Systematic literature review on the application of precision agriculture using artificial intelligence by small-scale farmers in Africa and its societal impact
Vol 8, Issue 13, 2024
VIEWS - 56 (Abstract) 44 (PDF)
Abstract
The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.
Keywords
Full Text:
PDFReferences
Abbott, P., Checco, A., and Polese, D. 2021. Smart Farming in Sub-Saharan Africa: Challenges and Opportunities. Sensornets, Article ID: 159-164.
Abioye, E. A., Hensel, O., Esau, T. J., Elijah, O., Abidin, M. S. Z., Ayobami, A. S., Yerima, O., and Nasirahmadi, A. 2022. Precision irrigation management using machine learning and digital farming solutions. AgriEngineering, 4 (1): 70-103.
Agboka, K. M., Tonnang, H. E., Abdel-Rahman, E. M., Odindi, J., Mutanga, O., and Niassy, S. 2022. Data-driven artificial intelligence (AI) algorithms for modelling potential maize yield under maize–legume farming systems in East Africa. Agronomy, 12 (12): 3085.
Agrawal, A.V., Magulur, L.P., Priya, S.G., Kaur, A., Singh, G. and Boopathi, S., 2023. Smart Precision Agriculture Using IoT and WSN. In Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies (pp. 524-541). IGI Global.
Ahmad, L., and Nabi, F. 2021. Agriculture 5.0: Artificial intelligence, IoT and machine learning. CRC Press.
Akansah, E., Senoo, E.E.K., Mendonça, I. and Aritsugi, M., 2022, November. Smart agricultural monitoring system: a practical design approach. In Proceedings of the 12th International Conference on the Internet of Things (pp. 139-142).
Anwana, E.O., and Aroba, O.J. 2022. African women entrepreneurs and COVID-19: Towards achieving the African Union Agenda 2063. HTS Teologiese Studies/Theological Studies, 78(2).
Aroba, O.J., 2023. An ERP SAP implementation case study of the South African Small Medium Enterprise sectors. International Journal of Computing Sciences Research; Vol. 7, Issue 2023.
Aroba, O.J., 2024. Professional Leadership Investigation in Big Data and Computer-Mediated Communication in Relation to the 11th Sustainable Development Goals (SDG) Global Blueprint. International Journal of Computing Sciences Research, 8, pp.2592-2611.
Aroba, O.J., Naicker, N., Adeliyi, T.T., and Ogunsakin, R.E. 2020. Meta-analysis of heuristic approaches for optimizing node localization and energy efficiency in wireless sensor networks. International Journal of Engineering and Advanced Technology; Vol. 10, Issue 1.
Bala, J. A., Olaniyi, O. M., Folorunso, T. A., and Daniya, E. 2021. An IoT-based autonomous robot system for maize precision agriculture operations in sub-Saharan Africa. In: Emergence of Cyber Physical System and IoT in Smart Automation and Robotics: Computer Engineering in Automation. Springer, 69-82.
Balatsouras, C.P., Karras, A., Karras, C., Karydis, I. and Sioutas, S., 2023. WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications. Sensors, 23(23), p.9486.
Charania, I., and Li, X. 2020. Smart farming: Agriculture’s shift from a labor-intensive to technology-native industry. Internet of Things, 9: 100142.
Chougule, M. A., and Mashalkar, A. S. 2022. A comprehensive review of agriculture irrigation using artificial intelligence for crop production. Computational Intelligence in Manufacturing, Article ID: 187-200.
De Abreu, C., and van Deventer, J. P. 2022. The application of artificial intelligence (AI) and internet of things (IoT) in agriculture: a systematic literature review. In: Proceedings of Southern African Conference for Artificial Intelligence Research. Springer, 32-46.
de Melo, D.A., Silva, P.C., da Costa, A.R., Delmond, J.G., Ferreira, A.F.A., de Souza, J.A., de Oliveira-Júnior, J.F., da Silva, J.L.B., da Rosa Ferraz Jardim, A.M., Giongo, P.R. and Ferreira, M.B., 2023. Development and Automation of a Photovoltaic-Powered Soil Moisture Sensor for Water Management. Hydrology, 10(8), p.166.
Dibal, P., Onwuka, E., Suleiman, Z., Salihu, B., Nwankwo, E., and Okoh, S. 2022. An Overview of IoT Solutions in Climate Smart Agriculture for Food Security in Sub Saharan Africa: Challenges and Prospects. EAI Endorsed Transactions on Internet of Things, 8 (3): e1-e1.
Foster, L., Szilagyi, K., Wairegi, A., Oguamanam, C., and de Beer, J. 2023. Smart farming and artificial intelligence in East Africa: Addressing indigeneity, plants, and gender. Smart Agricultural Technology, 3: 100132.
Gardezi, M., Adereti, D. T., Stock, R., and Ogunyiola, A. 2022. In pursuit of responsible innovation for precision agriculture technologies. Journal of Responsible Innovation, 9 (2): 224-247.
Georgopoulos, V. P., Gkikas, D. C., and Theodorou, J. A. 2023. Factors influencing the adoption of artificial intelligence technologies in agriculture, livestock farming and aquaculture: A systematic literature review Using PRISMA 2020. Sustainability, 15 (23): 16385.
Goagoses, N., and Koglin, U. 2020, The role of social goals in academic success: Recounting the process of conducting a systematic review, Springer, Wiesbaden. 1-8.
Gobezie, T. B., and Biswas, A. 2023. The need for streamlining precision agriculture data in Africa. Precision Agriculture, 24 (1): 375-383.
