Crop Protection and Productivity through the Adoption of Machine Learning Algorithms in Sub-Saharan Africa: A Systematic Review
DOI:
https://doi.org/10.65150/EP-gjetr/V1E3/2025-04Keywords:
Agricultural productivity, crop protection, machine learning, precision agriculture, Sub-Saharan AfricaAbstract
The economy of Sub-Saharan Africa depends heavily on agriculture, facing challenges like inefficient resource use, food insecurity, and climate shocks. This paper evaluates how machine learning (ML) could transform agricultural practices. We reviewed 88 studies (2010-2025) on ML applications like yield prediction, precision agriculture, and disease detection in countries like Rwanda, Nigeria, and Kenya. Systems like SMART-Crop Yield Prediction System predict maize yields with ≤0.177% inaccuracy using soil and meteorological data. CropGuard diagnoses crop diseases with 97% reli-ability using image recognition. ML can improve harvests by optimizing irrigation, cutting fertilizer waste by 30%, and enabling early pest action. Challenges include low technical literacy, erratic electricity, limited internet, and cultural resistance to adopting new tech. Scaling ML could strengthen food security, cut post-harvest losses, and em-power smallholder farmers. Success requires collaboration between governments, tech developers, and local communities. Priorities include developing farmer-friendly ML tools, funding rural infrastructure, and training initiatives. Sub-Saharan Africa may use ML for climate-resilient farms, stabilizing food supplies, and improving livelihoods by aligning tech with local needs, paving the way for sustainable agricultural growth.
References
1) World Bank. (2023). World Development Indicators. World Bank Group.
2) Jayne, T. S., & Sanchez, P. A. (2021). Agricultural productivity must improve in sub-Saharan Africa. Science. https://doi.org/10.1126/science.abf5413
3) Moyo, S. (2016). Family farming in sub-Saharan Africa: its contribution to agriculture, food security and rural development (No. 150).
Working paper. https://hdl.handle.net/10419/173805
4) FAO. 2022. The State of World Fisheries and Aquaculture 2022. Towards Blue Transformation. Rome, FAO.https://doi.org/10.4060/cc0461en
5) IPCC, 2023: Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 1-34, https://doi.org/10.59327/IPCC/AR6-9789291691647.001
6) AGRA. (2021). Africa Agriculture Status Report. A Decade of Action: Building Sustainable and Resilient Food Systems in Africa (Issue 9). Nairobi, Kenya: Alliance for a Green Revolution in Africa (AGRA). https://agra.org/annual-report-2021/wp-content/uploads/2022/10/AGRA-Annual-Report-2021.pdf
7) Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors. https://doi.org/10.3390/s18082674
8) Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons. https://doi.org/10.1016/j.bushor.2018.03.007
9) Tien, J. M. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science. https://doi.org/10.1007/s40745-017-0112-5
10) Panat, S., & Kumar, R. (2023). Introduction to artificial intelligence & ML. In A Guide to Applied Machine Learning for Biologists (pp. 127-146). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-22206-1_5
11) Kühl, N., Schemmer, M., Goutier, M., & Satzger, G. (2022). Artificial intelligence and machine learning. Electronic Markets. https://doi.org/10.1007/s12525-022-00598-0
12) Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science. https://doi.org/10.1007/s42979-021-00592-x
13) Cembrowska-Lech, D., Krzemińska, A., Miller, T., Nowakowska, A., Adamski, C., Radaczyńska, M., ... & Mikiciuk, M. (2023). An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture. Biology. https://doi.org/10.3390/biology12101298
14) Attri, I., Awasthi, L. K., & Sharma, T. P. (2024). Machine learning in agriculture: a review of crop management applications. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-16105-2
15) Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M. S., ... & Qian, Q. (2022). Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Molecular Plant. https://doi.org/10.1016/j.molp.2022.10.007
16) Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. H. M. (2019). Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE access. https://doi.org/10.1109/ACCESS.2019.2932609.
