Deep Learning Models for Detection and Severity Assessment of Cercospora Leaf Spot (Cercospora capsici) in Chili Peppers Under Natural Conditions.

Abstract

The accurate assessment of plant disease severity is crucial for effective crop management. Deep learning, especially via CNNs, is widely used for image segmentation in plant lesion detection, but accurately assessing disease severity across varied environmental conditions remains challenging. This study evaluates eight deep learning models for detecting and quantifying Cercospora leaf spot (Cercospora capsici) severity in chili peppers under natural field conditions. A custom dataset of 1645 chili pepper leaf images, collected from a Brazilian plantation and annotated with 6282 lesions, was developed for real-world robustness, reflecting real-world variability in lighting and background. First, an algorithm was developed to process raw images, applying ROI selection and background removal. Then, four YOLOv8 and four Mask R-CNN models were fine-tuned for pixel-level segmentation and severity classification, comparing one-stage and two-stage models to offer practical insights for agricultural applications. In pixel-level segmentation on the test dataset, Mask R-CNN achieved superior precision with a Mean Intersection over Union (MIoU) of 0.860 and F1-score of 0.924 for the mask_rcnn_R101_FPN_3x model, compared to 0.808 and 0.893 for the YOLOv8s-Seg model. However, in severity classification, Mask R-CNN underestimated higher severity levels, with an accuracy of 72.3% for level III, while YOLOv8 attained 91.4%. Additionally, YOLOv8 demonstrated greater efficiency, with an inference time of 27 ms versus 89 ms for Mask R-CNN. While Mask R-CNN excels in segmentation accuracy, YOLOv8 offers a compelling balance of speed and reliable severity classification, making it suitable for real-time plant disease assessment in agricultural applications.