Increasing AI Agriculture in Emerging Countries and Countries with Low Economy
Submitted by
Sateesh Rongali
A Proposed 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
08-22-2021
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 the possible future use in Agriculture. Particularly, the literature review will be a review of articles that focus on the field of AI agriculture. By discussing the potentials, challenges and limitations in the development of AI in Agriculture, the literature review hopes 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. 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. 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).
The study will use peer-reviewed literature as the main source for the literature review. Peer-reviewed research papers can be divided into two groups: journal articles and research reports. Journal articles are scientific papers that are published in academic journals and have undergone peer review process. The peer review process ensures the scientific validity of the research paper, such as the research question posed by the researcher. Research reports are scientific reports written by the researcher (Garske et al., 2021). These reports are not peer-reviewed before publication, which allows the researcher to freely write about their research without much scrutiny. This chapter will focus on reviewing journal articles from the peer-reviewed research literature. Journal articles from the peer-reviewed literature will be the main source of the literature review. By focusing on peer-reviewed journal articles, the study will ensure that the literature review is valid and that there are no biases.
This chapter will use the literature review for both the knowledge mapping and literature review, 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). The review process of journal articles and research reports on AI use in Agriculture can be divided into two steps. The first step will involve the selection of a specific topic of interest. Next, the review process will be continued by selecting appropriate bibliographic sources which may include peer-reviewed articles, articles, book chapters, or reports. Lastly, all information found from the selected bibliographic sources will be documented (Singh, 2020).
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). Therefore, a review of existing literature that covers these topics will help narrow down the list of potential references and improve the strength of the study. 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. Hence, to conduct a literature review, more information is required, which include the proposed research question, the topic, and the bibliographic sources of the research (Farooq et al., 2020).
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. The need for an understanding of this issue is that this might lead to new ideas in the field of the operation and management of the farms (Farooq et al., 2020).
The literature review will also help verify whether the proposed AI system will help automate the traditional processes of the farming or not. Therefore, the assumption associated with the technology is crucial to be explored. The literature review hopes to identify and define the existing areas of research, gaps, issues, and challenges that are present in the AI agriculture field. This will form the foundation of the research design and help guide the methodology for the research process (Sonaiya, 2019). However, a careful evaluation of the scope of the problem is essential. This will be done through a careful analysis and review of the literary sources that study the existing fields of AI agriculture. This will help create a comprehensive theoretical foundation for the investigation and identification of the problems that are relevant to the selected field of study. To that end, the literature review will investigate several ideas or suggestions regarding the current scope of AI Agriculture, which will later form the basis for the research hypotheses that surround the field (Sonaiya, 2019).
In addition, it will provide a summary of key concepts, definitions, and theories that will be needed for the research study. Following this, the methods of evaluation of AI in the agriculture field will be identified and categorized. Additionally, the current scope of the industry and its development as well as its progress will also be reviewed. This will be done through a comprehensive search of the literature using keywords such as: machine learning, deep learning, autonomous farming, and AI (Sonaiya, 2019). The review will also investigate the technological, financial, legal, and operational constraints faced by the farmers as well as challenges that the industry might face. This will help create the design and evaluation framework that will guide the rest of the investigation process.
Review of Literature
This literature review section will review various peer-reviewed literary sources that are relevant to the scope of the research. It will be a chronological review of the literature, beginning with the works that have the greatest effect on the state of the industry or the technology that has been developed. This review will attempt to provide a general overview of the field of study, the related theories, and concepts, and to identify the various technological developments and methods of investigation (Shokat & Großkinsky, 2019). The current state of the AI and its application in the agriculture industry as well as its progress will be reviewed through a bibliography search. The research hypothesis will be derived from the proposed objectives and the conclusions from the literature review. The literature review will be used to define the research question to be answered by the study, along with providing context, definitions, and terminology. It will also be used to define and evaluate the research design and the data analysis method used in the proposed research (Shokat & Großkinsky, 2019).
