An Efficient Hybrid CNN Classification Model for Tomato Crop Disease
Abstract
:1. Introduction
- A hybrid-enhanced CNN model is proposed for tomato disease identification. An inception block was added to the VGG16 model in order to take use of the capabilities of simultaneous multiscale feature extraction. The hybrid CNN model has powerful feature extraction qualities and uses these capabilities.
- The effectiveness of the proposed hybrid CNN model was analyzed through rigorous high-level simulations. The results obtained from the developed hybrid CNN model were compared against the most recent and state-of-the-art models.
2. Related Work
3. Proposed System
3.1. Dataset Description
3.2. Data Augmentation
3.3. Split Dataset
3.4. Hybrid CNN Model for Tomato Crop Disease
3.5. Implementation Specification
3.6. Performance Metrics
4. Results
4.1. Training Loss and Accuracy
4.2. Evaluation of Models on the Test Dataset
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
AUC | Area Under the Curve |
CBAM | Convolutional Attention Module |
CNN | Convolutional Neural Network |
DL | Deep learning |
FAO | Food and Agriculture Organization of the United Nations |
MDPI | Multidisciplinary Digital Publishing Institute |
ROC | Receiver Operating Characteristic Curve |
SVM | Support Vector Machine |
VGG | Visual Geometry Group |
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Reference | Algorithm | Accuracy (%) |
---|---|---|
[18] | VGG16 AlexNet | 97.29 97.49 |
[19] | Modified VGG16 | 98.40 |
[20] | CNN model | 91.20 |
[21] | CNN-SVM-CBAM | 97.20 |
[22] | CNN model | 96.55 |
[23] | Restructured residual dense network | 95.00 |
[24] | DensNet161 DensNet121 VGG16 | 95.65 94.93 90.58 |
[25] | CNN model | 98.49 |
[26] | GoogleNet AlexNet | 99.18 98.66 |
[27] | VGGNet LeNet ResNet50 Xception | 99.25 96.27 98.65 98.13 |
[28] | Xception NasNetMobile MobileNetV2 MobileNetV3 | 100.00 084.00 075.00 098.00 |
[29] | InceptionV3 GoogleNet AlexNet ResNet50 ResNet18 | 98.65 99.39 98.93 99.15 99.06 |
Parameter | Value |
---|---|
Random rotation | [+12, −12] |
Width shift | [0.6, 1.1] |
Zoom | [0.5, 0.9] |
Fill mode | Nearest |
Horizontal flip | True |
Height shift | 0.15 |
Shearing transformation | 0.25 |
Vertical flip | True |
Categories | Number of Original Images | Training Images | Training Images after Augmentation | Validation Images | Test Images |
---|---|---|---|---|---|
Early Blight | 1000 | 810 | 4050 | 90 | 100 |
Target Spot | 1404 | 1138 | 5688 | 126 | 140 |
Mosaic Virus | 373 | 302 | 1508 | 34 | 38 |
Septoria Leaf Spot | 1771 | 1434 | 7169 | 159 | 178 |
Late Blight | 1909 | 1547 | 7736 | 172 | 190 |
Healthy | 1591 | 1288 | 6440 | 143 | 160 |
Spider Mites | 1676 | 1357 | 6786 | 151 | 168 |
Bacterial Spot | 2127 | 1724 | 8618 | 192 | 212 |
Leaf Mold | 952 | 770 | 3852 | 86 | 96 |
Yellow Leaf Curl Virus | 5357 | 4340 | 21,699 | 482 | 535 |
Total Images | 18,160 | 14,709 | 73,544 | 1634 | 1817 |
Parameter | Value |
---|---|
Optimizer | Adam |
Batch size | 16 |
Loss function | Cross-entropy |
Epochs | 30 |
Learning rate | 0.0001 |
Performance Metrics (%) | InceptionResNet | ResNet152 | Hybrid CNN |
---|---|---|---|
Training accuracy | 99.69 | 99.45 | 99.83 |
Testing accuracy | 98.40 | 97.30 | 99.17 |
Precision | 98.27 | 97.19 | 99.13 |
Recall | 98.24 | 97.09 | 99.23 |
F1-score | 98.23 | 96.95 | 99.17 |
AUC | 99.03 | 98.39 | 99.56 |
Model | Categories | Precision | Recall | F1-Score |
---|---|---|---|---|
InceptionResNet | Bacterial Spot | 0.9722 | 0.9906 | 0.9813 |
Healthy | 0.9816 | 1.0000 | 0.9907 | |
Mosaic Virus | 1.0000 | 0.9737 | 0.9867 | |
Two Spotted Spider Mites | 0.9939 | 0.9762 | 0.9850 | |
Late Blight | 0.9840 | 0.9737 | 0.9788 | |
Early Blight | 0.9524 | 1.0000 | 0.9756 | |
Septoria Leaf Spot | 0.9570 | 1.0000 | 0.9780 | |
Leaf Mold | 0.9897 | 1.0000 | 0.9948 | |
Yellow Leaf Curl Virus | 0.9962 | 0.9888 | 0.9925 | |
Target Spot | 1.0000 | 0.9214 | 0.9591 | |
ResNet152 | Bacterial Spot | 0.9636 | 1.0000 | 0.9815 |
Healthy | 0.8556 | 1.0000 | 0.9222 | |
Mosaic Virus | 1.0000 | 1.0000 | 1.0000 | |
Two Spotted Spider Mites | 0.9937 | 0.9405 | 0.9664 | |
Late Blight | 0.9844 | 0.9947 | 0.9895 | |
Early Blight | 0.9434 | 1.0000 | 0.9709 | |
Septoria Leaf Spot | 0.9778 | 0.9888 | 0.9832 | |
Leaf Mold | 1.0000 | 1.0000 | 1.0000 | |
Yellow Leaf Curl Virus | 1.0000 | 0.9850 | 0.9925 | |
Target Spot | 1.0000 | 0.8000 | 0.8889 | |
Hybrid CNN | Bacterial Spot | 0.9770 | 1.0000 | 0.9883 |
Healthy | 0.9938 | 1.0000 | 0.9969 | |
Mosaic Virus | 1.0000 | 1.0000 | 1.0000 | |
Two Spotted Spider Mites | 0.9881 | 0.9881 | 0.9881 | |
Late Blight | 0.9845 | 1.0000 | 0.9922 | |
Early Blight | 0.9804 | 1.0000 | 0.9901 | |
Septoria Leaf Spot | 0.9888 | 0.9944 | 0.9916 | |
Leaf Mold | 1.0000 | 1.0000 | 1.0000 | |
Yellow Leaf Curl Virus | 1.0000 | 0.9907 | 0.9953 | |
Target Spot | 1.0000 | 0.9500 | 0.9744 |
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Sanida, M.V.; Sanida, T.; Sideris, A.; Dasygenis, M. An Efficient Hybrid CNN Classification Model for Tomato Crop Disease. Technologies 2023, 11, 10. https://doi.org/10.3390/technologies11010010
Sanida MV, Sanida T, Sideris A, Dasygenis M. An Efficient Hybrid CNN Classification Model for Tomato Crop Disease. Technologies. 2023; 11(1):10. https://doi.org/10.3390/technologies11010010
Chicago/Turabian StyleSanida, Maria Vasiliki, Theodora Sanida, Argyrios Sideris, and Minas Dasygenis. 2023. "An Efficient Hybrid CNN Classification Model for Tomato Crop Disease" Technologies 11, no. 1: 10. https://doi.org/10.3390/technologies11010010