A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery

It is important to develop accurate disease management systems to identify and segment corn disease lesions and estimate their severity under complex field conditions. Although deep learning techniques are becoming increasingly popular to identify singular diseases, access to robust models for identifying multiple diseases and segmenting lesion areas for severity estimation under field conditions remain unsolved. In this study, a custom dataset consisting of handheld images of corn leaves infected with Gray Leaf Spot (GLS), Northern Leaf Blight (NLB), and Northern Leaf Spot (NLS) diseases, acquired under field conditions, was used to develop a novel two-stage semantic segmentation approach for identifying corn diseases and estimate their severity. Three semantic segmentation models were trained for each stage using SegNet, UNet, and DeepLabV3+ network architectures. Stage one used semantic segmentation to extract leaves from complex field backgrounds. In stage two, semantic segmentation was used to locate, identify, and calculate area coverage for disease lesions. After the models were trained, the best performance for stage one was observed from the UNet model, which achieved up to 0.9422 mean weighted intersection over union (mwIoU) and 0.8063 mean boundary F1-score (mBFScore). The best performance for stage two was observed from the DeepLabV3+ model, which could identify the disease lesions with a mwIoU of 0.7379 and mBFScore of 0.5351. Finally, severity was estimated by calculating the percentage of leaf area covered by disease lesions. In the test set, an R2 value (coefficient of determination) of 0.96 was achieved, which denotes that the integrated (UNet-DeepLabV3+) model predicted the severity of three diseases very close to the actual observations. This study developed a novel two-stage deep learning-based approach to accurately identify three targeted corn diseases and estimate their severity to pave the way for developing a field-worthy disease management system.