Interpretable multi-resolution cotton moisture monitoring via Dual-Cycle UAV Learning

Accurate monitoring of cotton water status is crucial for optimizing irrigation management and improving water-use efficiency in precision agriculture. UAV-based remote sensing offers high-resolution, flexible, and efficient data acquisition for agricultural monitoring, presenting significant potential for assessing crop water stress. However, existing approaches often treat spectral and texture features separately, overlooking their complementary nature across resolutions. This increases model complexity and reduces generalizability across phenological stages. To address these limitations, we propose a Dual-Cycle Cognitive Learning (DCCL) framework that integrates multi-resolution vegetation indices and texture features through a two-stage interpretable training pipeline. In the first stage, all extracted features are fed into a random forest model, and their contributions are quantified using SHapley Additive exPlanations (SHAP). The top 20 SHAP-ranked features are further refined using Recursive Feature Elimination (RFE) to select the 10 most informative features. These features are reintroduced into a pretrained model to form a distilled final monitoring model in the second stage, enhancing interpretability and cross-scale monitoring accuracy. Knowledge distillation further facilitates feature integration across different resolutions and growth stages, eliminating the need for manual feature engineering. Experiments conducted on real UAV datasets demonstrate the effectiveness of the proposed DCCL framework. It achieved a training R² of 0.9577 and an RMSE of 0.0026, while maintaining a cross-validation R² of 0.6514 on unseen datasets. In contrast, the baseline random forest model yielded a training R² of 0.9484 but a considerably lower cross-validation R² of 0.4268. These results confirm the improved robustness and generalizability of our approach under real-world field conditions. The DCCL framework offers a scalable, interpretable, and high-precision solution for UAV-based cotton water status monitoring, with significant potential to support sustainable irrigation strategies and intelligent crop management in large-scale agricultural systems. • Two-stage SHAP-RFE feature selection with local refinement and Knowledge distillation optimizes feature distribution. • Resolution sensitivity shows robust UAV features across scales. • 45% R² improvement via bias–variance reduction. • 23% accuracy gain during key growth stages vs models. • Texture dominance guides multi-source agriculture integration.