{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:11:36Z","timestamp":1780402296629,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Climate change presents significant challenges to agriculture worldwide, leading to food insecurity and impacting rural livelihoods. Maize farming is especially vulnerable to extreme weather, such as heavy rainfall, high temperatures, soil acidity, humidity, and poor irrigation, which reduce crop yields and raise concerns about food security. The study aimed to develop a reliable and accurate machine learning method to predict maize crop yields using historical climate data to facilitate decision-making. This allows farmers and agronomists to forecast maize production based on past data for adaptation. A dataset from Meteo Rwanda and maize yield data from the Kayonza district, Rwanda, were used for training and testing. The weather data included annual mean temperature, maximum temperature, minimum temperature, rainfall, and soil temperature over the past thirteen years. The data were analyzed using machine learning techniques such as Random Forest regressor, Extreme Boost regressor, Gradient, Support Vector Machine, and LASSO (Least Absolute Shrinkage and Selection Operator). The results show that developing a high-yield crop depends on predicting and integrating climate variables, especially temperature and rainfall. Overall, Random Forest, Support Vector Machine, and Extreme Boost outperformed LASSO, with R2 values of 0.957, 0.955, and 0.953, compared to 0.256 for LASSO.<\/jats:p>","DOI":"10.3390\/a19060448","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:25:31Z","timestamp":1780399531000},"page":"448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance Analysis of Machine Learning Techniques in Predicting Maize Crop Yield: Case Study of Kayonza District\u2014Rwanda"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5124-3032","authenticated-orcid":false,"given":"Bobo Mafrebo","family":"Lionel","sequence":"first","affiliation":[{"name":"African Center of Excellence in Internet of Things, University of Rwanda, Kigali P.O. Box 3900, Rwanda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Musabe","sequence":"additional","affiliation":[{"name":"School of ICT, College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5001-377X","authenticated-orcid":false,"given":"Omar","family":"Gatera","sequence":"additional","affiliation":[{"name":"African Center of Excellence in Internet of Things, University of Rwanda, Kigali P.O. Box 3900, Rwanda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Celestin","family":"Twizere","sequence":"additional","affiliation":[{"name":"Regional Center of Excellence in Biomedical Engineering and E-Health, University of Rwanda, Kigali P.O. Box 3900, Rwanda"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15748","DOI":"10.1109\/ACCESS.2024.3358865","article-title":"Climate Change Impact on Agricultural Land Suitability","volume":"12","author":"Shevchenko","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"(2020, May 05). Updated Nationally Determined Contribution, Page 9. Available online: https:\/\/unfccc.int\/NDCREG."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"739","DOI":"10.3103\/S1068373923090017","article-title":"Climate Change and Its Impact on Agriculture","volume":"48","author":"Perevedentsev","year":"2023","journal-title":"Russ. Meteorol. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pereira, L. (2017). Climate Change Impacts on Agriculture Across Africa, Oxford University Press.","DOI":"10.1093\/acrefore\/9780199389414.013.292"},{"key":"ref_5","first-page":"49","article-title":"Evidence of Climate Change Impacts on Agriculture and Food Security in Nigeria","volume":"2","author":"Bello","year":"2012","journal-title":"Int. J. Agric. For."},{"key":"ref_6","unstructured":"Fifth Strategic Plan Agriculture Transformation PSTA 5 (2024). Building Resilient and Sustainable Agri-Food Systems."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mahin, A., Adnan, N., and Khondoker, R. (2026). Precision Agriculture Using Machine Learning and Deep Learning Algorithms. A Comprehensive Study. J. Agric. Educ. Res., Available online: https:\/\/www.researchgate.net\/publication\/391483355_Precision_Agriculture_using_Machine_Learning_and_Deep_Learning_Algorithms_A_Comprehensive_Study.","DOI":"10.21203\/rs.3.rs-6589718\/v1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s44279-025-00335-z","article-title":"A comparative study of machine learning models in predicting crop yield","volume":"3","author":"Lionel","year":"2025","journal-title":"Discov. