of food. Thus, the ability to apply AI to agriculture would create many jobs and opportunities to these countries. This can be achieved through the development and use of different technologies that are based on AI. The use of AI in agriculture is also a major source of job creation. AI is also being used to address the challenges that are being faced by the current system (Singh, 2020). This means that it is using technology to address a problem that is being faced by farmers. By addressing this problem, it ensures a better and sustainable farming system in the developing country. Since this is an important step for the developing country, the adoption of this technology should not be delayed. Due to the rise in the prices of food, the growth of global population and the lack of food supplies, the use of AI in agriculture is the solution to this problem. This is one of the reasons that the research study was created (Singh, 2020).

The literature review also shows that AI and Agriculture can not only reduce the use of harmful inputs in the agriculture but also it can reduce the loss of farmers from the current farming system. Many of these losses are the result of various diseases that affect the farms and result in poor yields. This is because of problems like soil degradation, water loss, weather events, etc. through the development of AI, it can be used to reduce the harmful effects of agriculture (Singh, 2020). Through this it ensures that farmers produce more, have fewer losses, and have a happier life. It is also believed that with the widespread use of AI technology in agriculture, the entire food chain would benefit. These benefits assure that AI agriculture is on a growth curve and will help the global agriculture sector immensely. This overview lays a foundation for understanding how AI agriculture can benefit the agriculture sector in developing countries (Singh, 2020).

In addition, the challenges involved in implementing AI agriculture were also highlighted. Some of these are that the lack of knowledge, time, and cost to adopt the technology in agriculture. It is also important to understand that any AI related advantages must be weighed against the disadvantages (Gurumurthy & Bharthur, 2019). Disadvantages are mainly the cost and the effect on the workers. As the literature review shows, some of the applications of AI in agriculture use machine learning algorithms that are not able to detect problems in the farms. In this case, with the current algorithms, it takes a lot of human labor and effort to help the machines learn. Therefore, although AI helps to reduce the number of workers, it does not replace their labor. Another important factor is the lack of education and training for farmers to be able to incorporate AI in their daily work (Gurumurthy & Bharthur, 2019).

The literature review also highlights that many of the agricultural industries are not interested in adopting the technology because they believe that AI can only benefit wealthy nations. Therefore, this shows that the adoption of the technology in agriculture in developing countries must be encouraged (Gurumurthy & Bharthur, 2019). Furthermore, there are limited reports that have compared the benefits of adopting AI in agriculture with other technologies such as biotechnology. This highlights that there is more research required to evaluate the benefits of adopting AI in agriculture. Finally, the literature review shows that there is the need for government policy to encourage the adoption of AI in agriculture (Gurumurthy & Bharthur, 2019). This encourages the agricultural sectors to adopt AI in their farms and ensure that it benefits all sectors of the agriculture industry.

 

Gaps in Literature

One significant gap in the existing literature of AI agriculture systems is that there is little to no literature from the perspective of the developing countries. As such, the research study will not only cover the current literature but it will also look at the needs of the farmers in developing countries and suggest potential solutions for them (Gurumurthy & Bharthur, 2019). Through these findings, the research study aims to recommend the best solution for agriculture development in developing countries. The findings that will be presented in the research study will include a thorough overview of the literature related to the topic, needs of farmers in developing countries, and recommendations for the creation of the next generation of AI agriculture systems. It is believed that the research study will provide the needed tools for the development of AI technologies for agriculture and help reduce harmful emissions of farming technologies and improve the welfare of farmers. This will ultimately help agriculture develop faster (Farooq et al., 2020).

Another gap is that most of the literary sources that cover the AI agricultural benefits of emerging countries primarily consider prime models like India where there is significant technology and AI growth (Eli-Chukwu, 2019). Even though these literary sources provide valuable and factual data, they do not cover other regions or countries. This is especially true in the case of countries that are lower on the development spectrum like Africa. Since these countries have low technology or AI integration systems, the impact of AI agriculture in these countries is yet to be clearly understood (Eli-Chukwu, 2019). This warrants the need for a research study that will cover this critical aspect by looking at how the use of AI technologies in developing countries, including Africa. By conducting the study, farmers in those countries can address the challenges of the region and the impact that the proposed solutions can have on agriculture development in these countries.