Agriculture in Emerging Countries
Agriculture in emerging countries has decreased in importance due to a variety of factors including the increase in urbanization, the decreasing demand for agricultural products, and the shifting global commodity markets. There is a general belief in the research community that in order to meet the demands of the emerging markets, agricultural research must change and be conducted in an entirely different and new manner (Singh, 2020). Many factors contribute to this lack of change, including the belief that agricultural research is simply too difficult to conduct, the lack of agricultural funding in the emerging countries, the difficulty to recruit and retain researchers, and the lack of a research infrastructure. Although agricultural research conducted in developing countries is often aimed at improving the agricultural production systems in developing countries, the findings from this research often provide solutions that will benefit the agriculture in developing countries. There is a general belief that developing countries such as China and India, with their relatively lower per capita income, lower literacy rates and smaller agricultural land base, have less resources to pursue agricultural research (Singh, 2020).
In reality, there is some evidence that indicates that the developing countries, particularly China, have a larger agricultural research sector in comparison to the developed countries. Nevertheless, the magnitude of agricultural research activities and research institutions in emerging countries are relatively less than in the developed countries like the USA and the UK. However, there are some literary sources that assure that both developed countries and developing countries have some type of agricultural research that is improving the agriculture of those countries. This is in stark contrast to the major consensus in the field. Therefore, there is a need for further study of agricultural research in developing countries (Singh, 2020). Specifically, there is a need for a greater emphasis on conducting research on how to improve the agriculture in the developing countries, and an examination of the relative success of those agriculture-related research conducted in the developing countries. This will help identify the major problems hindering the agricultural research in the developing countries and provide a clear understanding of why there is such a disparity in agricultural research in developing countries compared with the developed countries (Singh, 2020).
The majority of agricultural research is conducted in the United States, Australia, the United Kingdom, and other developed countries. While there is some research that is being conducted in these countries, the majority of agricultural research is based on research and development initiatives supported by the United States and developed countries. While this research is not necessarily detrimental to the countries in which it is conducted, the research is often focused on improving the agricultural production systems in developed countries (Farooq et al., 2020). This research and development effort could provide new insights, but may not be sufficient to provide sustainable solutions for agricultural production in emerging countries. In addition, the lack of agricultural research and development in developing countries could be attributable to factors such as inadequate funding, a lack of trained agricultural personnel, the lack of sufficient access to data and technology, and the lack of opportunities to partner with other institutions (Farooq et al., 2020).
On the whole, agriculture in emerging countries seem to be on a decline, and this trend is being observed throughout the world. Although agriculture is still the single largest contributor of gross national product, the majority of agriculture-related research is based on research being conducted in the developed countries. The developed countries spend billions of dollars on research, and that cannot be replicated in developing countries that must rely on locally based research that may not provide more sustainable solutions (Farooq et al., 2020). This warrants continued global effort to identify and develop agricultural production systems in developing countries that are sustainable and economically viable. Thus, a study on the need for AI agriculture and its future potential is a relevant and much needed topic. It will help emerging countries worldwide to develop more sustainable agricultural production systems that ensure food security for people worldwide (Farooq et al., 2020).
Reasons for Low Popularity
There are also several literary sources that describe the factors which have contributed to the decline in the number of agricultural productivity and popularity. Some of the reasons that have been quoted in relevant literary sources are the increase in modernization and the lack of agricultural education among the younger generation (Alreshidi, 2019). Other factors include poor funding, a lack of research infrastructure and the lack of agricultural production. In fact, several developing countries have a lower output per unit of agricultural land. While there is a clear consensus among various authors about these factors being the reason for lack of agricultural production and popularity, the extent of impact of these factors is not explored in-depth (Alreshidi, 2019).
