Lebalwa,Bertha Msuliche2025-02-192025-02-192022https://www.suaire.sua.ac.tz/handle/123456789/6577ThesisYield monitoring is vital for food security in the country. The uncertainty that may influence fluctuations in yield is impeccable to the nation's food security strategy. Due to climate change around the world, yield fluctuation has dramatically been affected and led to food supply shortage in most developing countries, including Tanzania, since most of the staple food depends on rainfed agriculture, which is hit by high temperatures and variations in rainfall. Bad weather also contributes to diseases, pests and weeds which greatly challenge the growth of crops, particularly the top grain and maize. Most countries have adopted several yield prediction methods to mitigate the effects to resolve the situation. Most countries have shown that emerging techniques such as machine learning provide good predictions to help countries mitigate the problem and secure food security. Machine learning regression models (linear, AdaBoost, gradient boosting, k- Neighbour, random forest and stacking ensemble) are trained and evaluated using dataset obtained from the online database. Normalized Difference Vegetation Index (NDV1) is used to predict vegetation that are later assumed to locate probability of maize fields. The climate data from those areas is then subjected to training on forecasting maize yield. Therefore, the results have shown that Machine learning methods like a stacking ensemble, which combine several other models and use Random Forest as the final model achieved low Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) of 0.27, 0.12 and 0.34 Ton per Ha for each district respectively. The results suggest machine learning model like stacking ensemble can be used by policy makers and farmers to mitigate the effects of climate change in yields for a particular season.enMachine learningMaize yieldRain-fedThesis