A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++
| Autor: | Jianping Huang, Mingfeng Huang, Guoqin Xu, Junyu Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Lesion segmentation
Computer science Intersection (set theory) business.industry Agriculture (General) Pattern recognition Plant Science Image segmentation Disease Convolutional neural network Plant disease YOLACT++ S1-972 lesions segmentation plant disease northern maize leaf blight attention mechanism instance segmentation convolutional neural network Market segmentation Segmentation Artificial intelligence business Agronomy and Crop Science Food Science |
| Zdroj: | Agriculture; Volume 11; Issue 12; Pages: 1216 Agriculture, Vol 11, Iss 1216, p 1216 (2021) |
| ISSN: | 2077-0472 |
| DOI: | 10.3390/agriculture11121216 |
| Popis: | Northern leaf blight (NLB) is a serious disease in maize which leads to significant yield losses. Automatic and accurate methods of quantifying disease are crucial for disease identification and quantitative assessment of severity. Leaf images collected with natural backgrounds pose a great challenge to the segmentation of disease lesions. To address these problems, we propose an image segmentation method based on YOLACT++ with an attention module for segmenting disease lesions of maize leaves in natural conditions in order to improve the accuracy and real-time ability of lesion segmentation. The attention module is equipped on the output of the ResNet-101 backbone and the output of the FPN. The experimental results demonstrate that the proposed method improves segmentation accuracy compared with the state-of-the-art disease lesion-segmentation methods. The proposed method achieved 98.71% maize leaf lesion segmentation precision, a comprehensive evaluation index of 98.36%, and a mean Intersection over Union of 84.91%; the average processing time of a single image was about 31.5 ms. The results show that the proposed method allows for the automatic and accurate quantitative assessment of crop disease severity in natural conditions. |
| Databáze: | OpenAIRE |
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