For example, there is a consensus that the decrease in agricultural production and popularity can be attributed to the lack of agricultural education, especially in the case of people from the younger dynamic. While this is true in a way, there isn’t enough literature and research to confirm this with any degree of certainty. In fact, the number of people who are illiterate is increasing while the literacy rate is not commensurate with population growth (Alreshidi, 2019). The education systems in many countries have been affected by a number of challenges. For example, the increase in literacy and the rise of the literacy rate are not in sync with agricultural production. The increasing literacy rate has also not led to a corresponding increase in the number of agricultural producers and entrepreneurs. There is a clear need for further research to determine the extent of the contribution of various agricultural education factors towards declining agricultural production (Alreshidi, 2019).
Another factor which is attributed for the dwindling population in the agricultural sector is the introduction of a number of modern agricultural technologies. For example, the rise in mechanization and the improvement in agricultural research and development are attributed to the rise in production efficiency (Bannerjee et al., 2018). However, it would be helpful to investigate whether there are any empirical or research-based evidence to support these claims. While it is true that the agricultural population in emerging countries is likely to decline due to the introduction of modernization, there is also a growing body of literature to indicate that the agricultural population is growing at an unprecedented pace in less developed countries. However, even if the agriculture population in the developing countries is declining, there is a need to understand why people are choosing to stay in the farming sector (Bannerjee et al., 2018). While the agriculture population is declining, there is also a corresponding decrease in the labor force in the agriculture sector. This could be due to the lack of available jobs in the agricultural sector and/or the changing nature of employment which makes the work force less attractive. More research needs to be done on these issues to determine the key reasons for declining agricultural production.
The question is whether, and to what extent, these factors have also contributed to the lack of agricultural production or research in emerging countries (Bannerjee et al., 2018). This question is based on the assumptions that the developing countries have the potential to increase their agricultural production if the factors that prevent such production, and the agricultural research in emerging countries, could be corrected. The question is also based on the assumption that the agriculture research in emerging countries could be conducted in the same manner as in developed countries. Therefore, it is important to examine the current factors that contribute to the current status of agricultural research in emerging countries (Bannerjee et al., 2018).
In many cases, the emerging countries research that is being conducted is mostly based on developed countries. The reasons behind this are not only the lack of technical, infrastructural, and monetary capacity, but also due to the difference in the environment, culture, and mentality of the countries. In order to understand the reasons that underlie the current status of agricultural research in developing countries, it is necessary to first explore and understand the current status of agricultural research and the potential of agricultural research in developing countries (Bannerjee et al., 2018).
Importance AI Agriculture
Literary sources that explore AI agriculture offer a strong argument to support the research of AI integration in the agriculture field. AI agriculture has gained a lot of interest due to its wide range of potential applications in various fields in the agriculture industry (Eli-Chukwu, 2019). AI agriculture is also gaining popularity as the technology is becoming more advanced, cheaper, and easier to use. In fact, many authors and researchers in the field propose that the impact of the artificial intelligence on the food and agriculture industry is expected to be tremendous. They state that the technology has the potential to change the way farming is done and how food is harvested, and that it can be an advantage to farmers if they can harvest early, as the weather could be favorable for a certain crop and then bad for another crop. This showcases the importance of AI research and development in the agriculture industry and the importance of using AI as a tool to solve problems (Eli-Chukwu, 2019).
There is also a strong consensus among researchers that AI agriculture is highly beneficial for both farmers and societies. One of the biggest uses of AI technology is to improve the efficiency of farming. It is possible that farmers could harvest a crop, store it, and harvest another crop using an AI technology system. These systems could potentially allow farmers to harvest in the middle of the night when the weather is not conducive to doing so. The importance of improving efficiency cannot be emphasized enough (Eli-Chukwu, 2019). A study by the Department of Agriculture (USDA) estimated that in an average farm, a farmer can save around 9 cents when using an improved AI agricultural technology. As the efficiency of farming improves, the cost of food production also decreases. This can provide a sustainable food source and reduce the amount of money required by the farmer to obtain a food source. However, AI technology is still in its infancy, and there are more research, development, and testing needs to be done before more people can use AI technology to improve their food and agriculture (Garske et al., 2021).
