Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification
Abstract
:1. Introduction
2. Materials
2.1. Materials
2.2. Problem Formulation
2.3. Methods
2.3.1. Baseline Framework for Meta-Learning
2.3.2. Slimming Network Pruning Algorithm
2.3.3. Indicators for Model Evaluation
3. Results
3.1. Data Setting
3.2. Effect of Pruning Coefficients on Model Performance
3.3. Effect of Pruning Strategy on Performance
3.4. Visualization of Loss Curves
3.5. K-Fold Cross-Validation
3.6. Comparison with Lightweight Networks
3.7. Comparison with Related Work
4. Discussion
4.1. Limitations of the Model and Future Work
4.2. Effect of Way and Shot
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Species | Class Numbers | Class Name | Number in PV |
---|---|---|---|
Apple | 4 | Apple scab, Black rot, Cedar apple rust, Healthy | 3174 |
Blueberry | 1 | Healthy | 1502 |
Cherry | 2 | Healthy, Powdery mildew | 1905 |
Corn | 4 | Cercospora leaf spot, Gray leaf spot, Common rust, Northern leaf blight, Healthy | 3852 |
Grape | 4 | Black rot, Black measles, Healthy, Leaf blight | 3862 |
Orange | 1 | Haunglongbing | 5507 |
Peach | 2 | Bacterial spot, Healthy | 2657 |
Pepper | 2 | Bell bacterial spot, Bell healthy | 2473 |
Potato | 3 | Early blight, Healthy, Late blight | 2152 |
Raspberry | 1 | Healthy | 371 |
Soybean | 1 | Healthy | 5089 |
Squash | 1 | Powdert mildew | 1835 |
Strawberry | 2 | Healthy, Leaf scorch | 1565 |
Tomato | 10 | Bacterial spot, Early blight, Healthy, Late blight, Leaf mold, Septoria leaf spot, Spider mite, Two-spotted spider mite, Target spot, Tomato mosaic virus (TMOV), Yellow leaf curl virus (YLCV) | 18,159 |
Network | Training Stage | Paramars (M) | Macs (G) | 5-way 1-shot | 5-way 5-shot | ||
---|---|---|---|---|---|---|---|
Acc (%) | 95% MSE | Acc (%) | 95% MSE | ||||
Resnet12—80% | A | 0.49 | 0.14 | 77.97 | 1.46 | 90.70 | 1.02 |
Resnet12—60% | A | 1.99 | 0.56 | 80.87 | 1.32 | 91.37 | 0.98 |
Resnet12—40% | A | 4.47 | 1.27 | 82.07 | 1.30 | 92.30 | 0.90 |
Resnet12—20% | A | 7.95 | 2.25 | 82.10 | 1.31 | 92.10 | 0.93 |
Resnet12—0% | A | 12.42 | 3.52 | 82.47 | 1.30 | 92.27 | 0.91 |
Network | Training Stage | 5-way 1-shot | 5-way 5-shot | ||
---|---|---|---|---|---|
Acc (%) | 95% MSE | Acc (%) | 95% MSE | ||
Resnet12—80% | A | 77.97 | 1.46 | 90.70 | 1.02 |
Resnet12—60% | A | 80.87 | 1.32 | 91.37 | 0.98 |
Resnet12—40% | A | 82.07 | 1.30 | 92.30 | 0.90 |
Resnet12—20% | A | 82.10 | 1.31 | 92.10 | 0.93 |
Resnet12—80% | B | 55.53 | 1.72 | 65.77 | 1.65 |
Resnet12—60% | B | 60.70 | 1.69 | 69.13 | 1.55 |
Resnet12—40% | B | 71.00 | 1.53 | 81.73 | 1.37 |
Resnet12—20% | B | 79.33 | 1.42 | 88.60 | 1.12 |
Network | Fold | 5-way 1-shot | 5-way 5-shot | ||
---|---|---|---|---|---|
Acc (%) | 95% MSE | Acc (%) | 95% MSE | ||
Resnet12—20% | 1st fold | 73.00 | 1.54 | 89.40 | 1.07 |
Resnet12—20% | 2nd fold | 76.70 | 1.32 | 90.37 | 1.07 |
Resnet12—20% | 3th fold | 79.77 | 1.41 | 87.47 | 1.19 |
Resnet12—20% | 4th fold | 77.77 | 1.42 | 87.43 | 1.15 |
Resnet12—20% | 5th fold | 78.60 | 1.39 | 89.63 | 1.06 |
Resnet12—20% | AVG | 77.17 | 1.42 | 88.86 | 1.11 |
Network | Paramars (M) | Macs (G) | 5-way 1-shot | 5-way 1-shot | ||
---|---|---|---|---|---|---|
Acc(%) | 95% MSE | Acc(%) | 95% MSE | |||
Shufflenetv2 | 1.99 | 0.03 | 73.60 | 1.55 | 87.70 | 1.18 |
Mobilenetv2 | 3.50 | 0.05 | 72.67 | 1.45 | 85.90 | 1.27 |
Mobilenetv3 | 5.76 | 0.05 | 67.60 | 1.62 | 82.30 | 1.37 |
EfficientNet | 2.82 | 0.03 | 73.60 | 1.50 | 85.70 | 1.25 |
Densenet40 | 0.61 | 0.82 | 79.63 | 1.41 | 94.40 | 0.80 |
Resnet12 | 12.42 | 3.52 | 82.47 | 1.30 | 92.27 | 0.91 |
Resnet12—80% | 0.49 | 0.14 | 77.97 | 1.46 | 90.70 | 1.02 |
Method | 5-way 1-shot | 5-way 5-shot | ||
---|---|---|---|---|
Acc (%) | 95% MSE | Acc (%) | 95% MSE | |
Matching net (2017) | 81.52 | 0.01 | 91.14 | 0.01 |
Prototypical net (2018) | 78.21 | 1.56 | 89.47 | 1.06 |
FEAT (2021) | 81.38 | 1.35 | 91.29 | 0.96 |
DeepEMD (2023) | 82.47 | 1.30 | 92.27 | 0.91 |
FewTURE (2023) | 84.20 | 1.21 | 95.53 | 0.74 |
Resnet12—20% | 77.97 | 1.46 | 90.70 | 1.02 |
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Yan, W.; Feng, Q.; Yang, S.; Zhang, J.; Yang, W. Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification. Agronomy 2024, 14, 1878. https://doi.org/10.3390/agronomy14091878
Yan W, Feng Q, Yang S, Zhang J, Yang W. Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification. Agronomy. 2024; 14(9):1878. https://doi.org/10.3390/agronomy14091878
Chicago/Turabian StyleYan, Wenbo, Quan Feng, Sen Yang, Jianhua Zhang, and Wanxia Yang. 2024. "Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification" Agronomy 14, no. 9: 1878. https://doi.org/10.3390/agronomy14091878