GeoAI-Based Crop Yield Estimation in Africa: A Systematic and Bibliometric Literature Review With Comparisons to Major Agricultural Producers

Early and accurate crop monitoring and yield estimation are vital for improving food security and sustainable agriculture, yet conventional monitoring approaches based on censuses and crop cutting experiments remain costly and difficult to scale. GeoAI, which integrates EO data with ML algorithms, offers a cost-effective and scalable complement to conventional methods. However, the extent to which GeoAI approaches have advanced toward operational yield prediction in Africa remains unclear. This study presents the first comprehensive, systematic, and bibliometric review of GeoAI-based crop yield prediction in Africa, with a comparative perspective on major agricultural producers (MAP). Following PRISMA 2020 guidelines, 43 Africa- and 64 MAP-focused studies were analyzed to examine (i) the geospatial data sources used for yield estimation, (ii) the ML models and their reported performance, (iii) the geographic, institutional, authorship, and funding structures shaping the research landscape, and (iv) methodological trends, gaps, and future research directions. Results show that Sentinel-2 has become the dominant satellite data source for ML-based yield estimation in Africa, largely replacing coarse-resolution MODIS due to its improved spatial and spectral (red-edge bands) resolutions. Across studies, NDVI, precipitation, and temperature remain the most widely used predictors, while enhanced spectral indices (EVI, GCVI, MTCI, NDRE) and ancillary variables such as soil properties, LAI, evapotranspiration, and crop management information improve sensitivity and predictive performance. Random forest consistently performs well under data-scarce conditions, generally surpassing linear models. While deep learning remains underutilized and do not demonstrate systematic performance gains because of limited and noisy training datasets. Hybrid approaches integrating crop growth models with GeoAI show promise in mitigating sparse ground observations. Bibliometric analysis further reveals a highly donor-dependent and externally anchored research landscape, with geographically uneven study distribution and limited leadership by local institutions, in contrast to the diversified data infrastructures and nationally funded research ecosystems observed in MAP. We identify also critical gaps and actionable directions, including multi-year dataset harmonization, multi-sensor fusion, hybrid GeoAI-CGM modeling, domain adaptation strategies, and stable domestic research ecosystems to provide a sustainable roadmap for scalable and operational crop-yield prediction in data-scarce landscapes.