Another use of AI technology in agriculture is ‘predictive farming’ that helps determine harvest times and crop use. This can be especially important if there is an environmental concern because a farmer might not want to harvest when the environment is becoming unfriendly. This could provide an opportunity for more efficient and more cost effective farming. This is especially important for the use of crops and the usage of water because the use of the most efficient crops could require less water (Garske et al., 2021). The efficiency of a farm could also be defined by its net income. This would mean that the higher the efficiency of the farm is, the more the net income will be. Since the efficiency of the farm is directly linked to the net income, the farm is more efficient if it is able to attain a greater net income. With predictive farming, a farmer may learn what the soil is able to tolerate and when to plant. It can also determine what the crops are best for its environment at what time of the year (Garske et al., 2021). Therefore, AI technology helps farmers use these tests to optimize the quantity and quality of crops.
An overwhelming amount of literature recommend integrating AI tools and systems like machine learning, IoT and data visualization in order to monitor and control farms as a whole. AI can work with the information gathered in the field to determine what crops need to be grown based on weather, soil, and other environmental factors (Gurumurthy & Bharthur, 2019). Because of the huge amount of data that can be gathered from this information, it is hard to process it by human. AI can process the data, determine what crops need to be grown, and then send instructions to grow the best crops. AI can also use IoT devices and sensors to determine how much of a fertilizer, or other chemicals need to be used to improve the overall health of the soil. This can help the farmer plan for the best possible results (Gurumurthy & Bharthur, 2019).
However, there are also concerns in the mind of researchers and experts in the field about the implementation of said AI systems in the field of agriculture. This is because of the fact that a lot of the AI technology and systems that are used today in the agricultural sector have not been extensively tested in emerging However, there are also concerns in the mind of researchers and experts in the field about the implementation of said AI systems in the field of agriculture (Gurumurthy & Bharthur, 2019). This is because of the fact that a lot of the AI technology and systems that are used today in the agricultural sector have not been extensively tested in emerging economies, like India. While these emerging economies have a lot to gain by utilizing AI systems in their agricultural sector. It is important to do further research and testing of the said AI systems in order to make sure that they are effective and that there are no side effects to the ecosystem. Therefore, several experts and researchers state that the implementation of AI technology in the agricultural sector of emerging countries needs to be done slowly and carefully (Gurumurthy & Bharthur, 2019).
Exploration of Benefits
There is an overwhelming consensus that there are several benefits in implementing AI agriculture systems in the field of agriculture. The economic benefits of using AI systems in the agricultural sector in emerging economies is pretty significant. Many countries in the world, including the USA, are struggling with many issues like unemployment, poverty, increasing food prices, and increasing costs of agriculture as a result of climate change. While other countries like India have a much higher population and need more food (Bannerjee et al., 2018). Therefore, in these emerging economies, the implementation of AI systems in the agricultural sector will significantly help in the improvement of the economy, not to mention the improvement of the overall welfare of the people and agriculture as a whole. The implementation of AI technology in agriculture is going to be the most effective way of overcoming the food shortage and increasing food security issues that are plaguing the world (Bannerjee et al., 2018). This is because a lot of the world’s population is living in rural areas. As a result, the majority of the population do not have access to food that is sufficient and healthy. Therefore, using AI systems in agriculture to help farmers in rural areas produce food in a sustainable way is going to be the best way to solve the growing issues of food shortages and food security in the world (Bannerjee et al., 2018).