Furthermore, there is also the fact that most of the literature is based on theoretical analysis of AI agriculture’s relation with developing countries. While this is significant research, there needs to be more research that provides empirical evidence from the perspective of developing countries (Eli-Chukwu, 2019). Therefore, this research study on AI agriculture is of high interest to farmers in the region, as it will provide evidence to these farmers on how they can benefit from AI agricultural systems. By using qualitative interviews that are subjective in nature, the research study hopes to provide significant empirical evidence and research studies that specifically covers AI agriculture adoption in developing countries (Eli-Chukwu, 2019). This is of critical importance for AI development in the agricultural sector. This will not only help reduce adoption barriers of new and emerging AI technologies but also ensure that new and emerging AI technologies have the right support mechanisms in place that will help promote its effective and successful adoption. In this regard, this research study can provide a platform for a discussion on how AI agriculture can be promoted by relevant stakeholders (Eli-Chukwu, 2019).

 

Conclusion

This literature review focuses on understanding the relationship between AI tools/systems and agriculture. The main goal of the literature review is gaining an insight on the existing goal is to gain an understanding of the current research on the topic and identify the main themes within this research area. As the research on AI agriculture is still at the initial stages, there are many unexplored topics related to this research (Beriya, 2020). The findings in this literature review showcase the impact of the AI systems in both developing and developed countries. The main goal is to understand the current agricultural environment in developing countries and investigate the issues within the agricultural sector in these countries. In developing countries, the use of AI applications is at an early stage of adoption and development. This means that the use of AI applications has not yet been established in agricultural sector. However, the research literature has shown that farmers in developing countries are already making use of some of the AI tools/systems (Beriya, 2020).

Furthermore, some studies highlight that to overcome the challenges associated with the adoption of AI technology, there are some significant barriers within the existing agricultural systems that hinder the adoption of these tools/systems. The main goal of this literature review is to identify the existing technologies and to understand their importance in the adoption of AI technology. In addition, the literature review also looks at the benefits of AI agriculture. Based on the literature review, there are several benefits of using AI agriculture like increased agricultural production, improved efficiency, and enhanced food security. The findings in this literature review show that the adoption of AI technology is only feasible when proper policies and regulations are in place and that there is a need to educate the farmers. In addition, the existing literature shows that there is a need for the development of an open environment in order for the application of AI to take place in the agricultural sector (Beriya, 2020).

The challenges involved in implementing and integrating AI systems are also explored in several literary sources by researchers and experts in the field. In the case of developing countries, there are several barriers for AI agriculture integration (Alreshidi, 2019). Some of them include lack of education, lack of technology, lack of funding, and lack of regulatory policies. Furthermore, the adoption of AI systems in developing countries can be challenging as many of these countries are still trying to improve agricultural systems. Literary sources also show that the introduction of a modern technology, such as AI, is not feasible without developing a better understanding of the technology (Alreshidi, 2019).

In the case of developing countries, there is a need for identifying the challenges of implementing AI systems so that they can address these challenges. This is because there is little to no literature on the topic from a developing country’s perspective. This is a significant gap in the literature that we are hoping to fill with this research. Another gap is the lack of subjective analysis in the field of AI agriculture. A subjective interview in the field will help to provide a comprehensive and accurate understanding of the challenges faced by developing countries in the implementation of AI in agricultural applications (Alreshidi, 2019). In addition, this paper is one of the first pieces of research that has attempted to understand the importance of developing an open environment for the implementation of AI in the agricultural sector.

 

 

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).

 

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.

 

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

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).

 

Research Design

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 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 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 since firsthand data shall get collected from appropriate respondents (QuestionPro, n.d.). 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 people’s perspective and their expectations regarding the AI agriculture field and future trends in its development (Lane et al., 2018).

 

Study Population & Sample Selection

The proposed study is focused on the impact of AI agriculture in emerging countries. This makes the research setting a broad one, as it concerns all the farmers, farming-related businesses, and agricultural analysts in the emerging countries. The population group of the research study also spans across emerging countries like India and Africa as it concerns the state of emerging nations. Since the study also focuses on the use of AI agriculture, the total population also includes farmers in developed countries that use AI agriculture tools and systems (Lane et al., 2018). Therefore, we can clearly see that the total setting for the study spans across farmers from lower economic backgrounds or farmers from countries where farming is losing popularity like India.