17) Zhang, L., Dabipi, I. K., & Brown Jr, W. L. (2018). Internet of Things applications for agriculture. Internet of things A to Z: technologies and applications. https://doi.org/10.1002/9781119456735.ch18
18) Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems engineering. https://doi.org/10.1016/j.biosystemseng.2017.09.007
19) Tugrul, B., Elfatimi, E., & Eryigit, R. (2022). Convolutional neural networks in detection of plant leaf diseases: A review. Agriculture. https://doi.org/10.3390/agriculture12081192
20) Wani, J. A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., & Singh, S. (2022). Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges. Archives of Computational methods in Engineering. https://doi.org/10.1007/s11831-021-09588-5
21) Ngugi, H. N., Ezugwu, A. E., Akinyelu, A. A., & Abualigah, L. (2024). Revolutionizing crop disease detection with computational deep learning: a comprehensive review. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-024-12454-z
22) Lu, J., Tan, L., & Jiang, H. (2021). Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture. https://doi.org/10.3390/agriculture11080707
23) Ning, H., Liu, S., Zhu, Q., & Zhou, T. (2023). Convolutional neural network in rice disease recognition: accuracy, speed and lightweight. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2023.1269371
24) Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The digitisation of agriculture: A survey of research activities on smart farming. Array. https://doi.org/10.1016/j.array.2019.100009
25) Shidende, N., & Mwogosi, A. (2025). Exploring the impact of generative AI tools on healthcare delivery in Tanzania. Journal of Health Organization and Management, (ahead-of-print). https://doi.org/10.1108/JHOM-01-2025-0007
26) Lachman, J., & López, A. (2019). Innovation obstacles in an emerging high-tech sector: The case of precision agriculture in Argentina. Management Research. https://doi.org/10.1108/MRJIAM-11-2018-0883
27) Bangole, N. K. R. (2024). A machine learning-based crop diseases detection and management system. Advances in Business Information Systems and Analytics. https://doi.org/10.4018/979-8-3693-3583-3.ch001
28) Xia, M., Huang, Z., Tian, L., Wang, H., Chang, V., Zhu, Y., & Feng, S. (2021). SparkNoC: An energy-efficiency FPGA-based accelerator using optimized lightweight CNN for edge computing. Journal of Systems Architecture.https://doi.org/10.1016/j.sysarc.2021.101991
29) GSMA. (2023). The Mobile Economy Sub-Saharan Africa. GSM Association.
30) Reiger, B. (2022). Adoption of Artificial Intelligence Based Technologies in Sub-Saharan African Agriculture (Master's thesis, Universidade NOVA de Lisboa (Portugal)).
31) Singh, M., Upadhyay, L., Badekhan, A., Shil, S., Gautam, S. K., Anand, A., & Karthickraja, A. (2024). Leveraging remote sensing and nanotechnology to overcome barriers to agroforestry adoption by small holder farmers. J Sci Res Rep. http://www.ijrpr.com/
32) Kuradusenge, M., Hitimana, E., Mtonga, K., et al. (2024). SMART-CYPS: An intelligent IoT and machine learning-powered crop yield prediction system for food security. Discover Internet of Things. https://doi.org/10.1007/s43926-024-00079-0
33) Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal. https://doi.org/10.1155/2024/2126734
34) Konyannik, B. Y., & Zargar, M. (2025). Water Use Efficiency in Ghana’s Smallholder Farming Systems: Unpacking the Benefits of Integrating Conservation Tillage, Digital Agriculture, and Rainwater Harvesting Practices. Authorea Preprints.
35) Ajith, S., Vijayakumar, S., & Elakkiya, N. (2025). Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms. Discover Food. https://doi.org/10.1007/s44187-025-00338-1
36) Basu, A., & Narayan, A. (2025). The role of machine learning in transforming agricultural practices: insights into crop yield optimization and disease detection. Iran Journal of Computer Science. https://doi.org/10.1007/s42044-025-00280-6
