Advancing Corn Yield Mapping in Kenya Through Transfer Learning

Crop yield mapping is essential for food security and policy making. Recent machine learning (ML) and deep learning (DL) methods have achieved impressive accuracy in crop yield estimation. However, these models require numerous training samples that are scarce in regions with underdeveloped infrastructure. Furthermore, domain shifts between different spatial regions prevent DL models trained in one region from being directly applied to another without domain adaptation. This effect is particularly pronounced between regions with significant climate and environmental variations such as the U.S. and Kenya. To address this issue, we propose using fine-tuning-based transfer learning, which learns general associations between predictors and response variables from the data-abundant source domain and then fine-tunes the model on the data-scarce target domain. We assess the model’s performance on estimating corn yields using Kenya (target domain) and the U.S. (source domain). Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables from both domains, are used to pre-train and fine-tune the deep neural network model. The model is fine-tuned using data from 5 years (2019–2023) and tested using leave-one-year-out cross validation. The fine-tuned DNN achieves an overall R2 of 0.632—higher than both the U.S.-only and Kenya-only baselines—but paired significance tests show no aggregate difference, though a statistically significant gain does occur in 2023 under anomalous heat conditions. These results demonstrate that fine-tuning can reliably transfer learned representations across continents and, under certain climatic scenarios, yield meaningful improvements.