Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks
Abstract
1. Introduction
2. Material and Methods
2.1. Dataset
2.2. Experimental Approach
2.3. Convolutional Neural Network Architecture
2.4. Transfer Learning
2.5. Data Augmentation
2.6. Proposed Ensemble Convolutional Neural Network
Algorithm 1: The detailed illustration of the algorithm. |
Input : Leaf Images using dataset D Output: Class prediction 1 Step 1: D is divided into training set () (60%), validation set ()(20%), test set () (20%) 2 Step 2: Pre-processing: 3 The input images are resized to 224 × 224 × 3 4 The input images are normalized 5 The data augmentation techniques are applied 6 Step 3: Training 7 foreach 8 l = 0.001 9 for epochs = 1 to 100 do 10 Update the parameters of the model n 11 foreach mini batch () ∈ () do 12 if the test accuracy is not improving for 10 epochs then 13 l = l × 0.2 14 end 15 end 16 end 17 end 18 Step 4: 19 foreach do 20 ensemble the output of all networks 21 end |
2.7. Evaluation Metrics
- Accuracy is defined as ;
- Precision is defined as ;
- Recall is defined as ;
- F1 score is defined as ;
3. Experimental Setup
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ML | Machine Learning |
DL | Deep Learning |
ANN | Artificial Neural Network |
DSS | Decision Support System |
SVM | Support Vector Machine |
ECNN | Ensemble convolutional neural network |
RF | Random Forest |
CNN | Convolutional Neural Network |
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DiaMOS Plant Dataset [23] | |
---|---|
Plant | Pear |
Cultivar | Septoria Piricola |
Type of data | RGB Images |
ROI (Region of Interest) captured | leaf, fruit |
Total size | 3505 images (3006 leaves images + 499 fruit images) |
Data Accessibility | https://doi.org/10.5281/zenodo.5557313 Date: 16 January 2023 |
Application | The images are suitable for different machine and |
deep learning tasks such as images detection | |
and classification. |
Smartphone Camera | DSRL Camera | |
---|---|---|
Image size | 2976 × 3968 | 3456 × 5184 |
Model device | Honor 6× | Canon EOS 60D |
Focal length | 3.83 mm | 50 mm |
Focal ratio | f/2.2 | f/4.5 |
Color space | RGB | RGB |
Leaf Disease | Size |
---|---|
Healthy | 43 |
Spot | 884 |
Curl | 54 |
Slug | 2025 |
Total | 3006 |
Accuracy (%) | ||||
---|---|---|---|---|
CNN | Optimizer | Train | Validation | Test |
EfficientNetB0 | RMSprop | 81.13 | 82.82 | 83.38 |
Adam | 89.02 | 86.33 | 86.05 | |
InceptionV3 | RMSprop | 81.96 | 79.66 | 82.72 |
Adam | 84.44 | 80.29 | 83.39 | |
MobileNetV2 | RMSprop | 85.38 | 81.12 | 83.06 |
Adam | 87.70 | 83.83 | 84.05 | |
VGG19 | RMSprop | 72.42 | 71.68 | 73.75 |
Adam | 76.66 | 76.53 | 75.75 |
CNN | Optimizer | Precision (%) | Recall (%) | F1-Score (%) |
---|---|---|---|---|
EfficientNetB0 | RMSprop | 81.14 | 83.38 | 82.23 |
Adam | 84.42 | 86.04 | 85.03 | |
InceptionV3 | RMSprop | 80.21 | 82.72 | 81.45 |
Adam | 81.14 | 83.38 | 82.23 | |
MobileNetV2 | RMSprop | 81.35 | 83.05 | 82.07 |
Adam | 82.37 | 84.05 | 83.06 | |
VGG19 | RMSprop | 70.47 | 73.75 | 71.76 |
Adam | 72.71 | 75.74 | 74.05 |
Test Accuracy—Weighted Average | ||||
---|---|---|---|---|
Ensemble CNNS | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
EfficientNetB0 + InceptionV3 | 91.14 | 89.84 | 90.02 | 89.93 |
EfficientNetB0 + MobileNetV2 | 86.21 | 84.13 | 85.51 | 84.82 |
InceptionV3 + MobileNetV2 | 85.35 | 83.02 | 85.14 | 84.08 |
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Fenu, G.; Malloci, F.M. Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks. AgriEngineering 2023, 5, 141-152. https://doi.org/10.3390/agriengineering5010009
Fenu G, Malloci FM. Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks. AgriEngineering. 2023; 5(1):141-152. https://doi.org/10.3390/agriengineering5010009
Chicago/Turabian StyleFenu, Gianni, and Francesca Maridina Malloci. 2023. "Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks" AgriEngineering 5, no. 1: 141-152. https://doi.org/10.3390/agriengineering5010009
APA StyleFenu, G., & Malloci, F. M. (2023). Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks. AgriEngineering, 5(1), 141-152. https://doi.org/10.3390/agriengineering5010009