A Two-Stage Approach to the Study of Potato Disease Severity Classification

• Published in: Agriculture (Basel) 2024-02, Vol.14 (3), p.386
• Main Authors: Xu, Yanlei, Gao, Zhiyuan, Wang, Jingli, Zhou, Yang, Li, Jian, Meng, Xianzhang
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Agricultural research; Algorithms; Analysis; Classification; convolutional neural network; Crop diseases; Deep learning; disease classification; disease severity classification; Early blight; Image processing; Image segmentation; Late blight; Leaves; Lesions; Medical imaging; Medical research; Medicine, Experimental; Methods; Neural networks; potato diseases; Potatoes; Semantic segmentation; Semantics; Vegetables
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14030386

Early blight and late blight are two of the most prevalent and severe diseases affecting potato crops. Efficient and accurate grading of their severity is crucial for effective disease management. However, existing grading methods are limited to assessing the severity of each disease independently, often resulting in low recognition accuracy and slow grading processes. To address these challenges, this study proposes a novel two-stage approach for the rapid severity grading of both early blight and late blight in potato plants. In this research, two lightweight models were developed: Coformer and SegCoformer. In the initial stage, Coformer efficiently categorizes potato leaves into three classes: those afflicted by early blight, those afflicted by late blight, and healthy leaves. In the subsequent stage, SegCoformer accurately segments leaves, lesions, and backgrounds within the images obtained from the first stage. Furthermore, it assigns severity labels to the identified leaf lesions. To validate the accuracy and processing speed of the proposed methods, we conduct experimental comparisons. The experimental results indicate that Coformer achieves a classification accuracy as high as 97.86%, while SegCoformer achieves an mIoU of 88.50% for semantic segmentation. The combined accuracy of this method reaches 84%, outperforming the Sit + Unet_V accuracy by 1%. Notably, this approach achieves heightened accuracy while maintaining a faster processing speed, completing image processing in just 258.26 ms. This research methodology effectively enhances agricultural production efficiency.