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., and Uwamahoro, A. (2023). Crop Yield Prediction using machine learning models: Case of Potato and Maize. Agriculture, 13.","DOI":"10.3390\/agriculture13010225"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., Borodulin, A., and Tynchenko, Y. (2024). Predicting Sustainable crop yields: Deep learning and Explainable AI Tools. Sustainability, 16.","DOI":"10.3390\/su16219437"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2166222","DOI":"10.1080\/08839514.2023.2166222","article-title":"The Deep learning Resnet 101 and ensemble XGBoost algorithm with hyperparameter optimization accurately predict lung cancer","volume":"37","author":"Ahmed","year":"2023","journal-title":"Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, N., Qu, Y., Song, Z., Chen, Y., and Jiang, J. (2022). Responses and sensitivities of maize phenology to climate change from 1971 to 2020 in Henan Province, China. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262289"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9077383","DOI":"10.1155\/2022\/9077383","article-title":"The Effect of Rainfall, Temperature, and Relative Humidity on the Yield of Cassava, Yam, and Maize in the Ashanti Region of Ghana","volume":"2022","author":"Dwamena","year":"2022","journal-title":"Int. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Waqas, M.A., Wang, X., Zafar, S.A., Noor, M.A., Hussain, H.A., Azher Nawaz, M., and Farooq, M. (2021). Thermal Stresses in Maize: Effects and Management Strategies. Plants, 10.","DOI":"10.3390\/plants10020293"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/s13143-020-00212-3","article-title":"Weather Forecasting Using an Ensemble of Spatial-Temporal Attention Networks and Multi-Layer Perceptron","volume":"57","author":"Li","year":"2021","journal-title":"Asia-Pac. J. Atmos. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e40836","DOI":"10.1016\/j.heliyon.2024.e40836","article-title":"Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches with insight for future research and sustainability","volume":"10","author":"Jabel","year":"2024","journal-title":"Heliyon"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i47\/106449","article-title":"Application of machine learning techniques in agricultural crop production: A review paper","volume":"9","author":"Mishra","year":"2016","journal-title":"Indian J. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Morales, A., and Villalobos, F.J. (2023). Using machine learning for crop yield production in the past or for the future. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1128388"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11831-021-09569-8","article-title":"Emerging Trends in Machine Learning to Predict Crop Yield and Study Its Influential Factors: A Survey","volume":"29","author":"Bali","year":"2021","journal-title":"Arch. Computat. Methods Eng."},{"key":"ref_20","first-page":"103705","article-title":"Machine learning approaches for predicting maize biomass yield using environmental variables","volume":"210","author":"Abbasi","year":"2025","journal-title":"Agric. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s44279-025-00215-6","article-title":"Maize and soybean yield prediction using machine learning methods: A systematic literature review","volume":"3","author":"Sharma","year":"2025","journal-title":"Discov. Agric."},{"key":"ref_22","first-page":"100123","article-title":"Machine learning algorithms for maize yield prediction using multispectral imagery","volume":"11","author":"Sapkota","year":"2025","journal-title":"Sci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106546","DOI":"10.1016\/j.compag.2021.106546","article-title":"Support vector machine in Precision Agriculture: A review","volume":"191","author":"Kok","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"968","DOI":"10.12944\/CARJ.11.3.26","article-title":"Crop selection and yield prediction using machine learning approach","volume":"11","author":"Patil","year":"2023","journal-title":"Curr. Agric. Res. J."},{"key":"ref_25","unstructured":"Bhagat, M., and Bakariya, B. (2026, April 08). A Comprehensive Review of Cross-Validation Techniques in Machine Learning. Int. J. Sci. Technol., Available online: https:\/\/www.ijsat.org\/papers\/2025\/1\/1305.