Automatic Quantification of Plant Disease From Field Image Data Using Deep Learning

Kanish Garg, Swati Bhugra, Brejesh Lall; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 1965-1972

Plant disease is a major factor in yield reduction. Thus, plant breeders currently rely on selecting disease-resistant plant cultivars, which involves disease severity rating of a large variety of cultivars. Traditional visual screening of these cultivars is an error-prone process, which necessitates the development of an automatic framework for disease quantification based on field-acquired images using unmanned aerial vehicles (UAVs) to augment the throughput. Since these images are impaired by complex backgrounds, uneven lighting, and densely overlapping leaves, state-of-the-art frameworks formulate the processing pipeline as a dichotomy problem (i.e. presence/absence of disease). However, additional information regarding accurate disease localization and quantification is crucial for breeders. This paper proposes a deep framework for simultaneous segmentation of individual leaf instances and corresponding diseased region using a unified feature map with a multi-task loss function for an end-to-end training. We test the framework on field maize dataset with Northern Leaf Blight (NLB) disease and the experimental results show a disease severity correlation of 73% with the manual ground truth data and run-time efficiency of 5fps.