Influenced by several stages of the industrial revolution, the contemporary world is particularly reliant on information technologies and artificial intelligence manifested through automated and computerized processes. Different sectors of the economy rely on artificial intelligence to varying extents. Agriculture is one of the critical economic sectors, the sustainable and stable development of which implies the world economy’s longevity and food security for the population globally (Bondre & Mahagaonkar, 2019; PatrĂcio & Rieder, 2018). Moreover, artificial intelligence allows for conducting precision crop growing, planning for production, finding solutions for mitigating risk factors for harvesting, as well as finding alternative methods for achieving higher efficiency, productivity, and quality (Ruiz-Real et al., 2020). With the emergence of the Internet of Things (IoT), big data are generated rapidly, which allows for analyzing the indicators essential for monitoring the food production processes (Misra et al., 2020). Thus, the use of artificial intelligence in the agricultural sector of the economy is essential, which validates the need for researching this sphere in-depth.
While the necessity of technologization of agriculture is evident, the effectiveness of using artificial intelligence in this field requires clarification. Indeed, agriculture encounters a series of obstacles to efficient and qualitative production, including “disease and pest infestation, improper soil treatment, inadequate drainage and irrigation,” and others (Eli-Chukwu, 2019, p. 4377). Thus, given the multitude and diversity of challenges, it is essential to identify whether artificial intelligence is capable of coping with the constraining factors impeding the productivity and efficiency of agriculture.
Thus, the research question of the proposed study is as follows: how effective is the application of artificial intelligence to agriculture in terms of removing inefficiency and the lack of productivity? To answer this research question, the study will aim at conducting a qualitative inquiry to collect and analyze data from primary sources. Overall, it is anticipated that the study will provide scholars and specialists in the agricultural sphere with relevant and valuable evidence on the problematic areas of artificial intelligence’s implementation. In addition, the study will generate possible recommendations on how to improve the use of information technologies to benefit sustainable food production.
Background and Significance
Much research has been conducted to identify the relevance of artificial intelligence for the needs of the agricultural sphere and validate the benefits it yields. The question about the effectiveness of artificial intelligence’s usefulness for the agricultural sector stems from the debate about the relevance of implementing technologies in this sphere. On the one hand, with the expansion of information technologies and innovative solutions in other spheres, the use of such technologies as drones, computer imaging, big data analysis, and others is anticipated to minimize the hazards impacting agriculture.
Several studies address the positive outcomes of technological implementation on the background of environmentally-caused challenges the society faces today. In particular, according to Özkan and Dilay (2018), “changes and uncertainties in weather conditions, yields, prices, government policies, and global markets are risk factors for agriculture” (p. 2953). The exposure to such risks results in “uncertainties that can lead to large fluctuations in agricultural production and farm incomes” (Özkan and Dilay, 2018, p. 2953). For that matter, the facilitation of minimizing these risk factors by means of innovation and technologies is reasonable. Indeed, the loss of crops due to the drawbacks in risk prediction and mitigation, ineffective soil and disease management, and other problematic issues make a case for using artificial intelligence in agriculture possible (Bannerjee et al., 2018). From this perspective, the integration of artificial intelligence into the context of agriculture and farming, both animal and crops, provides significant benefits and is anticipated to yield effective results.
However, there are particularities in artificial intelligence development and implementation in the farming sector, which represent the opposing side of the debate about the usefulness and worth of introducing such solutions to the sector. Thus, multiple studies have been conducted to identify the risks that artificial intelligence contributes to the sphere of agriculture. Indeed, as stated by several scholars, the use of technological solutions introduces new challenges, such as the commercialization of technologies, teaching, research, and resource expenses (Misra et al., 2020; Özkan & Dilay, 2018). In particular, systemic risks of artificial intelligence are associated with “a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions; and d) trade-offs between efficiency and resilience” (Galaz et al., 2021, p. 4).
Moreover, there are “risks society faces when AI is practiced without proper care, lack of responsibility, poor assessment of bias, and a complete disregard for ethically aligned designs” (Bergsten & Rivas, 2019, p. 1). Thus, such challenges associated with technological solutions complicate the obtainment of effective results in agriculture and should be considered a significant factor for the proposed measurement.
