DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification
Abstract
:1. Introduction
- (1)
- Proposing a lightweight convolutional neural network model, called DFCANet, based on DFCA blocks and DS blocks, which are used to identify corn diseases in real environments.
- (2)
- Exploring an online data augmentation method for images of corn leaf diseases.
- (3)
- Comparing the DFCANet with other classical network models to prove the performance advantages of DFCANet and conduct ablation experiments to verify the validity of the different module designs.
2. Materials and Methods
2.1. Data Acquisition and Preprocessing
2.2. DFCANet Model
2.2.1. DFCANet
2.2.2. DFCA Block
2.2.3. Depthwise and Pointwise Convolution
2.2.4. Inverted Bottleneck
2.2.5. Coordinate Attention Module
2.2.6. DS Block
2.3. Experimental Environment and Hyperparameter Setting
2.4. Evaluation Indexes
3. Results
3.1. Impact of Different Data Augmentation Methods on the Model
3.2. Comparative Experiment of Different Network Models
3.3. Ablation Experiments
3.4. Simulation of Real Weather Data Augmentation Experiments
3.5. Comparative Experiments of the Public Datasets
4. Discussion
5. Future Work
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Dataset | Selected Plant/s | Performance Metrics/Accuracy | Ref |
---|---|---|---|---|
SVM | Plantvillage | Corn | 83.7% | [4] |
SVM | Plantvillage | Corn | 83.7% | [5] |
Improved LeNet | Plantvillage | Corn | 97.89% | [6] |
Improved CNN | Plantvillage | Corn | 98.78% | [7] |
GoogleNet | Plantvillage | 38 classes | 99.35% | [8] |
CNN | Plantvillage | Corn | 88.46% | [9] |
DMS-Robust AlexNet | Plantvillage, AI challenge, Google web of site and Self-collected diseases | Corn | 98.62% | [12] |
SKPSNet-50 | Own practical database | Corn | 92.9% | [13] |
DADCNN-5 | Own practical database | 44 classes | 97.33% | [14] |
MobileNet-V2 + Transformer | Kaggle datasets | 3 classes | 96.58% | [15] |
MS-DNet | Own practical database | Rice | 98.32% | [16] |
Mobile-Atten | Self-collected diseases | Rice | 98.48% | [17] |
DISE-NET | Self-collected diseases | Corn | 97.12% | [18] |
LDSNet | Plantvillage, public website and Self-collected diseases | Corn | 95.4% | [19] |
GrapeNet | AI challenge | Grape | 86.29% | [20] |
Input | Operator | Output |
---|---|---|
2242 × 3 | DS | 1122 × 12 |
1122 × 12 | DFCA | 1122 × 12 |
1122 × 12 | DS | 562 × 48 |
562 × 48 | DFCA | 562 × 48 |
562 × 48 | DS | 282 × 96 |
282 × 96 | DFCA | 282 × 96 |
282 × 96 | DS | 142 × 192 |
142 × 192 | DFCA | 142 × 192 |
142 × 192 | DS | 72 × 384 |
72 × 384 | DFCA | 72 × 384 |
72 × 384 | Depthwise Conv | 72 × 384 |
72 × 384 | AdaptiveAvgPool2d | 12 × 384 |
12 × 384 | FC | - |
Method | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Without augmentation | 0.9541 | 0.9535 | 0.9573 | 0.9547 |
Cutout | 0.9694 | 0.9720 | 0.9720 | 0.9720 |
Random Erasing | 0.9633 | 0.9659 | 0.9671 | 0.9665 |
GridMask | 0.9480 | 0.9504 | 0.9541 | 0.9516 |
KeepAugment | 0.9785 | 0.9817 | 0.9792 | 0.9803 |
Model | Accuracy | Precision | Recall | F1-Score | Params (M) | Flops |
---|---|---|---|---|---|---|
VGG16 [42] | 0.9480 | 0.9483 | 0.9491 | 0.9485 | 134.29 | 15.50G |
ResNet50 [39] | 0.9266 | 0.9314 | 0.9299 | 0.9298 | 23.51 | 4.12G |
EffcientNet-B0 [43] | 0.9571 | 0.9565 | 0.9591 | 0.9568 | 40.09 | 398.02M |
ConvNeXt-B [38] | 0.9296 | 0.9271 | 0.9336 | 0.9289 | 89.00 | 15.40G |
DenseNet121 [44] | 0.9357 | 0.9383 | 0.9383 | 0.9383 | 6.96 | 2.88G |
MobileNet-V2 [40] | 0.9480 | 0.9488 | 0.9500 | 0.9485 | 2.22 | 318.96M |
MobileNetV3-Large [45] | 0.9480 | 0.9453 | 0.9516 | 0.9476 | 4.20 | 226.43M |
ShuffleNetV2-1.0× [46] | 0.9449 | 0.9440 | 0.9483 | 0.9460 | 1.26 | 149.57M |
DFCANet | 0.9785 | 0.9817 | 0.9792 | 0.9803 | 1.91 | 309.1M |
