Data-Driven Artificial Intelligence (AI) Algorithms for Modelling Potential Maize Yield under Maize–Legume Farming Systems in East Africa

Abstract

Agroecological farming systems such as maize–legume intercropping (MLI) and push-pull technology (PPT) have been introduced to mitigate losses from pests. Nevertheless, the regionwide maize yield gained from practicing such farming systems remains largely unknown. This study compares the performance of two uncomplex and interpretable models, namely the hybrid fuzzy-logic combined with the genetic algorithm and symbolic regression, to predict maize yield. Specifically, the study adopted the best-fitting model to map the potential maize yield under MLI and PPT compared to the monocropping system in East Africa using climatic and edaphic variables. The best model, i.e., the symbolic regression model, accurately fitted the maize yield data as indicated by the low root mean square error (RMSE < 0.09) and the higher R2 (>0.9). The study estimated that East African farmers would increase their annual maize yield by about 1.01 and 1.96 rates under MLI and PPT, respectively. Furthermore, the results showed a fairly good modelling performance as indicated by low standard deviations (range of 0.70–1.1) and skewness (absolute range of 0.03–0.09) values. The study guides the upscaling of MLI and PPT systems through awareness creation and public-private partnerships to ensure increased adoption of these sustainable farming practices.