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"359","DOI":"10.3390\/make1010021","article-title":"High Dimension LASSO\u2014Based computational Regression Model; Regularization, Shrinkage and Selection","volume":"1","author":"Emmart","year":"2019","journal-title":"Mach. Learn. Knowl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"980","DOI":"10.3934\/agrfood.2024053","article-title":"Crop yield prediction through machine learning. A path towards sustainable agriculture and climate resilience in Saudi Arabia","volume":"9","author":"Islam","year":"2024","journal-title":"AIMS Agric. Food"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Menon, A.G., and Prabhakar, M. (2021). Smart Agriculture Monitoring Rover for Small-Scale Farms in Rural Areas using IoT. Proceedings of the 2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/ICSES52305.2021.9633976"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nikhil, U.V., Pandiyan, A.M., Raja, S.P., and Stamenkovic, Z. (2024). Machine learning-based crop yield prediction in South India: Performance Analysis of various models. Computers, 13.","DOI":"10.3390\/computers13060137"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"162799","DOI":"10.1109\/ACCESS.2024.3486653","article-title":"Crop classification and yield prediction using robust machine learning models for agricultural sustainability","volume":"12","author":"Badshah","year":"2025","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez, G.C., G\u00f3mez G\u00f3mez, J., and Jim\u00e9nez-Cabas, J. (2025). Predictive Models Based on Artificial Intelligence to estimate crop yield. Agriculture, 15.","DOI":"10.3390\/agriculture15232438"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"679","DOI":"10.24191\/mjoc.v6i1.8822","article-title":"Implementation of machine learning for predicting maize crop yield using multiple linear regression and backward elimination","volume":"6","author":"Fashoto","year":"2021","journal-title":"Malays. J. Comput."},{"key":"ref_33","first-page":"1157","article-title":"An efficiency crop yield prediction framework using a hybrid machine learning model","volume":"37","author":"Chitradurga","year":"2023","journal-title":"Rev. D Intell. Artif."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"217","DOI":"10.54254\/2755-2721\/2025.20800","article-title":"Crop Yield Time-Series Data Prediction Based on Multiple Hybrid Machine Learning Models","volume":"133","author":"Yan","year":"2025","journal-title":"Appl. Comput. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Arizo-Garc\u00eda, P., Casti\u00f1eira-Ib\u00e1\u00f1ez, S., Cruzado-Campos, E., San Bautista, A., and Rubio, C. (2026). High resolution wheat and Barley yield forecasting using multi-temporal satellite time series and machine learning. Agriculture, 16.","DOI":"10.3390\/agriculture16050516"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shinyclimensa, C., and Parthiban, A. (2026). Network-enhanced machine learning framework for multi-crop yield prediction: A comprehensive analysis of Indian agriculture data. Front. Agron., 8.","DOI":"10.3389\/fagro.2026.1767878"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, R., Wu, X., Li, J., Zhao, P., Zhang, Q., Wuri, L., Zhang, D., Zhang, Z., and Yang, L. (2025). A bibliometric review of deep learning in crop monitoring: Trends, challenges and future perspectives. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1636898"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Singh, K., Yadav, M., Barak, D., Bansal, S., and Moreira, F. (2025). Machine learning-based frameworks for reliable and sustainable crop forecasting. Sustainability, 17.","DOI":"10.3390\/su17104711"},{"key":"ref_39","unstructured":"Zhao, X., Deng, X., Xiang, D., and Wang, S. (2026). Analysis and prediction of the coupling coordination relationship between digital economy and agricultural new quality productivity. Proceedings of the 2026 International Conference on Digital Economy and Agricultural Development, IEEE."},{"key":"ref_40","first-page":"99","article-title":"Hybrid Neural Networks for improved crop yield prediction and water demand estimation","volume":"43","author":"Rahman","year":"2025","journal-title":"Arab Gulf J. Sci. Res."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/6\/448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:40:00Z","timestamp":1780400400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/6\/448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":40,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["a19060448"],"URL":"https:\/\/doi.org\/10.3390\/a19060448","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,1]]}}}