Attention Mechanism | Accuracy | Precision | Recall | F1-Score | Param (M) | Training Time (Seconds)/Epoch | Test Time (Seconds)/Epoch |
---|---|---|---|---|---|---|---|
SE [47] | 0.9633 | 0.9631 | 0.9650 | 0.9638 | 1.89 | 70 | 33 |
CBAM [48] | 0.9510 | 0.9503 | 0.9561 | 0.9526 | 1.89 | 68 | 33 |
CA | 0.9785 | 0.9817 | 0.9792 | 0.9803 | 1.91 | 68 | 33 |
Accuracy | Precision | Recall | F1-Score | Param (M) | Flops | |
---|---|---|---|---|---|---|
Baseline | 0.9266 | 0.9263 | 0.9319 | 0.9281 | 1.89 | 302.1M |
+CA | 0.9388 | 0.9413 | 0.9423 | 0.9413 | 1.91 | 304.6M |
+Double Fusion | 0.9541 | 0.9605 | 0.956 | 0.9576 | 1.91 | 304.6M |
+DS Block | 0.9785 | 0.9817 | 0.9792 | 0.9803 | 1.91 | 309.1M |
Model | Auccary | Precision | Recall | F1-Score | Params (M) | Flops |
---|---|---|---|---|---|---|
VGG16 [42] | 0.9663 | 0.9656 | 0.9655 | 0.9643 | 134.29 | 15.50G |
ResNet50 [39] | 0.9327 | 0.9341 | 0.9359 | 0.9338 | 23.51 | 4.12G |
EffcientNet-B0 [43] | 0.9724 | 0.9738 | 0.9748 | 0.9736 | 40.09 | 398.02M |
ConvNeXt-B [38] | 0.9418 | 0.9430 | 0.9460 | 0.9440 | 89.00 | 15.40G |
DenseNet121 [44] | 0.9571 | 0.9560 | 0.9591 | 0.9568 | 6.96 | 2.88G |
MobileNet-V2 [40] | 0.9541 | 0.9580 | 0.9576 | 0.9571 | 2.22 | 318.96M |
MobileNetV3-Large [45] | 0.9633 | 0.9620 | 0.9665 | 0.9638 | 4.20 | 226.43M |
ShuffleNetV2-1.0× [46] | 0.9480 | 0.9501 | 0.9528 | 0.9508 | 1.26 | 149.57M |
DFCANet | 0.9847 | 0.9853 | 0.9853 | 0.9853 | 1.91 | 309.1M |
Method | Plant | Accuracy/% | Ref |
---|---|---|---|
SVM | Corn | 83.7% | Aravind et al. [4] |
SVM | Corn | 83.7% | Budiarianto et al. [5] |
Improved LeNet | Corn | 97.89% | Ramar et al. [6] |
Improved CNN | Corn | 98.78% | Panigrahi et al. [7] |
CNN | Corn | 88.46% | Mishra et al. [9] |
DFCANet | Corn | 99.74% | - |
GoogleNet | Plantvillage | 99.35% | Mohanty et al. [8] |
VGG16 | Plantvillage | 97.82% | Mohameth et al. [50] |
NasNet | Plantvillage | 99.15% | Huang et al. [51] |
DFCANet | Plantvillage | 99.58% | - |
Model | Accuracy | Precision | Recall | F1-Score | Params (M) | Flops |
---|---|---|---|---|---|---|
VGG16 [42] | 0.9529 | 0.9632 | 0.9528 | 0.9530 | 134.29 | 15.50G |
ResNet50 [39] | 0.9745 | 0.9739 | 0.9746 | 0.9743 | 23.51 | 4.12G |
EffcientNet-B0 [43] | 0.9847 | 0.9850 | 0.9846 | 0.9846 | 40.09 | 398.02M |
ConvNeXt-B [38] | 0.9477 | 0.9473 | 0.9483 | 0.9476 | 89.00 | 15.40G |
DenseNet121 [44] | 0.9681 | 0.9680 | 0.9683 | 0.9680 | 6.96 | 2.88G |
MobileNet-V2 [40] | 0.9757 | 0.9756 | 0.9756 | 0.9756 | 2.22 | 318.96M |
MobileNetV3-Large [45] | 0.9719 | 0.9730 | 0.9716 | 0.9720 | 4.20 | 226.43M |
ShuffleNetV2-1.0× [46] | 0.9808 | 0.981 | 0.9813 | 0.981 | 1.26 | 149.57M |
DFCANet | 0.9923 | 0.9926 | 0.9923 | 0.9923 | 1.91 | 309.1M |
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Chen, Y.; Chen, X.; Lin, J.; Pan, R.; Cao, T.; Cai, J.; Yu, D.; Cernava, T.; Zhang, X. DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification. Agriculture 2022, 12, 2047. https://doi.org/10.3390/agriculture12122047
Chen Y, Chen X, Lin J, Pan R, Cao T, Cai J, Yu D, Cernava T, Zhang X. DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification. Agriculture. 2022; 12(12):2047. https://doi.org/10.3390/agriculture12122047
Chicago/Turabian StyleChen, Yang, Xiaoyulong Chen, Jianwu Lin, Renyong Pan, Tengbao Cao, Jitong Cai, Dianzhi Yu, Tomislav Cernava, and Xin Zhang. 2022. "DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification" Agriculture 12, no. 12: 2047. https://doi.org/10.3390/agriculture12122047
APA StyleChen, Y., Chen, X., Lin, J., Pan, R., Cao, T., Cai, J., Yu, D., Cernava, T., & Zhang, X. (2022). DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification. Agriculture, 12(12), 2047. https://doi.org/10.3390/agriculture12122047