GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases
Abstract
:1. Introduction
- (1)
- Proposing a lightweight CNN model, named GrapeNet, based on residual feature fusion block (RFFB) modules and convolutional block attention modules (CBAMs) [22], for the identification of different symptom stages for specific grape diseases.
- (2)
- Implementing ablation experiments and visualization of results of the model to verify the effectiveness of the RFFB modules and the CBAM modules, respectively.
- (3)
- Comparing GrapeNet with other classical network models to verify the performance advantages of GrapeNet.
2. Materials and Methods
2.1. Image Acquisition
2.2. Image Preprocessing
2.3. GrapeNet Model Framework
2.4. RFFB Module
2.5. CBAM Module
2.6. Evaluation Indexes
2.7. Experimental Environment and Hyperparameter Setting
3. Results
3.1. The Impact of Data Augmentation on the Model
3.2. Ablation Experiment
3.3. Visual Comparison of Output Feature Maps
3.4. Comparison of Results of Different Attention Mechanisms
3.5. Comparison of Identification Results with Classical CNN Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sample Distribution before Augmentation | Sample Distribution after Augmentation | |||||
---|---|---|---|---|---|---|
Class | Training set | Validation set | Test set | Training set | Validation set | Test set |
Grape healthy leaf (GH) | 265 | 29 | 42 | 2650 | 29 | 42 |
Grape black rot fungus with general symptoms (BRF_G) | 343 | 38 | 54 | 2058 | 38 | 54 |
Grape black rot fungus with serious symptoms (BRF_S) | 416 | 46 | 66 | 2496 | 46 | 66 |
Grape black measles fungus with general symptoms (BMF_G) | 453 | 50 | 74 | 2718 | 50 | 74 |
Grape black measles fungus with serious symptoms (BMF_S) | 378 | 41 | 59 | 2268 | 41 | 59 |
Grape leaf blight fungus with general symptoms (LBF_G) | 55 | 6 | 9 | 1980 | 6 | 9 |
Grape leaf blight fungus with serious symptoms (LBF_S) | 567 | 63 | 90 | 3402 | 63 | 90 |
Total | 2477 | 273 | 394 | 17,572 | 273 | 394 |
Name | Parameter |
---|---|
CPU | Intel(R) Xeon(R) W-2235 |
GPU | NVIDIA GeForce RTX 2080Ti |
System | Windows 10 |
Programming language | Python 3.8.8 |
Deep learning framework | Pytorch 1.6.0 |
RFFB | CBAM | Accuracy | Recall | Precision | F1-Score | Param (M) |
---|---|---|---|---|---|---|
- | - | 0.7944 | 0.7569 | 0.7372 | 0.7413 | 2.05 |
√ | - | 0.8249 | 0.7738 | 0.7878 | 0.7756 | 2.14 |
- | √ | 0.8223 | 0.7658 | 0.7884 | 0.7689 | 2.05 |
√ | √ | 0.8629 | 0.7776 | 0.8843 | 0.7905 | 2.15 |
Attention Mechanism | Accuracy | Recall | Precision | F1-Score | Param (M) |
---|---|---|---|---|---|
SE [25] | 0.8553 | 0.8012 | 0.8111 | 0.8053 | 2.15 |
CA [26] | 0.8553 | 0.8206 | 0.8267 | 0.8217 | 2.15 |
CBAM | 0.8629 | 0.7776 | 0.8843 | 0.7905 | 2.15 |
Model | Accuracy | Recall | Precision | F1-Score | Param (M) | Training Time (mins) |
---|---|---|---|---|---|---|
GoogLeNet [28] | 0.8299 | 0.7521 | 0.8069 | 0.7601 | 5.98 | 107 |
Vgg16 [29] | 0.8401 | 0.7761 | 0.7817 | 0.7777 | 134.29 | 254 |
ResNet34 [23] | 0.8274 | 0.7617 | 0.77 | 0.762 | 21.29 | 108 |
DenseNet121 [30] | 0.8477 | 0.7845 | 0.8357 | 0.7972 | 6.96 | 206 |
MobileNetV2 [31] | 0.8096 | 0.7327 | 0.7572 | 0.74 | 2.23 | 98 |
MobileNetV3_large [32] | 0.8274 | 0.7479 | 0.7818 | 0.7569 | 4.21 | 84 |
ShuffleNetV2_×1.0 [33] | 0.8096 | 0.7455 | 0.7472 | 0.7424 | 1.26 | 64 |
EfficientNetV2_s [34] | 0.8376 | 0.7738 | 0.8241 | 0.7865 | 20.19 | 290 |
GrapeNet | 0.8629 | 0.7776 | 0.8843 | 0.7905 | 2.15 | 101 |
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Lin, J.; Chen, X.; Pan, R.; Cao, T.; Cai, J.; Chen, Y.; Peng, X.; Cernava, T.; Zhang, X. GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases. Agriculture 2022, 12, 887. https://doi.org/10.3390/agriculture12060887
Lin J, Chen X, Pan R, Cao T, Cai J, Chen Y, Peng X, Cernava T, Zhang X. GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases. Agriculture. 2022; 12(6):887. https://doi.org/10.3390/agriculture12060887
Chicago/Turabian StyleLin, Jianwu, Xiaoyulong Chen, Renyong Pan, Tengbao Cao, Jitong Cai, Yang Chen, Xishun Peng, Tomislav Cernava, and Xin Zhang. 2022. "GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases" Agriculture 12, no. 6: 887. https://doi.org/10.3390/agriculture12060887