Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx
Abstract
1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Methods
2.2.1. Proposed Algorithm
2.2.2. Improvements in the Convolutional Neural Network
2.2.3. Improvements in the Neck Component
2.2.4. Improvements in the Detection Head
3. Results
3.1. Experimental Environment and Model Evaluation Metrics
3.2. Ablation Study
3.3. Comparative Experiments with Different Models
3.4. Deployment on Edge Devices
4. Conclusions
- 1.
- Effectiveness of Model Improvements:
- 2.
- Lightweight and Efficient Design:
- 3.
- Practical Application Value:
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Categories | Label | Dataset | Train Set | Test Set |
---|---|---|---|---|
Spot disease | 0 | 3288 | 2630 | 658 |
Brown spot | 1 | 3096 | 2477 | 619 |
Grey spot | 2 | 2680 | 2144 | 536 |
Mosaic disease | 3 | 2504 | 2003 | 501 |
Rust disease | 4 | 2304 | 1843 | 461 |
Parameter | Value |
---|---|
Epoch | 250 |
Images size | 640 × 640 |
Batch size | 32 |
Learn rate | 0.01 |
Weight_decay | 0.0005 |
Momentum | 0.937 |
Method | P | R | mAP@0.5/% | mAP@0.5:0.95/% | Param/M | GFLOPs/G | Model Size/MB | FPS/ () |
---|---|---|---|---|---|---|---|---|
YOLOv8n | 0.911 | 0.921 | 0.955 | 0.577 | 3.157 | 8.9 | 6.3 | 256.7 |
YOLOv8n + Slim-Neck | 0.928 | 0.926 | 0.964 | 0.585 | 2.799 | 7.3 | 5.9 | 163.8 |
YOLOv8n + SPD-Conv | 0.922 | 0.932 | 0.964 | 0.584 | 2.789 | 7.6 | 5.8 | 235.8 |
YOLOv8n + SAHead | 0.926 | 0.924 | 0.961 | 0.581 | 2.868 | 7 | 6.0 | 153.5 |
YOLOv8n + Slim-Neck + SPD-Conv | 0.912 | 0.940 | 0.962 | 0.585 | 2.582 | 6.8 | 5.5 | 155.6 |
YOLOv8n + Slim-Neck + SAHead | 0.928 | 0.925 | 0.963 | 0.583 | 2.769 | 6.6 | 5.3 | 121.7 |
YOLOv8n + SPD-Conv + SAHead | 0.924 | 0.928 | 0.962 | 0.582 | 2.761 | 6.9 | 5.3 | 144.2 |
LightYOLO-AppleLeafDx | 0.930 | 0.923 | 0.965 | 0.587 | 2.443 | 5.7 | 5.2 | 107.2 |
Method | P | R | mAP@0.5/% | mAP@0.5:0.95/% | Param/M | GFLOPs/G | Model Size/MB | FPS/ () |
---|---|---|---|---|---|---|---|---|
YOLOv8n | 0.911 | 0.921 | 0.955 | 0.577 | 3.157 | 8.9 | 6.3 | 256.7 |
LightYOLO-AppleLeafDx | 0.930 | 0.923 | 0.965 | 0.587 | 2.443 | 5.7 | 5.2 | 107.2 |
YOLOv5 | 0.892 | 0.855 | 0.945 | 0.565 | 2.655 | 7.8 | 5.7 | 145.6 |
YOLOv6 | 0.868 | 0.851 | 0.935 | 0.555 | 4.500 | 13.1 | 7.2 | 110.2 |
YOLOv7 | 0.907 | 0.882 | 0.950 | 0.570 | 3.545 | 10.4 | 6.0 | 135.8 |
Models | Hardware Environment | FPS/ () | mAP/% |
---|---|---|---|
YOLOv8n | NVIDIA GeForce RTX 2080 Ti | 256.7 | 95.5 |
RKNN-Toolkit2-Emulation | 53.5 | 94.8 | |
RV1103 NPU | 30.1 | 95.1 | |
LightYOLO-AppleLeafDx | NVIDIA GeForce RTX 2080 Ti | 107.2 | 96.5 |
RKNN-Toolkit2-Emulation | 23.1 | 95.2 | |
RV1103 NPU | 14.8 | 95.9 |
Models | NPU-FPS/ () | mAP/% |
---|---|---|
YOLOv8n + Slim-Neck | 20.5 | 0.957 |
YOLOv8n + SPD-Conv | 28.1 | 0.956 |
YOLOv8n + SAHead | 19.8 | 0.956 |
YOLOv8n + Slim-Neck + SPD-Conv | 19.8 | 0.954 |
YOLOv8n + Slim-Neck + SAHead | 15.9 | 0.953 |
YOLOv8n + SPD-Conv + SAHead | 18.2 | 0.956 |
YOLOv5 | 17.4 | 0.935 |
YOLOv6 | 13.4 | 0.926 |
YOLOv7 | 16.3 | 0.944 |
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Zou, H.; Lv, P.; Zhao, M. Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx. Plants 2025, 14, 599. https://doi.org/10.3390/plants14040599
Zou H, Lv P, Zhao M. Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx. Plants. 2025; 14(4):599. https://doi.org/10.3390/plants14040599
Chicago/Turabian StyleZou, Hongyan, Peng Lv, and Maocheng Zhao. 2025. "Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx" Plants 14, no. 4: 599. https://doi.org/10.3390/plants14040599
APA StyleZou, H., Lv, P., & Zhao, M. (2025). Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx. Plants, 14(4), 599. https://doi.org/10.3390/plants14040599