Another significant benefit of using AI systems in the agricultural sector is going to be the increased income of the farmers. Because there is a growing need for food in emerging economies, and a lot of the people in these countries are living in rural areas (Beriya, 2020). Therefore, it is pretty obvious that there is a huge need for increasing the productivity of the farmers in these countries. The implementation of AI technology in agriculture, and especially in the agricultural software, can significantly help these farmers to increase their income as well. Because a lot of the farmers that are in rural areas don’t know about any of the agricultural software applications that are available on the market (Beriya, 2020). Therefore, they are going to find it really difficult to benefit from these applications. This can be mitigated with the aid of AI education for farmers in different developing countries. Because these countries will be able to leverage on the available information, resources, and techniques, and use them to increase the productivity of the farmers in these areas. This will, in turn, lead to higher yields of the crops, increase the profit of the farmers, and more food for their families (Beriya, 2020).
However, the implementation of AI systems in agriculture will be able to help the farmers learn about the different types of agricultural applications that are available for them, and the different types of solutions that are needed to increase the production of the farmers in their respective farms (Beriya, 2020). Because, as mentioned earlier, the lack of such applications and solutions is going to be the main reason why farmers in rural areas don’t have access to modern farming technology and solutions. The increasing demand for food in developing economies is also going to have a significant impact on the agricultural sector (Beriya, 2020). As a result, the need for the implementation of technologies in agriculture to increase agricultural productivity is also going to be increasing. The implementation of the technology will be able to help the farmers improve the productivity and sustainability of the agriculture sector, which is going to have a significant impact on their respective economies. Because, after all, an effective agricultural sector will always have a huge impact on the economies of developing countries (Beriya, 2020).
As mentioned earlier, when a majority of people are unemployed and they don’t have enough to eat, it will have a significant impact on the economy of a developing country. The development of AI technologies in agriculture can significantly increase the agricultural productivity of the farmers, which can have a significant impact on the agricultural sector in these developing countries (Garske et al., 2021). Therefore, the development of the AI technology is going to have a big impact on the agriculture sector in these countries. These are some of the major benefits of incorporating AI technologies in the agriculture sector. Therefore, the agriculture sector in developing countries will have the best opportunity to benefit from the implementation of these technologies in their agriculture sector (Garske et al., 2021).
In general, these countries are considered to be developing countries. Therefore, they are considered to be among the most vulnerable countries for the use of AI technologies in the agricultural sector. The main reason for this is that a majority of the agricultural sector in developing countries rely on manpower (Garske et al., 2021). The manual implementation of the farming techniques in these countries is going to have a negative impact on the agriculture sector in these developing countries. This is because in developing countries, the implementation of AI technologies in the agriculture sector will make it a lot easier for the farmers to use them for agriculture than when it is manually done. As a result, the farmers will be able to achieve much more through the implementation of the automation. This is the general consensus among several experts and researchers in the field and it is also outlined in various literary sources (Garske et al., 2021).
The development of the AI will also help the farmers improve their farming techniques. However, the adoption of the technologies will not be easy for these farmers. This is because they are likely to be lacking the technical skills and knowledge of the farming technologies. Therefore, the adoption of the agriculture technologies will be more complicated for the farmers who have limited knowledge and experience in the field (Singh, 2020). In addition, the development of the agriculture technologies also raise some concerns from the users of the technologies. The main reason for this is that the implementation of the technologies in the agriculture sector may be accompanied with some potential disadvantages. Thus, there is a need to conduct a comprehensive study in the agricultural sector in order to address these concerns before the implementation of the technologies (Singh, 2020).
Challenges in Implementation
As mentioned earlier, several literary articles point out that AI implementation in the field of agriculture will not be an easy task for the farmers. There are several reasons for this. Firstly, the technologies will bring about a number of challenges. The main reason for this is that these technologies are completely dependent on machine learning and artificial neural networks. There are several challenges in the usage of these technologies (Sonaiya, 2019). For instance, these technologies are difficult to use for a wide range of tasks in the farming sector. Apart from this, the farmers are expected to make a number of changes in their agriculture sector in order to make the usage of the technology easy. It is expected that the adoption of these technologies will not be easy for these farmers. This is because the machines have high learning requirements (Sonaiya, 2019). The farmers are expected to spend a lot of time to learn the different farming techniques that the AI machines are capable of performing. In addition, the farmers are also expected to change their working environment in order to make the usage of the AI systems and machines easy (Sonaiya, 2019).
