Research on grape leaf disease detection method based on NMA-YOLOv8n
In response to the low inefficiency and high misjudgement rate of manually observing grape leaf diseases, an improved YOLOv8n grape leaf disease detection model NMA-YOLOv8n is proposed. Firstly, the global nonlinear attention NBL was introduced in the neck network, which enhances the backbone featur...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/08/itmconf_emit2025_01038.pdf |
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| Summary: | In response to the low inefficiency and high misjudgement rate of manually observing grape leaf diseases, an improved YOLOv8n grape leaf disease detection model NMA-YOLOv8n is proposed. Firstly, the global nonlinear attention NBL was introduced in the neck network, which enhances the backbone feature extraction capability by fusing the local and non-local attention, enabling the network to equally focus on small and normal targets. Secondly, the MPDCIoU loss function is designed to replace the original Bbox loss function, improving the regression accuracy of the bounding box. At the output end, the detection performance of the algorithm for small targets is improved by designing the AFPN small target detection head. The experimental results show that the NMA-YOLOv8n model mAP@0.5 reaches 94.7%, 1.7% higher than YOLOv8n; the FPS reaches 124.6 frames/sec, which can meet the real-time detection requirements, and has higher detection accuracy and speed compared with other five mainstream target detection models.NMA-YOLOv8n provides grape disease detection with a better method, which has certain significance for the prevention and control of grape diseases. |
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| ISSN: | 2271-2097 |