37) Mahenge, M. P. J., Mkwazu, H., Sanga, C. A., Madege, R. R., Mwaipopo, B., & Maro, C. (2023). Artificial intelligence and deep learning based technologies for emerging disease recognition and pest prediction in beans (phaseolus vulgaris l.): A systematic review. African Journal of Agricultural Research. https://doi.org/10.5897/AJAR2022.16226
38) Portela, F., Sousa, J. J., Araújo-Paredes, C., Peres, E., Morais, R., & Pádua, L. (2024). A systematic review on the advancements in remote sensing and proximity tools for grapevine disease detection. Sensors. https://doi.org/10.3390/s24248172
39) John, M. A., Bankole, I., Ajayi-Moses, O., Ijila, T., Jeje, T., & Lalit, P. (2023). Relevance of advanced plant disease detection techniques in disease and Pest Management for Ensuring Food Security and Their Implication: A review. American Journal of Plant Sciences. https://doi.org/10.4236/ajps.2023.1411086
40) Negi, P., & Anand, S. (2024). Plant disease detection, diagnosis, and management: Recent advances and future perspectives. Artificial Intelligence and Smart Agriculture: Technology and Applications. https://doi.org/10.1007/978-981-97-0341-8_20
41) Mahlein, A. K. (2016). Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant disease. https://doi.org/10.1094/PDIS-03-15-0340-FE
42) TuYizere, D., Uwase, V., Niyonkuru, M., Ndanyunzwe, G., Kabutware, M., Singadi, P., ... & Okeyo, G. (2024, July). AI-Driven Precision Farming: A Holistic Approach to Enhance Food and Nutrition Security in Africa. In 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS) (pp. 1-6). IEEE. https://doi.org/10.1109/COINS61597.2024.10622109
43) Nyasulu, C., Diattara, A., Traore, A., Deme, A., & Ba, C. (2022). Towards resilient agriculture to hostile climate change in the Sahel region: A case study of machine learning-based weather prediction in Senegal. Agriculture. https://doi.org/10.3390/agriculture12091473
44) Gul, D., & Banday, R. U. Z. (2024). Transforming Crop Management Through Advanced AI and Machine Learning: Insights into Innovative Strategies for Sustainable Agriculture. AI Computer Science and Robotics Technology. https://doi.org/10.5772/acrt.20240030
45) Bachu, L., Kandibanda, A., Grandhi, N., Athina, D. P., & Ande, P. K. (2024). Machine Learning for Enhanced Crop Management and Optimization of Yield in Precision Agriculture. https://doi.org/10.1109/i-smac61858.2024.10714733
46) Jiménez, A. F., Cárdenas, P. F., & Jiménez, F. (2022). Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2021.106635
47) Musanase, C., Vodacek, A., Hanyurwimfura, D., et al. (2023). Data-driven analysis and machine learning-based crop and fertilizer recommendation system for revolutionizing farming practices. Poľnohospodárstvo. https://doi.org/10.3390/agriculture13112141
48) Biswas, H. K., Sim, T. Y., & Lau, S. L. (2024). Impact of building information modelling and advanced technologies in the AEC industry: a contemporary review and future directions. Journal of Building Engineering. https://doi.org/10.1016/j.jobe.2023.108165
49) Fattepur, G., Patil, A. Y., Kumar, P., Kumar, A., Hegde, C., Siddhalingeshwar, I. G., ... & Khan, T. Y. (2024). Bio-inspired designs: Leveraging biological brilliance in mechanical engineering—An overview. 3 Biotech. https://doi.org/10.1007/s13205-024-04153-w
50) Adinarayana, S., Raju, M. G., Srirangam, D. P., Prasad, D. S., Kumar, M. R., & veesam, S. B. (2024). Enhancing resource management in precision farming through AI‐based irrigation optimization. How Machine Learning is Innovating Today's World: A Concise Technical Guide. https://doi.org/10.1002/9781394214167.ch15
51) Mohyuddin, G., Khan, M. A., Haseeb, A., Mahpara, S., Waseem, M., & Saleh, A. M. (2024). Evaluation of machine learning approaches for precision farming in smart agriculture system: a comprehensive review. IEEE access. https://doi.org/10.1109/ACCESS.2024.3390581
52) Akintuyi, O. B. (2024). Adaptive AI in precision agriculture: a review: investigating the use of self-learning algorithms in optimizing farm operations based on real-time data. Research Journal of Multidisciplinary Studies. https://doi.org/10.53022/oarjms.2024.7.2.0023
