Integrating UAV and high-resolution satellite remote sensing for multi-scale rice disease monitoring

Abstract

Rice Bacterial Blight (RBB), caused by Xanthomonas oryzae pv. oryzae (Xoo), is a major rice disease that significantly threatens yield and quality. RBB spreads rapidly under favorable conditions, affects extensive areas, and requires timely, large-scale monitoring due to its narrow window for effective detection. Traditional satellite monitoring methods, which rely on specific remote sensing platforms and extensive ground surveys, often fail to meet the timely and efficient needs of large-scale disease monitoring. To address the limitations of these traditional methods, this study proposes a cross-scale crop disease monitoring approach that integrates unmanned aerial vehicle (UAV) and satellite remote sensing. With RBB disease monitoring in rice as a case study, the inconsistency between different scale remote sensing data is first introduced to align satellite imagery with UAV data. Next, a sensitivity analysis of the original reflectance and disease-related vegetation indices at both scales is conducted to identify features with consistent performance. The minimum redundancy maximum relevance (mRMR) feature selection algorithm is then employed to obtain sensitive feature sets for each scale. Three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—were used to develop disease monitoring models at both UAV and satellite scales. The optimal UAV-scale RF model was then applied to the corrected satellite data for cross-scale monitoring. Results indicate that the proposed cross-scale monitoring method achieved an accuracy of 87.78%, a precision of 88.13%, a recall of 87.78%, and an F1-score of 0.88 for the three-class classification of healthy, mildly infected, and severely infected RBB. The method effectively overcomes the reliance on extensive ground survey data typical of traditional large-scale crop disease remote sensing monitoring methods. Furthermore, the developed approach enables the cross-scale transfer of small-scale monitoring models, ensuring timely disease monitoring during outbreaks.