Goralski and Tan (2020), Idoje et al. (2021), Spanaki et al. (2021), Sparrow et al. (2021), and others focus on the risks and challenges in terms of governance and policies of artificial intelligence implementation. Given the character of the reviewed scholarly literature, one might identify that there is a research gap in the explicit measurement of the effectiveness of using artificial intelligence in agriculture. The existing literature predominantly focuses on either the benefits or risks associated with informational technologies in the agricultural business without evaluating the ratio of the contribution of both in the same study. For that matter, the proposed research study will attempt to fill this gap by correlating advantages and disadvantages to measure the effectiveness of technological solutions for the selected sector. It is anticipated to inform the drawbacks in the sphere of cyber security and information technologies and equip decision-makers with necessary data for improvement.
Methodology
To conduct the proposed study, the researcher will use a qualitative methodology, which is validated by the nature of the research question related to measuring the effectiveness of the use of artificial intelligence in agriculture. Yordanova and Curt (2018) state that qualitative methodologies allow for assessing risks and challenges and investigating the cause-and-effect relationship between different variables. The representatives of small, medium-sized, and big agricultural organizations will be recruited for the purposes of this study. The sample will be selected using non-randomized methods of purposeful sampling to enroll the individuals that possess relevant qualifications and credibility to inform the study.
To collect the qualitative data for the study, the researcher will use the method of the survey with experts representing small-, medium, and large-size agricultural companies. To narrow the scope of research, the crop farming companies will be contacted to recruit the participants, namely the representatives of the organizations. The selection of the survey method is validated by its accuracy, convenience, and efficiency (Sroka & Żmija, 2021). The method of data analysis will be a thematic analysis of the survey responses to measure the experts’ perception of the effectiveness of artificial intelligence’s use in their organizations. The responses of the participants to the questions of the survey will be categorized by themes, namely, the risks, their threat to company production, the benefits of artificial intelligence, and the impact of technologies on the efficiency of farming. The themes will be compared to identify the strength of their influence in the researched organizations. Since the inquiry will be based on survey results conducted specifically for the study, the use of primary sources of data will be prioritized.
Preliminary Suppositions
It is anticipated that the proposed study’s results will indicate the strength of artificial intelligence as a tool for the efficient and sustainable future of agriculture. In particular, given the research literature context, the proposed study is likely to identify particular risks and challenges faced by farming organizations (Basso & Antle, 2020; Bergsten & Rivas, 2019; Bhakar et al., 2021; Bondre & Mahagaonkar, 2019, Waaswa et al. 2022). With the assessment of the scope of these challenges on the background of the benefits provided by artificial intelligence, the study will answer the research question on the level of technological effectiveness in agriculture. Moreover, it will provide a theoretical basis and recommendations for solving the problems associated with the complications caused by the implementation of artificial intelligence in crop production (Suresh et al., 2021; Smith, 2018). In general, the findings and implications of the proposed research are anticipated to have a positive impact on the advancement of green farming and sustainable development goals.
Conclusion
In summation, the purpose of this research proposal was to outline the context and background of the research topic and justify the problem. In particular, under the circumstances of the advancement of artificial intelligence use in a broad range of spheres of economy, the integration of information technologies into agriculture becomes appealing. With the intensification of farming challenges, namely seasonality, soil management, crop loss, diseases, and management issues, the usefulness of big data analytics and technological automated solutions are addressed by scholars as an effective remedy. However, the implementation of artificial intelligence requires additional resources, talent, education, and preparation, as well as contributing new risks to the agricultural sector. Therefore, it is essential to research the effectiveness of using artificial intelligence in agriculture, including farming and food production. The evaluation of risks and benefits is anticipated to unfold the value of technologies in the agricultural setting, indicate potential drawbacks, advantages, and solutions, and inform recommendations for decision-makers.
The proposed study will be conducted using qualitative methodology and relying on primary data sources. The main research question for the proposed qualitative inquiry is as follows. How effective is the application of artificial intelligence to agriculture in terms of removing inefficiency and the lack of productivity? To answer this question, a survey will be conducted with the representatives of small, medium-sized, and big agricultural companies working in the crop farming sector as a data collecting method. The collected data will be analyzed using thematic analysis, which will allow for determining the level of effectiveness of artificial intelligence in resolving the issues faced by the companies. It is anticipated that the results of the study will answer the research question and inform recommendations for agricultural companies and governmental bodies in order to address the drawbacks in artificial intelligence use that are likely to be detected. Moreover, the study findings are expected to contribute to the current body of scholarly literature on the topic and facilitate the implementation of green economy and sustainable development goals.
Annotated Bibliography
Bannerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1-6.