Since the study uses thematic analysis, the sampling method concerns both the document selection and the participant selection. The document selection is strictly based on topic relevance and context. The main goal here is to identify documents that are peer-reviewed and authentic (Braun & Clarke, 2019). The document needs to also focus on AI agriculture, particularly its benefits and limitations on farmers in developing countries. The proposed study uses sampling as a method for selecting the desired participants from the large pool of population (Braun & Clarke, 2019).

The participant selection will be conducted through snowball sampling because of the lack of field exposure of the AI agriculture field. Collaboration with persons dealing with AI in the filled of agriculture shall improve on better access of data from the appropriate persons. The main focus here is to obtain a participant pool from the agricultural domain that can provide relevant feedback regarding the quality of the input and to obtain as many relevant participants as possible. Through snowball sampling, the participants will be able to refer other relevant participants that fit the criteria for the qualitative interviews. Integration of AI experts and agriculture department leaders shall promote better access to data after collaborating with companies conversant in the field. To prevent research-bias, the proposed study will be evaluated based on the prospective participant based on specific characteristics (Braun & Clarke, 2019). The characteristics for the snowball sampling selection will be knowledge on AI agriculture and its benefits/limitations. The sample population will also be selected based on their farming experience as it adds significant value for the research.

 

Data Collection Methods

The proposed study has two methods of data collection. The primary data collection methodology will be document selection that focuses on identifying relevant peer-reviewed literary sources for the primary research. The secondary data collection methodology is a qualitative interview that will be conducted to participants selected using snowball sampling methodology (Braun & Clarke, 2019).

i. Document Selection

The document selection will focus on selecting relevant documents that are focused on the AI agriculture field. The document selection method will consist of a number of stages. The first phase is to identify the relevant documents for the proposed study. In this section, the relevant document will be selected from a pool of peer-reviewed sources. The sources will be examined for scientific and topic-related relevance (Braun & Clarke, 2019). The main methodology that is used in the document selection process is purposive sampling that will be based on judgmental analysis of documents based on the topic and thematic relevance on the AI agriculture field.

ii. Qualitative Interview

The goal of the qualitative interview is to ensure that there are comprehensive in-depth answers that can be used for thematic analysis. The interviews will be conducted through virtual telephone call and web-based discussions where an interviewee can share in-depth thoughts on the topic of the AI agriculture field. Collaboration with fifty participants as a maximum shall be the suitable method of ensuring improvement in accurate information access. Interview questions shall be mandatory and those in appendix A will provide information access. As mentioned earlier, the participant selection for the interviews will be based on snowball sampling where the researcher will connect with a participant with expertise in the AI agriculture field and ask them to refer other relevant participants. The questions of the structured and formal interview were closely based on the research questions to enhance the quality of answers (Braun & Clarke, 2019). The interviews were also designed to have a variety of topics of relevance to the field.

Through this section, the proposed study will provide a step-by-step exploration of all the major data collection steps that were used.

1. Identification of peer-reviewed literary sources from verified resource pools (Google Scholar)

2. Examination of Scientific and Topic Relevance.

3. Research analysis of the selected document for factual sections about

a. AI agriculture benefits and limitations

b. specific challenges based on AI agriculture systems

c. and geographical, cultural, and technical challenges from emerging countries.

4. Data collation using uniform random sampling of information.

5. Participant selection for secondary data collection using snowball sampling methodology.

6. Selecting referred participants based on specific characteristics (age, agricultural experience, AI agriculture knowledge)

7. Information collection via structured virtual interviews (Braun & Clarke, 2019).

 

Data Analysis & Procedures

The data analysis methodology and procedures will be the focus of this section through an in-depth exploration. The main goal of the qualitative data analysis is identifying actionable and factual data about the limitations and challenges of AI agriculture processes (Vaismoradi & Snelgrove, 2019). The data collected through both literary research and qualitative interviews needs to be focused on thematic and factual relevance of the issues. This thematic process is also the point of reference in the case of the analysis of the specific issues related to the AI agriculture processes. The data collected will be used to construct an independent and objective, well-defined dataset that will lead to the final report.

