Dynamic and Lightweight Detection of Strawberry Diseases Using Enhanced YOLOv10
Abstract
1. Introduction
2. Proposed Method
2.1. Introduction of the CBAM Attention Mechanism
- —channel attention weights;
- —activation function sigmoid;
- —the feature mapping in space after tie pooling;
- —the feature mapping in space after maximum pooling;
- —the weight matrix of the 1st fully connected layer;
- —the weight matrix of the 2nd fully connected layer.
- —spatial attention weights;
- —activation function sigmoid;
- —convolutional operational filters of size;
- —the feature mapping after tie pooling on the channel;
- —the feature mapping after maximum pooling on the channel.
- —input feature map;
- —the final feature map obtained after CBAM processing.
2.2. Integration of the SCConv Module with the C2f Module to Establish the C2f_SCConv Module
2.3. Introducing DySample, an Ultra-Lightweight and Effective Dynamic Upsampler
3. Experiments
3.1. Dataset
3.2. Experimental Platform and Parameters
3.3. Evaluation Indicators
3.4. Comparison Experiment Before and After Improvement
3.5. Ablation Experiment
3.6. Comparison Experiment
3.7. Strawberry Disease Detection System
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Category of Disease | Angular Leafspot | Anthracnose Fruit Rot | Blossom Blight | Gray Mold | Leaf Spot | Powdery Mildew Fruit | Powdery Mildew Leaf |
---|---|---|---|---|---|---|---|
Quantities | 870 | 194 | 416 | 954 | 1230 | 270 | 1066 |
Software and Hardware Platform | Model Parameters |
---|---|
Operating system | Windows 11 |
Processing unit | 11th Gen Intel(R) Core(TM) i9-11900 @ 2.50 GHz |
Display card (computer) | NVIDIA GeForce RTX 3080 |
Organizing plan | Pytorch 2.3.1 |
Programming Environment | Python 3.9 |
Video Memory, GB | 36 GB |
Memory, GB | 32 GB |
Image Size | 640 × 640 |
Optimizer | AdamW |
Learning Rate | 0.01 |
Epochs | 200 |
Batch Size | 32 |
Arithmetic | P (%) | R (%) | mAP50 (%) | F1 | Parameters | GFLOPs | Model Size | FPS |
---|---|---|---|---|---|---|---|---|
Pre-improvement | 0.882 | 0.812 | 0.873 | 0.846 | 2,697,146 | 8.2 | 5.8 | 142.8 |
Improved | 0.885 | 0.865 | 0.914 | 0.875 | 2,624,204 | 7.9 | 5.7 | 149 |
Arithmetic | P (%) | R (%) | mAP50 (%) | F1 | Parameters | GFLOPs | Model Size | FPS |
---|---|---|---|---|---|---|---|---|
Pre-improvement | 0.935 | 0.851 | 0.917 | 0.891 | 2,697,146 | 8.2 | 5.8 | 141.1 |
Improved | 0.951 | 0.903 | 0.958 | 0.926 | 2,624,204 | 7.9 | 5.7 | 146.5 |
Arithmetic | P (%) | R (%) | mAP50 (%) | F1 |
---|---|---|---|---|
Pre-improvement | 0.587 | 0.402 | 0.436 | 0.477 |
Improved | 0.574 | 0.420 | 0.449 | 0.485 |
Serial Number | CBAM | C2f_SCConv | Dysample | P | R | mAP50 | F1 |
---|---|---|---|---|---|---|---|
A | 0.882 | 0.812 | 0.873 | 0.846 | |||
B | √ | 0.883 | 0.843 | 0.896 | 0.863 | ||
C | √ | 0.928 | 0.806 | 0.899 | 0.863 | ||
D | √ | 0.915 | 0.838 | 0.892 | 0.875 (0.87480) | ||
E | √ | √ | 0.901 | 0.845 | 0.901 | 0.872 | |
F | √ | √ | 0.918 | 0.822 | 0.904 | 0.867 | |
G | √ | √ | 0.915 | 0.82 | 0.892 | 0.865 | |
YOLO10-SC (non-pre-trained) | √ | √ | √ | 0.885 | 0.865 | 0.914 | 0.875 (0.87488) |
YOLO10-SC (pre-trained) | √ | √ | √ | 0.951 | 0.903 | 0.958 | 0.926 |
Method | P | R | mAP | F1 |
---|---|---|---|---|
YOLOv5 (2020) | 0.892 | 0.829 | 0.888 | 0.859 |
Rt-DETR (2023) | 0.862 | 0.856 | 0.874 | 0.859 |
YOLOv7 (2022) | 0.871 | 0.802 | 0.854 | 0.835 |
YOLOv8 (2023) | 0.878 | 0.843 | 0.888 | 0.860 |
YOLOv9 (2024) | 0.885 | 0.824 | 0.896 | 0.853 |
YOLOv8-AM (2024) | 0.842 | 0.808 | 0.869 | 0.825 |
FCE-YOLOv8 (2024) | 0.843 | 0.823 | 0.873 | 0.833 |
YOLO9tr (2024) | 0.869 | 0.825 | 0.891 | 0.846 |
HIC-YOLOv5 (2023) | 0.798 | 0.791 | 0.803 | 0.794 |
RCS-YOLO (2024) | 0.933 | 0.802 | 0.855 | 0.863 |
Mask R-CNN (2021) | 0.702 | 0.815 | 0.824 | 0.754 |
YOLOv11 (2024) | 0.889 | 0.824 | 0.885 | 0.855 |
YOLOv12 (2025) | 0.880 | 0.823 | 0.892 | 0.851 |
YOLO10-SC | 0.885 | 0.865 | 0.914 | 0.875 |
Improved Faster R_CNN (pre-trained) (2022) | - | - | 0.922 | - |
YOLO-GIC-C (pre-trained) (2023) | 0.933 | 0.903 | 0.947 | 0.918 |
YOLO10-SC (pre-trained) | 0.951 | 0.903 | 0.958 | 0.926 |
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Jin, H.; Ji, X.; Liu, W. Dynamic and Lightweight Detection of Strawberry Diseases Using Enhanced YOLOv10. Electronics 2025, 14, 3768. https://doi.org/10.3390/electronics14193768
Jin H, Ji X, Liu W. Dynamic and Lightweight Detection of Strawberry Diseases Using Enhanced YOLOv10. Electronics. 2025; 14(19):3768. https://doi.org/10.3390/electronics14193768
Chicago/Turabian StyleJin, Huilong, Xiangrong Ji, and Wanming Liu. 2025. "Dynamic and Lightweight Detection of Strawberry Diseases Using Enhanced YOLOv10" Electronics 14, no. 19: 3768. https://doi.org/10.3390/electronics14193768
APA StyleJin, H., Ji, X., & Liu, W. (2025). Dynamic and Lightweight Detection of Strawberry Diseases Using Enhanced YOLOv10. Electronics, 14(19), 3768. https://doi.org/10.3390/electronics14193768