Abstract

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

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

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

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

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

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

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

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

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

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

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

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

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

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).

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

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

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

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

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

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

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

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).

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

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

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

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

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

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

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

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

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

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