The data related to AI agriculture’s benefits and limitations will be inferred and thematically categorized for analysis after the identification of relevant information groups in both the documents and the interviews. These information groups will be grouped for analysis and extraction of factual data that will be divided into an initial set that consists of relevant data elements and a supplementary set that consists of additional data elements from secondary data collected through interviews. The thematic analysis of the core research data allows researchers to gain insights on AI agriculture limitations and challenges related to the agricultural sector (Vaismoradi & Snelgrove, 2019). The supplementary set of secondary data and the additional data elements from secondary data collected through interviews will be used to develop a conceptual framework that can guide a final report on AI agricultural production and use in the Agri-industrial economy. The Agri-industrial economy incorporates agricultural techniques to the current industry development stages. One of the purposes of this is to define the most promising areas for further research. The statistical significance for the secondary data and the additional data elements that are collected through interviews will be assessed using the IARF tool, which provides statistical significance estimates for all sample sizes of samples and the number of observations (Vaismoradi & Snelgrove, 2019).

 

Validity & Reliability

The proposed study will provide valid assumptions to indicate that the lack of technical education and knowledge about AI agricultural tools stands as the most critical limitation for the lack of penetration in emerging countries. The other factors include geographical and technical challenges. These are factors that are widely acknowledged in several peer-reviewed articles in reputed literary sources (Morris & James, 2017). Furthermore, the interviews will also add significant insights into the limitations of AI agricultural capabilities in emerging countries. The quantitative interview needs to be highly valid in evaluating AI agricultural technology in emerging countries. The interviews need to focus mainly on the specific needs of farmers and their knowledge base and the extent to which farmers will adopt such technologies for the new crop. This leads to a highly important and timely contribution to knowledge and awareness in these areas (Morris & James, 2017).

The research instruments that will be used, both thematic analysis and qualitative interviews, can be considered as highly reliable because of the instruments’ ability to render excellent insights into the field of AI agriculture. Integration of inter-rate agreements shall promote less ambiguity and accuracy of the research process. The thematic analysis was considered because of the consensus that it is easy-to-emulate and can be adopted to suit other future research iterations. (Morris & James, 2017). Therefore, there is a high level of probability that the results generated through the document analysis will stay consistent on repeated trials. The interview questions were created to be highly specific, and hence the values generated through the interviews might change based on the interview framework. The study hopes that the interviews represent a valuable approach to the field, especially as these types of interviews have the potential to inform and enlighten, as well as influence future decision making (Morris & James, 2017).

 

Ethical Considerations

Since the primary research methodology is document analysis, the ethical considerations surrounding the methodology are firmly based on researcher behavior and data collection. Therefore, the major ethical concern is the misuse and falsification of information from literary sources (Lane et al., 2018). Hence, the data integrity of the literature collection is critically important for the factual accuracy of the proposed study. The study will conduct external and internal review/assessments to ensure that data falsification is not a major concern. Another major ethical issue is misinformation during participation selection and interview process. Any misinformation during participant collection will skew the research findings resulting in negative impact on the study’s goals. With the aid of the monitoring activity, the research instrument data and the interviews should provide good data that can aid in understanding the data’s validity (Lane et al., 2018).

Participants who will be selected must also be educated about the goal of the research before interview to ensure that they are compliant interview objectives. In addition, an interviewee must provide accurate and balanced information about the research topic, such as the research question asked (Lane et al., 2018). Use of informed consent forms where interviewees accept to provide accurate data and comparing responses to the research topic shall be valid methods of ensuring study accuracy. The overall approach and methodology will use to conduct the research needs to be very detailed, which allows data collection through questions and answers to be carried out within minutes from the beginning. Furthermore, the research methodology will be based on a systematic data collection from multiple sources and is consistent with the National Institute of Health guidelines. The research team has ensured that the above-mentioned ethical procedures are followed (Lane et al., 2018).

 

Limitations

Even though the proposed study will be highly informative and provides factual results on the limitations of the AI agriculture sector, there might be a few limitations. One of the main limitations of the proposed study would be that it is localized in nature. Since the literary sources selected for the research study will be focused on only India and Africa, it might fail to showcase the impact of AI agriculture on a broader level. (Terry et al., 2017). One factor that leads to the same is the lack of varied sources related to specific developing nations, and this can limit the capability of the research study significantly. An option is conducting region-wise interviews and surveys and use the data collected for further research that is focused on identifying geographical and technical limitations of AI agriculture integration (Terry et al., 2017). Snowball sampling could also create a sampling bias, which might become a major limitation. Issues can occur when a particular network gets reused when experts who know each other recommend their colleagues. Ethical concerns can occur when respondents find it hard to integrate their peers.

