Maize Crop Yield Prediction Using Machine Learning Techniques

Abstract

Maize is one of the main crops cultivated all throughout the world, including in Ethiopia. However, the production of maize changes widely based on many factors, such as weather, soil quality, and fertilizer usage. Predicting maize yields is crucial for farmers because it allows them to make informed crop management decisions. Machine learning approaches have shown promise in predicting crop productivity in recent years. The goal of this thesis is to explore the utilization of ensemble methods, namely Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT), in the context of maize yield prediction. Ensemble methods involve combining predictions from multiple models to enhance accuracy and fortify the reliability of the forecasting process. The dataset used in this study was compiled from data collected between 2003 and 2022 on several aspects such as weather, soil quality, and maize production. Before developing the ensemble techniques, the dataset was preprocessed and the features were normalized. The results of this research show that ensemble techniques can potentially be employed for predicting maize yields with great performance. The MAE was 0. 0025, the MSE was 0. 0032, the RMSE was 0. 0057, and the R2 was 0. 9928. The results show that meteorological factors like rainfall and temperature have a considerable impact on maize yields. Soil quality was also recognized as an important factor influencing maize crop production by the model. The research demonstrates that ensemble techniques could potentially be used to accurately predict maize yields. Farmers can use the study's findings to informed decisions about their agricultural practices. The research also emphasizes the significance of meteorological conditions and soil quality in predicting maize yields.