Homework answers / question archive / Increasing AI Agriculture in Emerging Countries and Countries with Low Economy Submitted by Sateesh Rongali A Proposed Study Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Education/Philosophy in Leadership with a specialization in Computer Science Judson University Elgin, Illinois 08-15-2021               Abstract This research study focuses on exploring the field of AI agriculture from an emerging countries’ standpoint

Increasing AI Agriculture in Emerging Countries and Countries with Low Economy Submitted by Sateesh Rongali A Proposed Study Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Education/Philosophy in Leadership with a specialization in Computer Science Judson University Elgin, Illinois 08-15-2021               Abstract This research study focuses on exploring the field of AI agriculture from an emerging countries’ standpoint

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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

08-15-2021

 

 

 

 

 

 

 

Abstract

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

Table of Contents

Chapter 1: Introduction 2

Background 2

Problem Statement and Significance 4

Theoretical Framework 4

Researcher’s Positionality 8

Purpose 8

Research Question(s) 9

Significance 10

Definition of Terms 11

Summary 11

Chapter 3: Introduction 13

Statement of the Problem 14

Research Question(s) 14

Research Methodology 15

Research Design 15

Study Population & Sample Selection 16

Data Collection Methods 17

Sequential Document Selection 18

Qualitative Interview 18

Data Collection Procedures 18

Data Analysis & Procedures 19

Validity & Reliability 20

Ethical Consideration 21

Limitations 22

Summary 23

 

 

 

 

Chapter 1: Introduction

 

Background

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

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

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

 

Problem Statement and Significance

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

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

 

Theoretical Framework Comment by Mellissa Gyimah: This should be centred

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

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

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

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

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

 

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

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

Comment by Mellissa Gyimah: centred

Purpose

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

 

Research Question(s ) Comment by Mellissa Gyimah: centre

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

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

 

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

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

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

 

Significance

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

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

 

Definition of Terms

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

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

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

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

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

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

 

Summary Comment by Mellissa Gyimah: centred

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

 

Chapter 3: Methodology

 

Introduction

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

 

Statement of the Problem

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

Comment by Mellissa Gyimah: centred

Research Question(s)

The proposed study will focus on addressing the following critical questions

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

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

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

 

Research Methodology Comment by Mellissa Gyimah: centred

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

 

Research Design Comment by Mellissa Gyimah: centred

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

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

 

Study Population and& 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. 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. 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. Comment by Mellissa Gyimah: Good…but how? Who will you ask? From where? Does it matter what they do exactly within the agricultural domain?

 

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. 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. Comment by Mellissa Gyimah: How many people will be interviewed? Why? Do you have interview questions you will use?

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 factual 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. One of the purpose 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). Comment by Mellissa Gyimah: This term should probably be explained

 

Validity and& Reliability Comment by Mellissa Gyimah: centred

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). Comment by Mellissa Gyimah: Good!

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. 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). Comment by Mellissa Gyimah: They can also be seen as highly subjective so you may want inter-rater agreement to help with reliability

 

Ethical Considerations Comment by Mellissa Gyimah: centred

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). 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). Comment by Mellissa Gyimah: how will you ensure this?

 

Limitations Comment by Mellissa Gyimah: centred

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).

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 Comment by Mellissa Gyimah: centred

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 sequential 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. Snowball sampling could also create a sampling bias, which might become a major limitation. Comment by Mellissa Gyimah: this is the first time I am seeing you use this language Comment by Mellissa Gyimah: how and why? Expound. This should probably be in the limitations section, however.