Another limitation could be the use of snowball sampling for the interview participant selection. Since the participants of the interview are responsible for selecting other participants, there is a high probability of sampling bias. Even though the proposed study will regularly assess and monitor the interview process, there is a chance for sampling bias (Terry et al., 2017). One scenario is an environment where interview participants can be trained by other participants, which could impact the ability of determining factual findings. One way that can eliminate the same is by conducting interviews to participants selected through other sampling methodologies. These two might become the major limitations in the proposed study (Terry et al., 2017).

 

Summary

This chapter provides a comprehensive overview of the research methodology that was used in the proposed study. The study aims at understanding the limitations and challenges that have impacted the penetration of AI agriculture in developing countries. The core research methodology that will be used in the proposed study is thematic analysis of selected literary sources. The proposed study also uses secondary interviews for quantifying the core findings. The research will be conducted through a sequential design that emphasizes on a blended framework that is based on thematic analysis. The main goal of the research design is to enhance the understanding of AI agriculture, particularly the limitations that surround its implementation in developing countries. The sample participant base for the secondary qualitative interviews will be selected from a group of farmers, farming-related agencies and agricultural analysts that have knowledge about farming in developing countries and AI agriculture. The sampling methodology that is used is snowball sampling due to the limited exposure about the agriculture field.

The data collection methodologies that will be used in the proposed study are document selection and qualitative interviews. The selected literary sources will be analyzed for information on AI agriculture’s benefits and limitations; and factors concerning developing countries. The data collected will be thematically categorized to understand the major factors that challenge the use of AI agriculture. The same will be quantified through a thematic analysis of the interview answers. The results should show that lack of technical education and AI agriculture knowledge is the major challenge for the implementation of AI agriculture. The proposed study’s main ethical concern could be document and interview data integrity, and constant monitoring/assessment needs to be undertaken to ensure that ethics are maintained. The proposed study could also be limited in scope because of the lack of documentation about geographical limitations.

 

References

Adeleke, D. (2018). An expert shares the simple reasons why young people are not interested in agriculture and how to change that. Retrieved 9 January 2022 from https://africa.businessinsider.com/strategy/strategy-an-expert-shares-the-simple-reasons-why-young-people-are-not-interested-in/5zebnsj.

Alreshidi, E. (2019). Smart sustainable agriculture (SSA) solution underpinned by internet of things (IoT) and artificial intelligence (AI). arXiv preprint arXiv:1906.03106.

Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities. Agronomy, 11(4), 667.

Bannerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1-6.

Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors (Basel, Switzerland), 21(11), 3758. https://doi.org/10.3390/s21113758.

Beriya, A. (2020). Precision Agriculture to Digital Agriculture: A Literature Review.

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597.

El-Gayar, O. F., & Ofori, M. Q. (2020). Disrupting agriculture: The status and prospects for ai and big data in smart agriculture. In AI and Big Data’s Potential for Disruptive Innovation (pp. 174-215). IGI Global.

Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377-4383.

Farooq, M. S., Riaz, S., Abid, A., Umer, T., & Zikria, Y. B. (2020). Role of IoT technology in agriculture: A systematic literature review. Electronics, 9(2), 319.

Garske, B., Bau, A., & Ekardt, F. (2021). Digitalization and AI in European Agriculture: A Strategy for Achieving Climate and Biodiversity Targets?. Sustainability, 13(9), 4652.

Green, D. (2014). Who Wants to Farm? Hardly any young people, it seems. Should/Could that change? Retrieved 9 January 2022 from https://oxfamapps.org/fp2p/who-wants-to-farm-hardly-any-young-people-it-seems-shouldcould-that-change/.

Gurumurthy, A., & Bharthur, D. (2019). Taking Stock of Artificial Intelligence in Indian Agriculture. Artificial Intelligence in India, 3.

Jain, P. (2020). Artificial Intelligence in Agriculture : Using Modern Day AI to Solve Traditional Farming Problems. Retrieved 9 January 2022 from https://www.analyticsvidhya.com/blog/2020/11/artificial-intelligence-in-agriculture-using-modern-day-ai-to-solve-traditional-farming-problems/.

Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1-12.

