Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection
Abstract
:1. Introduction
2. Literature Review
- Employing three compact CNNs with dissimilar structures involving ResNet-18, ShuffleNet, and MobileNet to extract deep features via TL.
- Retrieving deep features out of the final fully connected (FC) layer for each CNN before the softmax layer and obtaining a lower number of deep features from this FC layer compared to earlier layers.
- Blending deep features obtained from the three CNNs to merge benefits of every CNN construction.
- Utilizing a hybrid FS approach for selection among merged deep features diminishes their dimensionality.
- Using three search policies to choose among these combined features results in selecting only the most significant.
3. Materials and Methods
3.1. Tomato Diseases Dataset Acquisition
3.2. Proposed Tomato Leaf Disease Classification Pipeline
3.2.1. Tomato Leaf Image Preparation
3.2.2. Compact CNNs Retraining and Feature Extraction with TL
3.2.3. Feature Concatenation and Selection
3.2.4. Classification
4. Pipeline Evaluation and Networks’ Hyperparameters Setting
4.1. Pipeline Results Evaluation
4.2. Networks Hyperparameters Setting
5. Results
5.1. Context 1 Classification Performance
5.2. Context 2 Classification Performance
5.3. Context 3 Classification Performance
6. Discussion
6.1. Comparative Analysis
6.2. Limitations and Forthcoming Directions
7. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Class Label | Number of Images |
---|---|
Bacterial Spot | 2127 |
Early Blight | 1000 |
Healthy | 1591 |
Late Blight | 1909 |
Leaf Mold | 952 |
Mosaic virus | 373 |
Septoria Leaf Spot | 1771 |
Two Spotted Spider Mites | 1676 |
Target Spot | 1405 |
Yellow Leaf Curl Virus | 3209 |
Accuracy | Sensitivity | Specificity | Precision | F1-Score | MCC | |
---|---|---|---|---|---|---|
ResNet-18 Features | ||||||
NB | 98.5 | 98.2 | 99.8 | 98.5 | 98.5 | 98.3 |
LDA | 97.3 | 97.3 | 97.0 | 97.4 | 97.3 | 96.9 |
QDA | 98.9 | 98.9 | 99.9 | 98.9 | 98.9 | 98.8 |
LSVM | 99.34 | 99.3 | 99.9 | 99.3 | 99.3 | 99.3 |
KNN | 99.34 | 99.3 | 99.9 | 99.3 | 99.3 | 99.3 |
DT | 99.05 | 99.1 | 99.1 | 99.1 | 98.9 | 99.6 |
ShuffleNet Features | ||||||
NB | 99.21 | 99.2 | 99.9 | 99.2 | 99.2 | 99.1 |
LDA | 99.33 | 99.3 | 99.9 | 99.3 | 99.3 | 99.2 |
QDA | 99.48 | 99.5 | 99.9 | 99.5 | 99.5 | 99.4 |
LSVM | 99.53 | 99.5 | 99.9 | 99.5 | 99.5 | 99.5 |
KNN | 99.60 | 99.6 | 100 | 99.6 | 99.6 | 99.6 |
DT | 99.17 | 99.2 | 99.9 | 99.2 | 99.2 | 99.1 |
MobileNet Features | ||||||
NB | 99.28 | 99.3 | 99.9 | 99.3 | 99.3 | 99.2 |
LDA | 99.24 | 99.3 | 99.9 | 99.3 | 99.3 | 99.2 |
QDA | 99.47 | 99.5 | 99.9 | 99.5 | 99.5 | 99.4 |
LSVM | 99.56 | 99.6 | 99.9 | 99.6 | 99.6 | 99.5 |
KNN | 99.67 | 99.7 | 100 | 99.7 | 99.7 | 99.6 |
DT | 99.18 | 99.2 | 99.9 | 99.2 | 99.2 | 99.1 |
Sensitivity | Specificity | Precision | F1-Score | MCC | |
---|---|---|---|---|---|
NB | 99.7 | 100 | 99.7 | 99.7 | 99.7 |
LDA | 99.7 | 100 | 99.7 | 99.7 | 99.7 |
QDA | 99.6 | 99.9 | 99.6 | 99.6 | 99.5 |
LSVM | 99.9 | 100 | 99.9 | 99.9 | 99.8 |
KNN | 99.9 | 100 | 99.9 | 99.9 | 99.9 |
DT | 99.1 | 99.9 | 99.1 | 99.1 | 99.0 |
# Features | Accuracy | Sensitivity | Specificity | Precision | F1-Score | MCC | |
---|---|---|---|---|---|---|---|
Forward Search Methodology | |||||||
NB | 21 | 99.80 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
LDA | 21 | 99.80 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
QDA | 15 | 99.80 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
LSVM | 24 | 99.90 | 99.9 | 100 | 99.9 | 99.9 | 99.8 |
KNN | 22 | 99.90 | 99.9 | 100 | 99.9 | 99.9 | 99.9 |
DT | 14 | 99.34 | 99.3 | 99.9 | 99.3 | 99.3 | 99.3 |
Backward Search Methodology | |||||||
NB | 19 | 99.81 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
LDA | 24 | 99.81 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
QDA | 16 | 99.92 | 99.9 | 100 | 99.9 | 99.9 | 99.8 |
LSVM | 16 | 99.8 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
KNN | 24 | 99.91 | 99.9 | 100 | 99.9 | 99.9 | 99.9 |
DT | 14 | 99.34 | 99.3 | 99.9 | 99.3 | 99.3 | 99.3 |
Bidirectional Search Methodology | |||||||
NB | 21 | 99.81 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
LDA | 19 | 99.81 | 99.8 | 100 | 99.8 | 99.8 | 99.8 |
QDA | 16 | 99.92 | 99.9 | 100 | 99.9 | 99.9 | 99.9 |
LSVM | 24 | 99.90 | 99.9 | 100 | 99.9 | 99.9 | 99.8 |
KNN | 22 | 99.92 | 99.9 | 100 | 99.9 | 99.9 | 99.9 |
DT | 14 | 99.36 | 99.4 | 99.9 | 99.4 | 99.4 | 99.3 |
Article | # Diseases | Model | Features | Accuracy |
---|---|---|---|---|
[51] | 10 | ResNet-50 | Features of ResNet-50 | 97.0% |
[28] | 10 | U-Net | ResNet-50 | 97.11% |
[3] | 10 | Customized CNN | Customized CNN | 99.3% |
[21] | 10 | Customized CNN | Customized CNN | 98.70% |
[52] | 4 | Fine-tuned MobileNet | Features of MobileNet | 90.3% |
[53] | 10 | Spatial attention with CNN | Fully connected layer | 95.20% |
[54] | 7 | VGG16 | Features of VGG16 | 96.19% |
[2] | 6 | Multinomial Logistic regression | MobileNetV2 or NASNetMobile | 97% |
[55] | 10 | EfficientNet-B0 | EfficientNet-B0 | 98.60% |
Proposed | 10 | KNN | Fully connected layer (MobileNet + ShuffleNet + ResNet-18) + hybrid FS | 99.92% |
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Attallah, O. Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection. Horticulturae 2023, 9, 149. https://doi.org/10.3390/horticulturae9020149
Attallah O. Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection. Horticulturae. 2023; 9(2):149. https://doi.org/10.3390/horticulturae9020149
Chicago/Turabian StyleAttallah, Omneya. 2023. "Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection" Horticulturae 9, no. 2: 149. https://doi.org/10.3390/horticulturae9020149