Another challenge for implementing AI systems and machines in the agriculture sector is the security issues that might occur during the implementation. The usage of AI systems and machines for the control of the water or for pest eradication may involve with some potential security issues. Due to the fact that these systems are completely dependent on machine learning, the developers are unable to control these machines due to various factors. Some of these factors include the environmental conditions (Sonaiya, 2019). This is because if these systems are to work under a particular environmental condition, then they will learn and adapt accordingly. The developers also cannot identify the reasons why the AI machines are not working as expected. Due to this, the developers cannot make any modifications or changes in order to make the machines working correctly. Due to these reasons, the machine is considered as unsafe or in a wrong place (Sonaiya, 2019).
Some researchers and experts feel that the benefits of using AI agriculture overshadow any and all challenges involved in the implementation of the technology. These experts see the use of AI systems and machines as a key to the future of farming (Shokat & Großkinsky, 2019). This is because if this technology is to be used, then it will help them get the products that they need at a cheaper price. This is because most farmers or growers are willing to spend more on the production of a single product. Therefore, the use of these machines will help them save on their expenses (Shokat & Großkinsky, 2019). This means that they are able to make sure that they get the maximum benefits of their products and also be able to make sure that they maximize their profits. In the long term, this technology will help them with the usage of a single system and machine. With the usage of one machine or one system, they will be able to control multiple or even all farms (Shokat & Großkinsky, 2019).
There are also literary sources that are concerned with the use of AI agriculture in emerging economies like Africa because these places are considered as most deprived with respect to the use of technology. These machines are the solution to many problems and issues. These issues involve human labor and its usage in the production of food (Shokat & Großkinsky, 2019). Therefore, AI agriculture will help these communities and countries overcome these problems that are currently in the pipeline. The lack of respective technology is a significant limitation to the usage of AI agriculture technology in Africa. It is noted that the main focus is on the improvement of the quality of life in these regions and countries. This means that a lot of efforts will be made in the improvement of the lifestyle, work culture, and the society of these countries (Shokat & Großkinsky, 2019). Furthermore, it is noted that agricultural and farm practices are considered as the major sources of poverty and hunger.
Other than this, we can also identify the major and critical challenges related to the use of AI agriculture in emerging countries. First, AI agriculture is mainly applied on the larger scale. This means that they can be applied by farmers or farmers groups (Garske et al., 2021). However, in some countries like Africa, the implementation of the technology is not as broad as in other countries. This is because it is applied in some regions only. However, the extent of the challenges involved in the system is rather theoretical in most cases because most literary sources do not approach the issues from a real-life standpoint (Garske et al., 2021). This limits the ability to identify and eliminate the challenges in the use of AI agriculture. Furthermore, it is noted that the focus is more on a theoretical approach that deals with the general concept rather than an in-depth analysis of the topic. With an in-depth analysis, we can identify the major issues that are related to the use of AI agriculture in emerging countries like Africa (Garske et al., 2021).
Overview
AI agriculture is a relatively new concept that has gained prominence because of its immense benefits to the field of agriculture. Through the literature review, it is clear to see that AI agriculture has become a major part of agricultural research and development in developed countries. This is because it helps address various global challenges that have led to the decline in agriculture (Singh, 2020). Some of these challenges include environmental challenges, natural disasters, labor issues, market related challenges, etc. Furthermore, research has also shown that if this technology is not applied effectively, it would lead to an increase in hunger and poverty in developing countries (Singh, 2020). The development of this technology is also very critical in developing countries where its adoption would create a new age of prosperity in these countries.
