Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning.

Abstract The plant pathogen Phytophthora infestans causes the severe disease late blight in potato, which results in a huge loss for potato production. Automatic and accurate disease lesion segmentation enables fast evaluation of disease severity and assessment of disease progress for precision crop breeding. Deep learning has gained tremendous success in computer vision tasks for image classification, object detection and semantic segmentation. To test whether we could extract late blight lesions from unstructured field environments based on high-resolution visual field images and deep learning algorithms, we collected ~500 field RGB images in a set of diverse potato genotypes with different disease severity (0-70%), resulting in 2100 cropped images. 1600 of these cropped images were used as the dataset for training deep neural networks. Finally, the developed model was tested on the 250 cropped images. The results show that the intersection over union (IoU) values of background (leaf and soil) and disease lesion classes in the test dataset are 0.996 and 0.386, respectively. Furthermore, we established a linear relationship (R 2 = 0.655) between manual visual scores of late blight and the number of lesions at the canopy level. We also learned that imbalance weights of lesion and background classes improved segmentation performance, and that fused masks based on the majority voting of the multiple masks enhanced the correlation with the visual scores. This study demonstrates the feasibility of using deep learning algorithms for disease lesion segmentation and severity evaluation based on proximal imagery for crop resistance breeding in field environments.