Karnawat, M., Trivedi, S. K., Nagar, D., & Nagar, R. (2020). Future of AI in Agriculture. Biotica Research Today, 2(9), 927-929.

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors (Basel, Switzerland), 18(8), 2674. https://doi.org/10.3390/s18082674.

Lane, D., Chatrchyan, A., Tobin, D., Thorn, K., Allred, S., & Radhakrishna, R. (2018). Climate change and agriculture in New York and Pennsylvania: risk perceptions, vulnerability and adaptation among farmers. Renewable Agriculture and Food Systems, 33(3), 197.

Mapulanga, A. M., & Naito, H. (2019). Effect of deforestation on access to clean drinking water. Proceedings of the National Academy of Sciences of the United States of America, 116(17), 8249–8254. https://www.jstor.org/stable/26703498.

Methorst, R., Roep, D., Verstegen, J., & Wiskerke, J. (2017). Three-Fold Embedding: Farm Development in Relation to Its Socio-Material Context. Sustainability, 9(10), 1677. doi: 10.3390/su9101677.

Morris, W., & James, P. (2017). Social media, an entrepreneurial opportunity for agriculture-based enterprises. Journal of Small Business and Enterprise Development.

QuestionPro. (n.d.). Types of Interviews in Research and Methods. Retrieved 9 January 2022 from https://www.questionpro.com/blog/types-of-interviews/.

Shacklett, M. (2021). AI and robotics are helping optimize farms to increase productivity and crop yields. Retrieved 9 January 2022 from https://www.techrepublic.com/article/ai-and-robotics-are-helping-optimize-farms-to-increase-productivity-and-crop-yields/.

Shokat, S., & Großkinsky, D. K. (2019). Tackling salinity in sustainable agriculture—What developing countries may learn from approaches of the developed world. Sustainability, 11(17), 4558.

Singh, S. S. J. (2020). A Review on Usage and Expected Benefits of Artificial Intelligence in Agriculture Sector.

Sivarethinamohan, R., Yuvaraj, D., Priya, S. S., & Sujatha, S. (2020, December). Captivating Profitable Applications of Artificial Intelligence in Agriculture Management. In International Conference on Intelligent Computing & Optimization (pp. 848-861). Springer, Cham.

Sonaiya, E. B. (2019). A Rebirth of the Development of Commercial Large Scale Agriculture in Nigeria.

Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The Sage handbook of qualitative research in psychology, 17-37.

Tzachor, A. (2021). Barriers to AI Adoption in Indian Agriculture: An Initial Inquiry. International Journal of Innovation in the Digital Economy (IJIDE), 12(3), 30-44.

Vaismoradi, M., & Snelgrove, S. (2019, September). Theme in qualitative content analysis and thematic analysis. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 20, No. 3).

Weißhuhn, P., Helming, K., & Ferretti, J. (2018). Research impact assessment in agriculture—A review of approaches and impact areas. Research Evaluation, 27(1), 36-42.

 

 

Appendices

Appendix A

Initial Survey Questions

Please answer the following questions.

1. Name:

2. Nationality: ________________ Age:________________

3. Circle: Female Male

4. Circle Religion: Christian Moslem Other (please specify)________________

5. Contact phone number: Contact email:

6. How long have you been working in the agricultural sector?

7. How does AI promote better agricultural industry management?

8. Have there been valid economic benefits of using AI in the current business industry stage?

9. How do some countries deal with agricultural problems that lead to low crop yield?

10. Have there been improvements in food management in the current industry stages due to AI?

11. Does artificial intelligence promote food security in terms of new methods of farming?

12. How does the food and agriculture industry handle new technologies that offer better food production?

13.Circle willingness to participate in research

Oh yes! Maybe No, thanks

Appendix B

Logistics Survey

Thank you for your interest to participate in this study. The study is structured into a unique method of seeking information from participants in terms of groups of 5 to 10 participants. The study shall take two hours each to promote a feasible schedule and handling of all participants. It is imperative to ensure the survey aligns with your schedule and thus the section below shall require your response. Please tick the choice in the bracket beside your appropriate day. Thank you.

1. What day of the week is most convenient for you?

Monday ( ) Tuesday () Wednesday ( ) Thursday () Friday ( )

Saturday ( )

2. What is the most convenient meeting time for you?

9pm.- 11pm. ( ) 2pm.- 5pm. ( ) 7pm.- 9pm. ( )

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