Maize yield prediction using machine learning: a systematic literature review

Abstract

Introduction Accurate maize yield prediction is critical for food security planning, particularly in sub-Saharan Africa, where maize is essential to national economies and livelihoods. This systematic review assesses the use of machine learning (ML) techniques in maize yield estimation, focusing on the methodologies, predictor variables, and results in peer-reviewed studies. Methods The review followed the PRISMA 2021 guidelines, synthesizing 81 peer-reviewed studies published between 2014 and 2025. The analysis examined the ML algorithms, predictor variables, evaluation metrics, and methodological gaps identified in these studies. Results The review found a significant increase in publications after 2021, reflecting growing confidence in the application of ML for agronomic decision-support. Random Forest (49.4%), XGBoost (16.1%), and Support Vector Machines (12.4%) were the most common algorithms, with hybrid deep-learning frameworks showing superior performance. Environmental variables, remote-sensing indices, and soil properties were the most frequently used predictors. RMSE and R 2 were the primary evaluation metrics. Discussion The findings underscore the challenges of data scarcity, limited interpretability, and geographical imbalance in the research, with Africa contributing less than 25% of the studies. There is a need for open-access agricultural data systems, hybrid explainable AI frameworks, and capacity building in computational agronomy to improve the effectiveness of ML applications in maize yield prediction.