Using the method of literature survey, Bannerjee et al. (2018) investigated how the learning capabilities of artificial intelligence help bridge the gap in crop production and agricultural challenges addressing. The scholars found that the use of multilayered crop management systems using technologies allows for reducing crop losses and facilitates harvesting. The reliability and credibility of the source make it a valuable evidential contribution to the proposed study’s theoretical background.
Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 3(4), 254–256.
This article is a scholarly commentary on the prominence of technological solutions in enabling farming productivity to increase as a way of improving the world’s ability to feed itself. Basso and Antle (2020) claim that regardless of the advancement of technologies, it is essential to ensure continuous research and education to sustain the benefits and progress. The article will allow for the creation of a reliable theoretical and evidential context for the future research study by providing data showing the limited opportunities of artificial intelligence’s agriculture facilitation without proper implementation measures.
Bergsten, S., & Rivas, P. (2019). Societal benefits and risks of artificial intelligence: A succinct survey. In 21st International Conference on Artificial Intelligence, 1-4, Web.
This source is an article from a conference proceeding that highlights the particularities of the benefits and risks associated with the advancement of artificial intelligence in several pivotal spheres of human life in the modern world. Bergsten and Rivas (2019) state that for technologies to maximize their effectiveness, they must be used ethically, properly, and sustainably. The insights obtained from this article are supported by scholarly research and will inform the proposed study’s justification of the need to conduct the investigation.
Bhakar, V., Kaur, K., & Singh, H. (2021). Analyzing the environmental burden of an aquaponics system using LCA. Procedia CIRP, 98, 223-228.
Bhakar et al.’s (2021) study was conducted using a life cycle assessment methodology to identify the benefits and drawbacks of the implementation of aquaponics. Although the researched method of farming facilitation is not particularly based on artificial intelligence, the article illustrates the investigation process of the correlation between agriculture-specific effectiveness results and the challenges. In particular, the study will be used to identify the resource-based approach for the measurement of artificial intelligence use in farming.
Bondre, D. A., & Mahagaonkar, S. (2019). Prediction of crop yield and fertilizer recommendation using machine learning algorithms. International Journal of Engineering Applied Sciences and Technology, 4(5), 371-376.
Bondre and Mahagaonkar’s (2019) research study was aimed at testing how the use of machine learning might be useful for predicting crop yield in farming. Based on quantitative data, the study informs that the prediction of yield might be facilitated by information technologies’ application, which enhances the planning and implementation of business processes. The source is reliable and credible due to its first-hand evidence and scholarly origin. It will be used as a valuable information source providing the context for conducting the proposed study and claiming the importance of improving artificial intelligence implementation routes for crop yield facilitation.
Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377-4383.
The research article by Eli-Chukwu (2019) is a review of scholarly literature with the aim of analyzing the strengths and weaknesses of several techniques of using artificial intelligence for crop, weed, soil, and disease management. The study found that despite drawbacks in the implementation, artificial intelligence’s flexibility is a critically important feature that is likely to enable the increase of agriculture by 70% by 2050 (Eli-Chukwu, 2019). The source is reliable due to its evidential basis and scholarly grounds. It will contribute to the background data of the proposed study and will help claim the necessity of using artificial intelligence in agriculture.
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia. D., McPhearson, T., Jimenez, D., King, B., Larcey, P., & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 1–10.
The research article by Galaz et al. (2021) was conducted as an analytical statistics overview of the rationale for using artificial intelligence in agriculture and the systemic risks associated with this process. The scholars found that despite significant contributions to the solving of environmental and sustainability issues, technologies might be characterized by bias and uncertainties. In such a manner, this credible research study provides a valuable piece of information for the opposing side of the debate referring to the hindrance of technologies’ effectiveness in agriculture.
Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.
This case study was devoted to the establishment of the relationship between the use of artificial intelligence in agriculture and other economic spheres with sustainable development. The study found that the use of artificial intelligence allows for achieving sustainable development goals more efficiently, although with some risks for the systematic implementation of technologies. The study will be a reliable and useful source of information for the proposed study, given its evidence and relevance to the investigated topic.
Idoje, G., Dagiuklas, T., & Iqbal, M. (2021). Survey for smart farming technologies: Challenges and issues. Computers & Electrical Engineering, 92, 107104.
The article by Idoje et al. (2021) is a survey aimed at investigating the gaps in research on the implementation of IoT and artificial intelligence in agriculture. While highlighting the usefulness of the technological solutions in particular examples, the scholars found that there are significant weaknesses in a smart agriculture that should be addressed to make this sector more efficient. The source will be used as a foundation for justifying the need for conducting the proposed research.
Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data, and artificial intelligence in the agriculture and food industry. IEEE Internet of Things Journal, 1-19. Web.
Misra et al.’s (2020) research article was devoted to the investigation of using IoT in the agriculture and food sector to identify the disruptive impact of big data on the technologization of agriculture. In particular, this systematic review unfolds significant implementation and commercialization challenges for artificial intelligence use in the agricultural sector. The internal validity of the source and its voluminous data represent the perspective of challenges presented by artificial intelligence. It will be used as a demonstration of the debate’s sides in order to exemplify the issues that jeopardize the effectiveness of technologies in the food and agriculture industry.
Ă–zkan, A., & Dilay, Y. (2019). Risks in the agricultural sector. Journal of Multidisciplinary Engineering Science Studies, 5(12), 2953-2955.
This source is an exploratory study conducted by Turkish scholars to identify the most prevalent risk factors that the agricultural sector faces. A multitude of factors, ranging from financial, labor, chemical, and managerial to legal and seasonality-based issues, were detected. Although the article is not an original research study, the findings Ă–zkan and Dilay (2018) introduce allow for setting the proposed research in a relevant context. Therefore, this article will be used for the research study to identify the risks that the implementation of artificial intelligence might mitigate and those it might contribute to.
PatrĂcio, D. I., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69-81.
The unlike many other sources used for the research, this article focuses on specific interventions related to artificial intelligence, which allows for identifying the particularities of the effects of technologies on agriculture. PatrĂcio and Rieder (2018) explored the impact of computer imaging use on crop production; the study found that this methodology is particularly effective for precision agriculture. The source is reliable and credible; it matches the topic of the proposed research study and unfolds the scope of artificial intelligence implementation.
Ruiz-Real, J. L., Uribe-Toril, J., Torres Arriaza, J. A., & de Pablo Valenciano, J. (2020). A look at the past, present, and future research trends of artificial intelligence in agriculture. Agronomy, 10(11), 1-16.
This source is a systematic review study that investigated the prevalence of researched issues in relation to artificial intelligence in the context of agriculture. Ruiz-Real et al. (2020) found that multiple authors devoted their work to establishing the connection between a green economy, sustainability, and the use of information technologies. The article will be a useful source of information for constructing a context and background for the proposed research study since it allows for establishing a positive relationship between sustainable agriculture and artificial intelligence use.
Smith, M. J. (2018). Getting value from artificial intelligence in agriculture. Animal Production Science, 60(1), 46-54.
The research study conducted by Smith (2018) is an analytical discussion of the value behind the use of artificial intelligence in the farming and food industry. The study found that the use of technologies in agriculture allows for generating monetary and social value by cultivating higher productivity and a sustainable economy. Given the relevance of the study to the research question and the sample of agricultural businesses, the article will be an insightful source of evidence on the importance of artificial intelligence as a business-boosting factor in the contemporary highly competitive world.
Spanaki, K., Karafili, E., Sivarajah, U., Despoudi, S., & Irani, Z. (2021). Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations. Production Planning & Control, 1-19.
Spanaki et al. (2021) used the design science research methodology to measure how specific techniques based on artificial intelligence might help in predicting, managing, and monitoring the issues with soil in AgriFood production. The article is a reliable scholarly source of credible data on the researched topic. It will inform the examples of positive implementation of artificial intelligence in agriculture through challenges addressing and production facilitation.
Sparrow, R., Howard, M., & Degeling, C. (2021). Managing the risks of artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 93(1), 172-196.
This study exemplifies the risks posed by artificial intelligence and the ways they might be mitigated to maximize the benefits of automation and the computerization of farming. The study was based on an exploratory review to highlight the ways to trade off the risks and challenges. Despite lacking a scientific basis and being grounded on secondary data, the article informs the research gap for the proposed study. Thus, it will be used to illustrate that there is a need to measure the effectiveness of artificial intelligence’s implementation in the agricultural sector, given its weaknesses and strengths.
Sroka, W., & Żmija, D. (2021). Farming systems changes in the urban shadow: A mixed approach based on statistical analysis and expert surveys. Agriculture, 11(5), 455.
This source is a scholarly research study exemplifying the use of surveys as a methodology in the agricultural setting. The source provides a piece of insightful information on the theoretical and practical implications of the survey method as a data-collecting tool in research. Sroka and Żmija (2021) demonstrate the mechanism of using qualitative data in research related to farming. This valuable source of information will help structure, plan, and conduct the proposed research using the appropriate methods.
