Enhancing Crop Yield Prediction Using Machine Learning and  Geospatial Data

Izukanji Muuka; Kasongo Changwe; Musoka Nyongolo; Penjani Hopkins Nyimbili; Masauso Sakala; Erastus M Mwanaumo; Wellington D Thwala

doi:10.38027/ICCAUA2026EN0149

Authors

Izukanji Muuka Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Kasongo Changwe Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Musoka Nyongolo Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Penjani Hopkins Nyimbili Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Masauso Sakala Department of Geomatic Engineering, School of Engineering, University of Zambia, Zambia
Erastus M Mwanaumo Built Environment and Information Technology, Faculty of Engineering, Walter Sisulu University, South Africa
Wellington D Thwala Built Environment and Information Technology, Faculty of Engineering, Walter Sisulu University, South Africa

DOI:

https://doi.org/10.38027/ICCAUA2026EN0149

Keywords:

Machine Learning, Crop Yield Prediction, NDVI, Remote Sensing, Precision Agriculture

Abstract

This study presents a geospatially informed machine learning approach to improve crop yield prediction in Zambia, where agriculture underpins rural livelihoods and national food security. The research integrates satellite-derived vegetation indices, principally the Normalised Difference Vegetation Index (NDVI), with meteorological indicators including seasonal rainfall distribution and temperature trends, across a 25-year wheat yield record (1999 to 2024) for a commercial farm in Chongwe District, Zambia. Datasets were harmonised through spatial standardisation, feature engineering, and temporal aggregation to produce a coherent input structure for a Random Forest regression model benchmarked against Extreme Gradient Boosting (XGBoost). Rainfall frequency, seasonal thermal accumulation, and vegetation vigour emerged as the most influential predictors of yield variability. Random Forest achieved a stable coefficient of determination (R2 = 0.389), while XGBoost exhibited apparent superiority (R2 = 0.950) attributable to overfitting on a small, spatially homogeneous dataset. The findings demonstrate that model selection is critical in small-sample agricultural prediction contexts and contribute an interactive, field-ready decision-support dashboard for precision agriculture in Zambia.