Based on the general feedback attached, we need to update copy of chapter 1 2 3 attached.

Insight Consulting, LLC

October Deliverable:

Summary of Student Revisions and Recommendations

Version 1.0

October 29, 2021

Presented by:

Dr. Brittny James

October Deliverable

Summary of Student Revisions and Recommendations

General Comments:

Of the five student papers reviewed, all clearly need more detailed revisions, including returning to the literature and properly citing claims made throughout the document, more thought into conceptualizing their ideas, developing attainable research questions for a dissertation study, and developing a detailed methodology section with clear steps for how the dissertation will be completed (i.e., data collection, sample description where applicable, data analysis tools). The table below will detail the summation of comments from Dr. Gyimah-Concepcion with the explanation of such needs to the program of study, and recommendations for how to address each revision need should you choose to continue with the written dissertation as the measure of success for students to graduate. Comments below were consistent for all students. Given these recommendations would require intensive time and human capacity, additional conversation can ensure on how to move forward without impacting the graduation of these students and ensuring a meaningful matriculation through the Doctor of Education in Computer Science program with a practice-based format instead of the traditional written dissertation.

Table 1. Explanation of revisions needed for the DSC program student submissions of Chapters 1-3

Revisions Needed



1. Conceptualization of study design needed

There is a general lack of clarity about the concept of the study, meaning no students were able to articulate a clear purpose and rationale for their proposed studies, nor is it evident the type of study that is proposed (e.g., phenomenology, case study, observation, etc.). Once research methods are clarified, then there can be a clear explanation of data collection procedures and measures, as appropriate. This is also either unclear or not described in the documents.

This would take detailed discussions with students to understand their goals for each study and assistance articulating these studies’ purposes. Essentially, either the students’ committee members or an outside research assistant should be employed to carry out this task. It is evident that the students do not understand the research process; therefore, a project-based format is recommended such that they can still employ their obvious technical expertise to a predetermined problem.

2. Missing citations

APA 7 needs to be employed for in-text citations. This will take time and resources for students to return to the literature, find the claims made, and correctly cite them. It is not clear based on these submissions whether students are committing plagiarism purposely or if there is a lack of understanding about how and when to appropriately cite information. Citations are inconsistent between and within students’ documents. This comment is also applicable to the document formatting in general, and references.

If students can relocate studies or links to information in these documents, the recommendation is to hire an APA 7 expert to edit these documents; however, this would cost approximately 60 hours of work, and students would then be disconnected from the process. To fully engage students, a guided writing process would be best, but if this is not an intention for future cohorts, this recommendation is a waste of time and resources.

3. Proposal language not used (should be in future tense)

Language in these documents is written as if the studies have already been either approved or completed. Given these are proposals, proper tense should be future (will or would). Current language might mean that language has been improperly obtained from another study or document (refer to revision #2).

Revise documents to reflect future tense and ensure that the proposal is original.

4. Content organization and clarity needed

Content is arbitrarily placed and often the wrong chapters/sections. This is also a function of the students’ lack of understanding of APA 7 format (refer to revision #2).

Given previous recommendations, once a clear understanding of the proposals is developed, these sections can be created in APA format, with templates/guidance for what should be included in each section (see recommendations #1 and #2).

5. Unclear/inconsistent definitions of terms

Definitions of terms are included in Chapter 1, but throughout the documents, either different terms are used or other definitions are applied.

Revision of documents to include consistent language throughout.

6. Lack of explanation of appropriate theoretical frameworks

It is unclear if theoretical frameworks were advised to be part of these proposals, as explanations of theory are largely absent from these documents.

If theory is not a requirement, it should be reconsidered and included. This very necessary revision might require a refresher course on the available popular theories used in computer science literature.

7. Lacking understanding of ethical considerations

These sections are either missing or incorrectly written.

Given the lack of clarity about research and data collection, additional problems will likely arise during the data collection and analysis phases that could create ethical dilemmas for the program. To avoid, students could be tasked with revising the current documents to fit a literature review format to identify gaps in the available literature. Upon identification of research and practical needs, students



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



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

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

Chapter 1: Introduction


Agriculture has been a field that is gradually declining in popularity in several countries around the world. The rate of growth of the global demand for agricultural products has also started to decline in the recent past. This is particularly significant in countries that are referred to as developing and having low economy that were dependent on agriculture (Sivarethinamohan et al., 2020). The number of agricultural lands in developing countries like India have started to decrease. This decrease can be attributed to several factors including an increase in modernization which has changed the way of life of people from doing agriculture as a way of earning their living to other modernized means and the decrease of groundwater levels in several regions which has affected the water needed for irrigating the agricultural farms (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 reduces their economy. An increase in agricultural production helps lower food prices and increases the country’s ability to do commerce based on the agriculture products. Therefore, it is important for these countries to improve their economic condition. In addition to increased modernization and decreasing water levels, most countries also face a decrease in agricultural labor (Sivarethinamohan et al., 2020). This is because most of the youths of the countries do not view agriculture as a viable option for sustenance or growth (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 are carried out by an older population. This poses several problems for the economy. The lack of a younger agricultural labor population makes agriculture a non-sustainable option for economic growth. As mentioned earlier, the lack of agriculture could cause economic disruptions. There is also the fact that the older population is unable to pass on their knowledge to other generations because of the lack of interest (Sivarethinamohan et al., 2020; Tzachor, 2021). Thus, farmers in these countries are less able to take advantage of other areas that produce food or products. If these issues are not solved, further problems may arise such as social unrest or political instability within the populations. This poses a threat to emerging economies that are dependent on agricultural production (Sivarethinamohan et al., 2020).

Problem Statement and Significance

The main problem behind the decrease in agriculture in emerging and low economy countries is the decrease in the significance and popularity of agriculture (Adeleke, 2018). Because of modernization, the younger population in most of the countries do not understand the value of agriculture in their economy. This could be partially attributed to the growth of various industries and their marketing ability (Tzachor, 2021). This has attracted many youths in the countries to ignore farming as a viable option for their economic or social growth. As more and more people revolve and change towards modern fields and industries, they have started occupying more land in the countries. This has resulted in the transformation of valuable agricultural lands into factories, companies and residential areas in most of the countries (Tzachor, 2021).

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

Theoretical Framework

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

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

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

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

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

Researcher’s Positionality

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


The purpose of the study is to learn the opportunities for integrating AI technologies to improve the agricultural production of various emerging countries and countries of lower economy (Araújo et al., 2021). The proposed study uses literary research and document analysis to explore the various methods of AI technology used in global agriculture and understanding the challenges in emulating the same. The relationship between AI-based agricultural framework and the various internal and external factors 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 some important research questions that would help develop a method of AI integration (Araújo et al., 2021).

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

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

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


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

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

Definition of Terms

i. 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 – 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 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. Through the proposed study, the researcher aims at increasing the agricultural production and the economy of emerging and low-economy countries. This is the main goal of the thesis.

Chapter 2: Literature Review

This chapter will explore the field of AI Agriculture and provide insights on the need for further research in the field through an in-depth literature review. The focus of the literature review is to explore the existing literature and highlight the current trends in the development of AI usage for Agriculture and 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 have started to decline because of the diminishing popularity of the agriculture field in the developing nations and its consumers. The literature review will use peer-reviewed literary sources to understand the reason behind the same and the importance of AI agriculture in these developing nations (Beriya, 2020).

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

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

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 by learning how machine learning increases reliability and accuracy (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 that all work cohesively to handle agricultural production. According to Benos (2021), agriculture experts can use artificial neural networks to enhance 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 automate 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 has limited exploration.

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