Potato Late Blight Disease Detection on UAV Multispectral Imagery

In this study, Mask R-CNN was applied to 5-band raw reflectance images to detect potato plants in UAV images. The highest model performance across all metrics was achieved with a ResNeXt-101 backbone and transfer learning from the same model trained on apple orchard data. An F-1 score of 84.2% was achieved. To determine whether the plant was infected with PLB, two methods were used. In the first method, a Mask R-CNN with a DINOv3 small variant backbone was applied to 5-band raw reflectance images. The highest achieved F1-score was 69.05%. In the second method, classical ML classifiers were applied to the 5-band raw reflectance images and 16 associated vegetation index images. The highest F1-score (66.71%) was obtained with a decision tree classifier applied to the 16 vegetation index images. Feature importance analysis indicated that chlorophyll- and red-edge-related indices, such as CIgreen, TCARI, OSAVI2, and Red-edge NDVI, were the most discriminative features for distinguishing healthy and unhealthy potato plants. These results show the effectiveness of combining deep learning and machine learning approaches for potato late blight detection using UAV multispectral imagery.