Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

Abstract

Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.

Code and data availability

The paper publicly releases its paper-specific assets: the annotated UAV image dataset (File S2) on CyVerse Data Commons, the ImageJ polygon annotation macro (File S1) and the Mask R-CNN training/validation code (File S3) in the authors' GoreLab GitHub repository. The matterport/Mask_RCNN dependency is a generic third‑