Suresh, G., Kumar, A. S., Lekashri, S., & Manikandan, R. (2021). Efficient crop yield recommendation system using machine learning for digital farming. International Journal of Modern Agriculture, 10(1), 906-914.
Surech et al.’s (2021) article is a proposal for implementing machine learning-based frameworks for analyzing soil quality and generating options for crop production on those soils based on the relevance criteria. The relevance of this source to the research proposal is validated by the justification of the usefulness of big data analytics and artificial intelligence for agribusiness. The source will provide reliable and credible information about the ways of using artificial intelligence efficiently.
Waaswa, A., Oywaya Nkurumwa, A., Mwangi Kibe, A., & Ngeno Kipkemoi, J. (2022). Climate-Smart agriculture and potato production in Kenya: Review of the determinants of practice. Climate and Development, 14(1), 75-90.
This research study was conducted by Waaswa et al. (2022) using a systematic review method to identify the effectiveness of using climate-based technologies in potato growing in Kenya. The study’s strength is its specific focus on the climate as a dominant factor in the agricultural business in countries with unfavorable environments for farming. The focus of the study on climate and technologies that integrate this factor in planning and production will help integrate its findings into the research context of the proposed study.
Yordanova, R. P., & Curt, C. (2018). Towards a systematic qualitative methodology for multi-hazards risk representation and preliminary assessment. 10èmes Journées Fiabilité des Matériaux et des Structures, 1-12. Web.
This article is a methodological study that justifies the use of systematic qualitative methods to investigate cause and effect in multiple research settings. The scholars claim that qualitative inquiries help identify the relationship between variables, which validates their use for investigating the challenges and benefits of a phenomenon. Yordanova and Curt’s (2018) article will be used as a methodological reference for the proposed research study.
References
Bannerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1-6.
Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 3(4), 254-256.
Bergsten, S., & Rivas, P. (2019). Societal benefits and risks of artificial intelligence: A succinct survey. In 21st International Conference on Artificial Intelligence, 1-4, Web.
Bhakar, V., Kaur, K., & Singh, H. (2021). Analyzing the environmental burden of an aquaponics system using LCA. Procedia CIRP, 98, 223-228.
Bondre, D. A., & Mahagaonkar, S. (2019). Prediction of crop yield and fertilizer recommendation using machine learning algorithms. International Journal of Engineering Applied Sciences and Technology, 4(5), 371-376.
Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377-4383.
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia. D., McPhearson, T., Jimenez, D., King, B., Larcey, P., & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 1-10.
Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.
Idoje, G., Dagiuklas, T., & Iqbal, M. (2021). Survey for smart farming technologies: Challenges and issues. Computers & Electrical Engineering, 92, 107104.
Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data and artificial intelligence in agriculture and food industry.IEEE Internet of Things Journal, 1-19. Web.
Ă–zkan, A., & Dilay, Y. (2019). Risks in agricultural sector. Journal of Multidisciplinary Engineering Science Studies, 5(12), 2953-2955.
PatrĂcio, D. I., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69-81.
Ruiz-Real, J. L., Uribe-Toril, J., Torres Arriaza, J. A., & de Pablo Valenciano, J. (2020). A look at the past, present and future research trends of artificial intelligence in agriculture. Agronomy, 10(11), 1-16.
Smith, M. J. (2018). Getting value from artificial intelligence in agriculture. Animal Production Science, 60(1), 46-54.
Spanaki, K., Karafili, E., Sivarajah, U., Despoudi, S., & Irani, Z. (2021). Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations. Production Planning & Control, 1-19.
Sparrow, R., Howard, M., & Degeling, C. (2021). Managing the risks of artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 93(1), 172-196.
Sroka, W., & Żmija, D. (2021). Farming systems changes in the urban shadow: A mixed approach based on statistical analysis and expert surveys. Agriculture, 11(5), 455.
Suresh, G., Kumar, A. S., Lekashri, S., & Manikandan, R. (2021). Efficient crop yield recommendation system using machine learning for digital farming. International Journal of Modern Agriculture, 10(1), 906-914.
Waaswa, A., Oywaya Nkurumwa, A., Mwangi Kibe, A., & Ngeno Kipkemoi, J. (2022). Climate-smart agriculture and potato production in Kenya: Review of the determinants of practice. Climate and Development, 14(1), 75-90.
Yordanova, R. P., & Curt, C. (2018). Towards a systematic qualitative methodology for multi-hazards risk representation and preliminary assessment.10èmes Journées Fiabilité des Matériaux et des Structures, 1-12. Web.