Gokool, S., Mahomed, M., Kunz, R., Clulow, A., Sibanda, M., Naiken, V., Chetty, K., and Mabhaudhi, T. 2023. Crop monitoring in smallholder farms using unmanned aerial vehicles to facilitate precision agriculture practices: A scoping review and bibliometric analysis. Sustainability, 15 (4): 3557.
Gumbi, N., Gumbi, L. and Twinomurinzi, H., 2023. Towards sustainable digital agriculture for smallholder farmers: A systematic literature review. Sustainability, 15(16), p.12530.
Gwagwa, A., Kazim, E., Kachidza, P., Hilliard, A., Siminyu, K., Smith, M., and Shawe-Taylor, J. 2021. Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. Patterns, 2 (12).
Ianni, J.D., and Gorrell, Z., 2015. Treemap visualization for space situational awareness. In Advanced Maui Optical and Space Surveillance Technologies Conference (p. 71).
Kombat, R., Sarfatti, P., and Fatunbi, O. A. 2021. A review of climate-smart agriculture technology adoption by farming households in sub-Saharan Africa. Sustainability, 13 (21): 12130.
Kumar, P., Singh, A., Rajput, V. D., Yadav, A. K. S., Kumar, P., Singh, A. K., and Minkina, T. 2022. Role of artificial intelligence, sensor technology, big data in agriculture: Next-generation farming. In: Bioinformatics in Agriculture. Elsevier, 625-639.
Lakshmi, V., and Corbett, J. 2020. How artificial intelligence improves agricultural productivity and sustainability: A global thematic analysis. AIS e-library.Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020.Page 5202 to 5211
Ly, R. 2021. Machine learning challenges and opportunities in the African agricultural sector--A general perspective. arXiv preprint, 1-13, https://doi.org/10.48550/arXiv.2107.05101
Mendonca Dos Santos, I., Chong Vun Yiing, A. and Aritsugi, M., 2023, November. Not Seeing is a Flower: Experiences and Lessons Learned from Making IoT Platforms for Small-Scale Farms in Japan. In Proceedings of the 13th International Conference on the Internet of Things (pp. 146-149).
Mizik, T. 2023. How can precision farming work on a small scale? A systematic literature review. Precision Agriculture, 24 (1): 384-406.
Mohr, S., and Kühl, R. 2021. Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22 (6): 1816-1844.
Mtshali, L. and Akinola, A. O. 2021. Small-scale farming, fourth industrial revolution and the quest for agriculture development. The new political economy of land reform in South Africa, Article ID: 161-177.
Munghemezulu, C., Mashaba-Munghemezulu, Z., Ratshiedana, P. E., Economon, E., Chirima, G. and Sibanda, S. 2023. Unmanned aerial vehicle (UAV) and spectral datasets in South Africa for precision agriculture. Data, 8 (6): 98.
Obasi, S.N., Aa, T.V., Obasi, C.C., Jokthan, G.E., Adjei, E.A. and Keyagha, E.R., 2024. Harnessing artificial intelligence for sustainable agriculture: A comprehensive review of African applications in spatial analysis and pprecision agriculture.Big Data in Agriculture, 6(1).6-18pp. Doi: http://doi.org/10.26480/bda.01.2024.06.18 .
Ofori, M. and El-Gayar, O. 2021. Drivers and challenges of precision agriculture: a social media perspective. Precision Agriculture, 22 (3): 1019-1044.
Onyango, C. M., Nyaga, J. M., Wetterlind, J., Söderström, M., and Piikki, K. 2021. Precision agriculture for resource use efficiency in smallholder farming systems in sub-Saharan Africa: A systematic review. Sustainability, 13 (3): 1158.
Pandey, N., Kamboj, N., Sharma, A.K. and Kumar, A., 2022. An Overview of Recent Advancements in the Irrigation, Fertilization, and Technological Revolutions of Agriculture. Environmental Pollution and Natural Resource Management, pp.167-184.
Payero, J.O., 2024. An Effective and Affordable Internet of Things (IoT) Scale System to Measure Crop Water Use. AgriEngineering, 6(1), pp.823-840.
Sampene, A. K., Agyeman, F. O., Robert, B., and Wiredu, J. 2022. Artificial intelligence as a path way to Africa ’s transformations. Artificial Intelligence, 9 (1).
Sandilya, D., Bharali, C., Ringku, A. and Sharma, B., 2022, April. Utilizing Greenhouse Technology Towards Sustainable Agriculture Using IoT “TechFarm”. In International Conference on Emerging Global Trends in Engineering and Technology (pp. 373-382). Singapore: Springer Nature Singapore.
Senoo, E.E.K., Anggraini, L., Kumi, J.A., Luna, B.K., Akansah, E., Sulyman, H.A., Mendonça, I. and Aritsugi, M., 2024. IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities. Electronics, 13(10), p.1894.
Sharma, A., Jain, A., Gupta, P., and Chowdary, V. 2020. Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9: 4843-4873.
Siregar, R. R. A., Seminar, K. B., Wahjuni, S., and Santosa, E. 2022. Vertical farming perspectives in support of precision agriculture using artificial intelligence: A review. Computers, 11 (9): 135.
Sparrow, R., Howard, M., and Degeling, C. 2021. Managing the risks of artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 93 (1): 172-196.
Talaviya, T., Shah, D., Patel, N., Yagnik, H., and Shah, M. 2020. Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4: 58-73.
Twetwa-Dube, S., and Oki, O. A. 2023. Technology adoption for precision agriculture in Africa: A bibliometric analysis. In: Proceedings of 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 1-7.
Tzachor, A., Devare, M., King, B., Avin, S., and Ó hÉigeartaigh, S. 2022. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nature Machine Intelligence, 4 (2): 104-109.
DOI: https://doi.org/10.24294/jipd8872
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Oluwasegun Julius Aroba, Michael Rudolph
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.