53) Upadhyay, A., Patel, A., Patel, A., Chandel, N. S., Chakraborty, S. K., & Bhalekar, D. G. (2025). Leveraging AI and ML in Precision Farming for Pest and Disease Management: Benefits, Challenges, and Future Prospects. Ecologically Mediated Development: Promoting Biodiversity Conservation and Food Security. https://doi.org/10.1007/978-981-96-2413-3_23
54) Sharma, K., & Shivandu, S. K. (2024). Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture. Sensors International. https://doi.org/10.1016/j.sintl.2024.100292
55) Musau, E. G., & Sharma, K. (2024). Sustainable Agriculture Leveraging Artificial Intelligence Systems in Kenya’s Agri-food Supply Chain. Agricultural Science. https://doi.org/10.55173/agriscience.v7i2.128
56) SS, V. C., Hareendran, A., & Albaaji, G. F. (2024). Precision farming for sustainability: An agricultural intelligence model. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2024.109386
57) Sharma, A., Sharma, A., Tselykh, A., Bozhenyuk, A., Choudhury, T., Alomar, M. A., & Sánchez-Chero, M. (2023). Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture. Open Life Sciences. https://doi.org/10.1515/biol-2022-0713
58) Mishra, H., & Mishra, D. (2023). Artificial intelligence and machine learning in agriculture: Transforming farming systems. Res. Trends Agric. Sci. https://doi.org/10.5281/zenodo.15157536
59) Wang, S., Xu, D., Liang, H., Bai, Y., Li, X., Zhou, J., ... & Wei, W. (2025). Advances in deep learning applications for plant disease and pest detection: A review. Remote Sensing. https://doi.org/10.3390/rs17040698
60) Karunathilake, E. M. B. M., Le, A. T., Heo, S., Chung, Y. S., & Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture. https://doi.org/10.3390/agriculture13081593
61) Li, K., Li, X., Liu, B., Ge, C., Zhang, Y., & Chen, L. (2023). Diagnosis and application of rice diseases based on deep learning. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.1384
62) Latif, G., Abdelhamid, S. E., Mallouhy, R. E., Alghazo, J., & Kazimi, Z. A. (2022). Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN model. Plants. https://doi.org/10.3390/plants11172230
63) Feng, C., Jiang, M., Huang, Q., Zeng, L., Zhang, C., & Fan, Y. (2022). A lightweight real-time rice blast disease segmentation method based on DFFANet. Agriculture. https://doi.org/10.3390/agriculture12101543
64) Deng, R., Tao, M., Xing, H., Yang, X., Liu, C., Liao, K., & Qi, L. (2021). Automatic diagnosis of rice diseases using deep learning. Frontiers in plant science. https://doi.org/10.3389/fpls.2021.701038
65) Shuaibu, N., Obunadike, G. N., & Jamilu, B. A. (2024). Crop yield prediction using selected machine learning algorithms. FUDMA journal of Sciences. https://doi.org/10.33003/fjs-2024-0801-2220
66) Panjwani, S., Jampani, M., Sambou, M. H., & Amarnath, G. (2024). Leveraging crop yield forecasts using satellite information for early warning in Senegal. Climate Smart Agriculture. https://doi.org/10.1016/j.csag.2024.100024
67) Wang, H., Li, J., Tian, Q., & Niyogi, D. (2022). Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning-Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction. Remote Sensing.https://doi.org/10.1002/essoar.10510222.1
68) Maginga, T. J., Kutuku, S. J., Hamza, H., Mulokozi, G., & Nsenga, J. (2024). MkulimaGPT: Equitable AI Use Via a Swahili Chatbot for Maize Farming System in Tanzania. African Journal of Agriculture and Food Science. https://doi.org/10.52589/ajafs-verntb5i
69) Bushara, A. R., T., A., & S., F. (2025). Optimizing crop yield forecasting with ensemble machine learning techniques. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2025.14.1.0189
70) Traunmüller, H. (2023). Performance Assessment of Machine Learning Techniques for Corn Yield Prediction.https://doi.org/10.1007/978-3-031-28183-9_23
71) Bharathi, S., & Navaprakash, N. (2024, April). Prediction of indian gdp using xgboost compared with adaboost. In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE.
