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 image