Corn Leaf Spot Disease Recognition Based on Improved YOLOv8
Abstract
:1. Introduction
2. Data and Methods
2.1. Data Collection and Dataset Construction
2.2. Methodology Research
2.2.1. YOLOv8 Network Models
2.2.2. Slim-Neck Module
2.2.3. GAM Attention Mechanisms
2.2.4. Loss Function Improvement
2.2.5. Improved Network Structure
3. Experiments and Analysis of Results
3.1. Experimental Environment
3.2. Evaluation Indicators
3.3. Analysis of Results
3.3.1. Performance Analysis of Algorithm
3.3.2. Ablation Experiments
3.3.3. Comparison Test
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Configure | Parameters |
---|---|
CPU | Intel(R) Xeon(R) Platinum 8358P CPU @ 2.60 GHz |
Random access memory (RAM) | 80 G |
GPUs | GeForce RTX 3090 |
Display memory | 24 G |
Training environment | CUDA 11.8 |
Operating system | Ubuntu 20.04 |
Development environment (computer) | Python 3.8.10 Pytorch 2.0.0 |
Module (in Software) | P | R | mAP50 | MAP50-95 | Total Parameters | Detection Time/ms | |||
---|---|---|---|---|---|---|---|---|---|
YOLOv8n | GAM | Slim-neck | |||||||
× | × | × | 91.39 | 84.46 | 91.09 | 64.32 | 3,157,200 | 11.7 | |
√ | × | × | 93.46 | 86.93 | 92.65 | 67.42 | 3,620,112 | 13.1 | |
× | √ | × | 93.91 | 85.81 | 92.08 | 66.53 | 2,528,851 | 10.4 | |
× | × | √ | 94.03 | 86.96 | 92.79 | 68.56 | 3,157,200 | 11.3 | |
√ | √ | × | 94.3 | 87.01 | 93.21 | 69.75 | 2,829,091 | 11.1 | |
√ | × | √ | 93.73 | 87.87 | 94.03 | 68.03 | 3,620,112 | 13.1 | |
× | √ | √ | 93.51 | 85.27 | 92.86 | 64.74 | 2,528,851 | 10.3 | |
√ | √ | √ | 95.18 | 89.11 | 94.65 | 71.62 | 2,829,091 | 11.3 |
Model | P | R | mAP50 | mAP50-95 | Total Parameters | Detection Time/ms |
---|---|---|---|---|---|---|
Improved model | 95.18 | 89.11 | 94.65 | 71.62 | 2,829,091 | 11.3 |
YOLOv3 | 90.00 | 84.61 | 89.96 | 64.04 | 4,058,603 | 13.6 |
YOLOv5 | 90.67 | 82.92 | 90.25 | 62.33 | 2,654,816 | 10.9 |
YOLOv6 | 90.89 | 81.40 | 89.07 | 62.61 | 4,500,080 | 13.9 |
YOLOv8 | 91.39 | 84.46 | 91.09 | 64.32 | 3,157,200 | 11.7 |
Faster R-CNN | 95.41 | 87.03 | 93.42 | 69.63 | 28,342,195 | 68.6 |
SSD | 94.40 | 86.36 | 92.83 | 68.60 | 23,745,908 | 45.3 |
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Yang, S.; Yao, J.; Teng, G. Corn Leaf Spot Disease Recognition Based on Improved YOLOv8. Agriculture 2024, 14, 666. https://doi.org/10.3390/agriculture14050666
Yang S, Yao J, Teng G. Corn Leaf Spot Disease Recognition Based on Improved YOLOv8. Agriculture. 2024; 14(5):666. https://doi.org/10.3390/agriculture14050666
Chicago/Turabian StyleYang, Shixiong, Jingfa Yao, and Guifa Teng. 2024. "Corn Leaf Spot Disease Recognition Based on Improved YOLOv8" Agriculture 14, no. 5: 666. https://doi.org/10.3390/agriculture14050666