Performance Analysis of Machine Learning Techniques in Predicting Maize Crop Yield: Case Study of Kayonza District—Rwanda

Climate change poses significant challenges to agricultural practices worldwide, affecting crop yields, food insecurity, and rural livelihoods. Maize crop farming is particularly vulnerable due to extreme weather conditions such as high rainfall, high temperature, soil acidity, humidity and unproper irrigation affecting crop yield and consider to be a source of hunger and food security concern. The aim of the study was to propose a reliable and accurate machine learning techniques to be used in the prediction of maize crop yield using historical climate and soil data for informed planning. This enables farmers, agronomists and decision makers to forecast maize crop yield based on historical data for adaptation. To come up with a comprehensive prediction model, historical dataset from Meteo Rwanda and Maize crop yield from Kayonza district-Rwanda were used in the training and testing. Weather data considered in this study were annual mean temperature, annual maximum temperature, annual minimum temperature, annual rainfall, soil temperature for the past thirteen years. The data collected were analyzed using Random Forest regressor, Extreme Boost regressor Gradient, support vector machine and least absolute shrinkage and least absolute shrinkage and selection ( LASSO) machine learning techniques. The results shows that random forest perform better compared to other models with an accuracy of R² 0.957, support vector machine 0.957, XGBoost regressor 0.953, LASSO 0.256 and can be recommended for prediction of maize crop yield. The random forest regressor will be adapted in design and development of prototype to improve farming decision making.