In developing countries, agriculture is often the main source of food. Thus, the ability to apply AI to agriculture would create many jobs and opportunities to these countries. This can be achieved through the development and use of different technologies that are based on AI. The use of AI in agriculture is also a major source of job creation. AI is also being used to address the challenges that are being faced by the current system (Singh, 2020). This means that it is using technology to address a problem that is being faced by farmers. By addressing this problem, it ensures a better and sustainable farming system in the developing country. Since this is an important step for the developing country, the adoption of this technology should not be delayed. Due to the rise in the prices of food, the growth of global population and the lack of food supplies, the use of AI in agriculture is the solution to this problem. This is one of the reasons that the research study was created (Singh, 2020).
The literature review also shows that AI and Agriculture can not only reduce the use of harmful inputs in the agriculture but also it can reduce the loss of farmers from the current farming system. Many of these losses are the result of various diseases that affect the farms and result in poor yields. This is because of problems like soil degradation, water loss, weather events, etc. through the development of AI, it can be used to reduce the harmful effects of agriculture (Singh, 2020). Through this it ensures that farmers produce more, have fewer losses, and have a happier life. It is also believed that with the widespread use of AI technology in agriculture, the entire food chain would benefit. These benefits assure that AI agriculture is on a growth curve and will help the global agriculture sector immensely. This overview lays a foundation for understanding how AI agriculture can benefit the agriculture sector in developing countries (Singh, 2020).
In addition, the challenges involved in implementing AI agriculture were also highlighted. Some of these are that the lack of knowledge, time, and cost to adopt the technology in agriculture. It is also important to understand that any AI related advantages must be weighed against the disadvantages (Gurumurthy & Bharthur, 2019). Disadvantages are mainly the cost and the effect on the workers. As the literature review shows, some of the applications of AI in agriculture use machine learning algorithms that are not able to detect problems in the farms. In this case, with the current algorithms, it takes a lot of human labor and effort to help the machines learn. Therefore, although AI helps to reduce the number of workers, it does not replace their labor. Another important factor is the lack of education and training for farmers to be able to incorporate AI in their daily work (Gurumurthy & Bharthur, 2019).
The literature review also highlights that many of the agricultural industries are not interested in adopting the technology because they believe that AI can only benefit wealthy nations. Therefore, this shows that the adoption of the technology in agriculture in developing countries must be encouraged (Gurumurthy & Bharthur, 2019). Furthermore, there are limited reports that have compared the benefits of adopting AI in agriculture with other technologies such as biotechnology. This highlights that there is more research required to evaluate the benefits of adopting AI in agriculture. Finally, the literature review shows that there is the need for government policy to encourage the adoption of AI in agriculture (Gurumurthy & Bharthur, 2019). This encourages the agricultural sectors to adopt AI in their farms and ensure that it benefits all sectors of the agriculture industry.
Gaps in Literature
One significant gap in the existing literature of AI agriculture systems is that there is little to no literature from the perspective of the developing countries. As such, the research study will not only cover the current literature but it will also look at the needs of the farmers in developing countries and suggest potential solutions for them (Gurumurthy & Bharthur, 2019). Through these findings, the research study aims to recommend the best solution for agriculture development in developing countries. The findings that will be presented in the research study will include a thorough overview of the literature related to the topic, needs of farmers in developing countries, and recommendations for the creation of the next generation of AI agriculture systems. It is believed that the research study will provide the needed tools for the development of AI technologies for agriculture and help reduce harmful emissions of farming technologies and improve the welfare of farmers. This will ultimately help agriculture develop faster (Farooq et al., 2020).