https://doi.org/10.1109/ADICS58448.2024.10533574
72) Yan, Y., Wang, Y., Li, J., Zhang, J., & Mo, X. H. (2025). Crop Yield Time-Series Data Prediction Based on Multiple Hybrid Machine Learning Models. https://doi.org/10.20944/preprints202501.1948.v1
73) Noorunnahar, M., Chowdhury, A. H., & Mila, F. A. (2023). A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. PloS one. https://doi.org/10.1371/journal.pone.0283452
74) Huber, F., Yushchenko, A., Stratmann, B., & Steinhage, V. (2022). Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107346
75) Paudel, D., De Wit, A., Boogaard, H., Marcos, D., Osinga, S., & Athanasiadis, I. N. (2023). Interpretability of deep learning models for crop yield forecasting. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.107663
76) Nayak, H. S., Silva, J. V., Parihar, C. M., Krupnik, T. J., Sena, D. R., Kakraliya, S. K., ... & Sapkota, T. B. (2022). Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India. Field Crops Research. https://doi.org/10.1016/j.fcr.2022.108640
77) Badshah, A., Alkazemi, B. Y., Din, F., Zamli, K. Z., & Haris, M. (2024). Crop classification and yield prediction using robust machine learning models for agricultural sustainability. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3486653
78) Morales, A., & Villalobos, F. J. (2023). Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2023.1128388
79) Lee, D., Davenport, F., Shukla, S., Husak, G. J., Funk, C., Budde, M. E., Rowland, J., & Verdin, J. P. (2023). Contrasting Performance of Panel and Time-Series Models for Subnational Crop Forecasting in Sub-Saharan Africa. https://doi.org/10.2139/ssrn.4635817
80) Ozor, N., Nwakaire, J., Nyambane, A., Muhatiah, W., & Nwobodo, C. E. (2025). Enhancing Africa’s agriculture and food systems through responsible and gender inclusive AI innovation: insights from AI4AFS network. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2024.1472236
81) Olarewaju, O. O., Fawole, O. A., Baiyegunhi, L. J., & Mabhaudhi, T. (2025). Integrating Sustainable Agricultural Practices to Enhance Climate Resilience and Food Security in Sub-Saharan Africa: A Multidisciplinary Perspective. Sustainability. https://doi.org/10.3390/su17146259
82) Chavula, P., Kayusi, F., & Juma, L. (2024). Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa. LatIA. https://doi.org/10.62486/latia202588
83) Raimi, L., Panait, M., & Sule, R. (2021). Leveraging precision agriculture for sustainable food security in sub-Saharan Africa: a theoretical discourse. In Shifting Patterns of Agricultural Trade: The Protectionism Outbreak and Food Security (pp. 491-509).Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-3260-0_21
84) Chavula, P., & Kayusi, F. (2025). Challenges in Sub-Saharan Africa’s Food Systems and the Potential Role of AI. LatIA. https://doi.org/10.62486/latia2025318
85) Tefera, M. L. (2025). Sustainable Solutions to Land Degradation and Rainfall Variability in Sub-Saharan Africa: Integrating Traditional Water Management, Agricultural Intensification, and Machine Learning Approaches. https://hdl.handle.net/20.500.14242/195962
86) Ahmed, S. M., Dinnar, H. A., Ahmed, A. E., Elbushra, A. A., & Turk, K. G. B. (2024). A Deeper Understanding of Climate Variability Improves Mitigation Efforts, Climate Services, Food Security, and Development Initiatives in Sub-Saharan Africa. Climate. https://doi.org/10.3390/cli12120206
87) Benti, N. E., Chaka, M. D., Semie, A. G., Warkineh, B., & Soromessa, T. (2024). Transforming agriculture with Machine Learning, Deep Learning, and IoT: perspectives from Ethiopia—challenges and opportunities. Discover Agriculture.https://doi.org/10.1007/s44279-024-00066-7
88) Daum, T., Buchwald, H., Gerlicher, A., & Birner, R. (2018). Smartphone apps as a new method to collect data on smallholder farming systems in the digital age: A case study from Zambia. Computers and electronics in agriculture.https://doi.org/10.1016/j.compag.2018.08.017
89) Kamran, M., Tanveer, K., Khalid, N., Khalil, M. N., Ahmad, B., Arooj, A., ... & Mir, S. Z. (2025). INTEGRATING ADVANCED DEEP LEARNING ALGORITHMS FOR CLIMATE SYSTEMS: ENHANCING WEATHER FORECAST ACCURACY, REAL-TIME CLIMATE MONITORING, AND LONG-TERM CLIMATE PREDICTIONS. Spectrum of Engineering Sciences.
