This research study focuses on exploring the field of AI agriculture from an emerging countries’ standpoint. The goal of the research study is to understand 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 an in-depth literature review will that explore the 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 Abstract 2 Chapter 1: Introduction 5 Background 5 Problem Statement and Significance 6 Theoretical Framework 7 Researcher’s Positionality 11 Purpose 11 Research Question(s) 12 Significance 13 Definition of Terms 14 Summary 15 Chapter 2: Literature Review 16 Theoretical Foundation 18 1. Agriculture in Emerging Countries 20 2. Reasons for Low Popularity 22 3. Importance AI Agriculture 24 4. Exploration of Benefits 27 5. Challenges in Implementation 30 Overview 33 Gaps in Literature 35 Conclusion 37 Chapter 3: Methodology 39 Introduction 39 Statement of the Problem 40 Research Question(s) 40 Research Methodology 40 Research Design 41 Study Population and Sample Selection 42 i. Document Selection 44 ii. Qualitative Interview 44 Data Analysis & Procedures 46 Validity & Reliability 47 Ethical Considerations 48 Limitations 49 Summary 50 References 51 Appendices 55

Agriculture is one of the fields that have been gradually declining in popularity in most countries around the world. The rate of growth of the global demand for agricultural products has been declining in the recent years past. This is particularly significant in countries that are referred to as developing and/or having low economies that had been were dependent on agriculture (Sivarethinamohan et al., 2020). The number of agricultural areas in developing countries like India has begun 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 is because most 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 agriculture in emerging and low economy countries is carried out by the older population. The generational gap poses several problems for the economy. The lack of a younger in agricultural labor population makes the industry all the more unsustainable. Agriculture is 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).

The main problem behind the decrease in agriculture in emerging and low economy countries is 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. This has resulted in the transformation of valuable agricultural lands into factories, companies and residential areas in most of the countries (Tzachor, 2021).

The lack of agricultural knowledge is also a significant factor in developing countries. Knowledge of farming is extremely important for developing countries to manage an agricultural process. Since most emerging and low economy countries need to grow their economy rapidly, they are forced to disregard 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). The research study will therefore seek to perform an extensive discussion looking at how countries can use AI in Agriculture to develop their production activities

The term "AI" refers to information processing and intelligence. The general idea of using IT in agricultural sector is to help the Agricultural students to learn and master their skills on building the applications which can be used to improve agriculture in developing countries. Most studies have reported on the information processing and nature of such systems. Therefore, such studies will be referenced in the proposed study. This proposal will 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 will focus on compiling instances of AI usage in global agriculture and explore the possibilities and challenges that are involved in the same. The study will discuss 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, it will look at the 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 applications such as Plantix. These applications help the farmers to detect nutrient deficiencies in their crops. In many of the countries, the economic output as a result of the advances made in agricultural technology has been greatly increasing. In many of the nations where the production has increased, the development of AI has been a critical help in substantially increasing agricultural productivity and production (Jha et al., 2019). This is evidenced in several literary papers.

The growth of agricultural technology as a field provides great opportunities for emerging and low economy countries that are struggling to improve their agricultural production. Thus, the theoretical framework 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 (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 includes 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 is not an integral part of agriculture in most developing 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 (Karnawat et al., 2020).

In addition to challenges that threaten the AI agriculture framework, there are also various 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.

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 technologies that are 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.

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 China and India (El-Gayar & Ofori, 2020). The research will use document analysis and other indirect methods to quantify the use of AI technology in agriculture. This will help the researcher to determine the efficient technology that could help some of the emerging technology improve their agricultural production (El-Gayar & Ofori, 2020).

The purpose of the study is to learn