Another gap is that most of the literary sources that cover the AI agricultural benefits of emerging countries primarily consider prime models like India where there is significant technology and AI growth (Eli-Chukwu, 2019). Even though these literary sources provide valuable and factual data, they do not cover other regions or countries. This is especially true in the case of countries that are lower on the development spectrum like Africa. Since these countries have low technology or AI integration systems, the impact of AI agriculture in these countries is yet to be clearly understood (Eli-Chukwu, 2019). This warrants the need for a research study that will cover this critical aspect by looking at how the use of AI technologies in developing countries, including Africa. By conducting the study, farmers in those countries can address the challenges of the region and the impact that the proposed solutions can have on agriculture development in these countries.
Furthermore, there is also the fact that the majority of literature is based on theoretical analysis of AI agriculture’s relation with developing countries. While this is significant research in its own right, there needs to be more research that provides empirical evidence from the perspective of developing countries (Eli-Chukwu, 2019). Therefore, this research study on AI agriculture is of high interest to farmers in the region, as it will provide evidence to these farmers on how they can benefit from AI agricultural systems. By using qualitative interviews that are subjective in nature, the research study hopes to provide significant empirical evidence and research studies that specifically covers AI agriculture adoption in developing countries (Eli-Chukwu, 2019). This is of critical importance for AI development in the agricultural sector. This will not only help reduce adoption barriers of new and emerging AI technologies but also ensure that new and emerging AI technologies have the right support mechanisms in place that will help promote its effective and successful adoption. In this regard, this research study can provide a platform for a discussion on how AI agriculture can be promoted by relevant stakeholders (Eli-Chukwu, 2019).
Conclusion
This literature review focuses on understanding the relationship between AI tools/systems and agriculture. The main goal of the literature review is gaining an insight on the existing goal is to gain an understanding of the current research on the topic and identify the main themes within this research area. As the research on AI agriculture is still at the early stages, there are many unexplored topics related to this research (Beriya, 2020). The findings in this literature review showcase the impact of the AI systems in both developing and developed countries. The main goal is to understand the current agricultural environment in developing countries and investigate the issues within the agricultural sector in these countries. In developing countries, the use of AI applications is at an early stage of adoption and development. This means that the use of AI applications has not yet been established in agricultural sector. However, the research literature has shown that farmers in developing countries are already making use of some of the AI tools/systems (Beriya, 2020).
Furthermore, some studies highlight that in order to overcome the challenges associated with the adoption of AI technology, there are some significant barriers within the existing agricultural systems that hinder the adoption of these tools/systems. The main goal of this literature review is to identify the existing technologies and to understand their importance in the adoption of AI technology. In addition, the literature review also looks at the benefits of AI agriculture. Based on the literature review, it is clear that there are several benefits of using AI agriculture like increased agricultural production, improved efficiency, and enhanced food security. The findings in this literature review show that the adoption of AI technology is only feasible when proper policies and regulations are in place and that there is a need to educate the farmers. In addition, the existing literature shows that there is a need for the development of an open environment in order for the application of AI to take place in the agricultural sector (Beriya, 2020).
The challenges involved in implementing and integrating AI systems are also explored in several literary sources by researchers and experts in the field. In the case of developing countries, there are several barriers for AI agriculture integration (Alreshidi, 2019). Some of them include lack of education, lack of technology, lack of funding, and lack of regulatory policies. Furthermore, the adoption of AI systems in developing countries can be challenging as many of these countries are still trying to improve agricultural systems. Literary sources also show that the introduction of a new technology, such as AI, is not feasible without developing a better understanding of the technology (Alreshidi, 2019).
In the case of developing countries, there is a need for identifying the challenges of implementing AI systems so that they can address these challenges. This is because there is little to no literature on the topic from a developing country’s perspective. This is a significant gap in the literature that we are hoping to fill with this research. Another gap is the lack of subjective analysis in the field of AI agriculture. A subjective interview in the field will help to provide a comprehensive and accurate understanding of the challenges faced by developing countries in the implementation of AI in agricultural applications (Alreshidi, 2019). In addition, this paper is one of the first pieces of research that has attempted to understand the importance of developing an open environment for the implementation of AI in the agricultural sector.
References
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