https://sesjournal.com/index.php/1/article/view/471
90) Padhiary, M. (2025). The convergence of deep learning, IoT, sensors, and farm machinery in agriculture. In Designing sustainable internet of things solutions for smart industries (pp. 109-142). IGI Global. https://doi.org/10.4018/979-8-3693-5498-8.ch005
91) Achieng, M. (2023). Digital technologies for integrated food loss and waste reduction in agrifood chains in Sub-Saharan Africa: A scoping review. IEEE Xplore. https://doi.org/10.23919/IST-AFRICA60249.2023.10187757
92) Omar, M. M., Shitindi, M. J., Massawe, B. J., Fue, K. G., Pedersen, O., & Meliyo, J. L. (2022). Exploring farmers’ perception, knowledge, and management techniques of salt-affected soils to enhance rice production on small land holdings in Tanzania. Cogent Food & Agriculture. https://doi.org/10.1080/23311932.2022.2140470
93) Mduma, N., & Mayo, F. (2024). Updating “machine learning imagery dataset for maize crop: A case of Tanzania” with expanded data to cover the new farming season. Data in Brief. https://doi.org/10.1016/j.dib.2024.110359
94) Peng, W., & Karimi Sadaghiani, O. (2023). A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. https://doi.org/10.1080/15567036.2023.2232322
95) Abdullah, H. M., Islam, M. N., Saikat, M. H., & Bhuiyan, M. A. (2024). Precision agriculture practices from planting to postharvest: scopes, opportunities, and challenges of innovation in developing countries. Remote Sensing in Precision Agriculture. https://doi.org/10.1016/B978-0-323-91068-2.00014-X
96) Jose, A., Deepak, K. S., & Rajamani, N. (2024). Innovation in agriculture and the environment: a roadmap to food security in developing nations. In Food Security in a Developing World: Status, Challenges, and Opportunities (pp. 259-281). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-57283-8_15
97) Khaspuria, G., Khandelwal, A., Agarwal, M., Bafna, M., Yadav, R., & Yadav, A. K. (2024). Adoption of Precision Agriculture Technologies among Farmers: A Comprehensive Review. Journal of Scientific Research and Reports.https://doi.org/10.9734/jsrr/2024/v30i72180
98) Brugler, S., Gardezi, M., Dadkhah, A., Rizzo, D. M., Zia, A., & Clay, S. A. (2023). Improving decision support systems with machine learning: identifying barriers to adoption. Agronomy Journal. https://doi.org/10.1002/agj2.21432
99) Adewusi, A. O., Asuzu, O. F., Olorunsogo, T., Iwuanyanwu, C., Adaga, E., & Daraojimba, D. O. (2024). AI in precision agriculture: A review of technologies for sustainable farming practices. World Journal of Advanced Research and Reviews.https://doi.org/10.30574/wjarr.2024.21.1.0314
100) Aroba, O. J., & Rudolph, M. (2024). Systematic literature review on the application of precision agriculture using artificial intelligence by small-scale farmers in Africa and its societal impact. Journal of Infrastructure, Policy and Development. https://doi.org/10.24294/jipd8872
101) Luo, M. (2025). Enhancing agricultural knowledge and digital literacy of farmers through data-driven technology integration. International Journal of Information and Communication Technology. https://doi.org/10.1504/IJICT.2025.146098
102) Cravero, A., & Sepúlveda, S. (2021). Use and adaptations of machine learning in big data—Applications in real cases in agriculture. Electronics. https://doi.org/10.3390/electronics10050552
103) Prajapati, C. S., Priya, N. K., Bishnoi, S., Vishwakarma, S. K., Buvaneswari, K., Shastri, S., ... & Jadhav, A. (2025). The role of participatory approaches in modern agricultural extension: bridging knowledge gaps for sustainable farming practices. Journal of Experimental Agriculture International. https://doi.org/10.9734/jeai/2025/v47i23281
104) Manjunath, M. C., & Palayyan, B. P. (2023). An efficient crop yield prediction framework using hybrid machine learning model. Revue d'Intelligence Artificielle. https://doi.org/10.18280/ria.370428
105) Stathers, T., & Mvumi, B. (2020). Challenges and initiatives in reducing postharvest food losses and food waste: sub-Saharan Africa. In Preventing food losses and waste to achieve food security and sustainability (pp. 729-786). Burleigh Dodds Science Publishing. https://www.taylorfrancis.com/chapters/edit/10.1201/9780429266621-27/challenges-initiatives-reducing-postharvest-food-losses-food-waste-sub-saharan-africa-tanya-stathers-brighton-mvumi
106) Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023). Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives. Agronomy. https://doi.org/10.3390/agronomy13122976
107) Gebremedhin, Z. (2021). User-centered innovation and localized rapid prototyping for agricultural technology co-development with women smallholder farmers In Malawi (Doctoral dissertation, NUI Galway). https://doi.org/10.13025/17165
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Benjamin Yennuna Konyannik, Funchious Paul Mensah, Amos Ntow Djarbeng, Evans Oko Bapulah, Abdul-Jalil Salifu, Mohammed Yakubu, Liberty Lugu, Daniel Agbetawopkor Futorwu, Charles Yaw Gyamfi, Rufai Yakubu, Omed Mohammed Pirot (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
