Estimating fine-scale soil moisture content and wheat yield in drought-heterogeneous farmlands using machine learning regressions on multispectral UAV imagery

With the development of precision agriculture, soil moisture content (SMC), a key factor affecting wheat yield, requires efficient and near real-time monitoring at the field scale. This study applies soil line indices derived from unmanned aerial vehicle (UAV) multispectral imagery to estimate fine-scale SMC and wheat yield in two farmlands with distinct spatial SMC distributions (i.e., farmland drought heterogeneity) in Kinmen, Taiwan. By comparing traditional linear regression with several machine learning approaches, including support vector regression (SVR), gradient boosting regression (GBR), and convolutional neural network regression (CNNR), key indices contributing to SMC and yield estimation across different wheat growth stages were identified. The results indicate that both SMC and wheat yield can be reasonably explained when significant farmland drought heterogeneity is present. Linear regression achieved optimal coefficients of determination (p < 0.01) of 0.65 for SMC using the perpendicular vegetation index and 0.93 for wheat yield using the normalized difference vegetation index. Areas with higher SMC within a farmland were found to negatively affect wheat growth. Among the machine learning models, GBR and CNNR based on the selected indices (VAPDI and NDVI, respectively) achieved estimation accuracies of 98.0 % and 72.2 %, respectively, for total wheat yield across the two experimental farmlands. Temporal validation across different growing cycles suggests the potential for cross-year wheat yield prediction. Deviations from expected estimation patterns may be considered preliminary indicators of abnormal yield conditions, and early-stage yield estimation shows promising potential. Overall, these findings provide preliminary evidence for early-stage yield prediction and offer valuable insights for optimizing irrigation timing and enhancing the development of AI-driven agricultural decision support systems.