Monitoring Tomato Leaf Disease through Convolutional Neural Networks
Abstract
:1. Introduction
2. Related works
3. Materials and Methods
3.1. Dataset Creation
3.2. Model Creation
3.3. Data Distribution
3.4. Model Creation
4. Results
4.1. Environmental Setup
4.2. Evaluation Metrics
4.3. Results and Discussion
4.3.1. Validation of the Proposed Model
4.3.2. Comparison of the Model
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Layers | Parameters |
---|---|
Conv2D | Filters: 128, kernel size: (3,3), activation: “relu”, input shape: (112,112,3) |
MaxPool2D | Pool size: (2,2) |
Conv2D | Filters: 64, kernel size: (3,3), activation: “relu” |
MaxPool2D | Pool size: (2,2) |
Conv2D | Filters: 32, kernel size: (3,3), activation: “relu” |
MaxPool2D | Pool size: (2,2) |
Conv2D | Filters: 16, kernel size: (3,3), activation: “relu” |
MaxPool2D | Pool size: (2,2) |
Dropout | Rate: 0.2 |
GlobalAveragePooling2D | |
Dense | Units: 10, activation: “softmax” |
Parameter | Value |
---|---|
Optimization algorithm | Adam |
Loss function | Categorical cross entropy |
Batch size | 32 |
Number of epochs | 200 |
Steps per epoch | 12,000 |
Validation steps | 3000 |
Activation function for conv layer | ReLu |
Class | Precision | Recall | F1 Score | Support |
---|---|---|---|---|
Tomato bacterial spot | 0.99 | 0.990 | 0.99 | 100 |
Tomato early blight | 1 | 1 | 0.98 | 100 |
Tomato late blight | 0.97 | 0.98 | 0.97 | 100 |
Tomato leaf mold | 1 | 1 | 0.99 | 100 |
Tomato Septoria leaf spot | 0.99 | 0.98 | 0.97 | 100 |
Tomato Two-spotted spider mite | 1 | 1 | 0.98 | 100 |
Tomato target spot | 0.99 | 0.98 | 0.99 | 100 |
Tomato yellow leaf curl virus | 0.98 | 0.98 | 0.98 | 100 |
Tomato mosaic virus | 1 | 1 | 0.99 | 100 |
Tomato healthy | 1 | 1 | 0.99 | 100 |
ResNet | VGG16Net | Inception-v3-Net | AlexNet | Proposed | |
---|---|---|---|---|---|
Trainable parameters (Millions) | 26.7 | 39.4 | 24.9 | 44.7 | 5.6 |
Model size (MB) | 98 | 128 | 92 | 133 | 36 |
Reference | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|
Widiyanto, et al. (2019) | 97.6 | 0.98 | 0.98 | 0.98 |
Afif Al Mamum et al. (2020) | 98.77 | 0.98 | 0.98 | 0.98 |
Kaur et al. (2019) | 98.8 | 0.98 | 0.98 | 0.98 |
Proposed model | 99.64 | 0.99 | 0.99 | 0.99 |
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Guerrero-Ibañez, A.; Reyes-Muñoz, A. Monitoring Tomato Leaf Disease through Convolutional Neural Networks. Electronics 2023, 12, 229. https://doi.org/10.3390/electronics12010229
Guerrero-Ibañez A, Reyes-Muñoz A. Monitoring Tomato Leaf Disease through Convolutional Neural Networks. Electronics. 2023; 12(1):229. https://doi.org/10.3390/electronics12010229
Chicago/Turabian StyleGuerrero-Ibañez, Antonio, and Angelica Reyes-Muñoz. 2023. "Monitoring Tomato Leaf Disease through Convolutional Neural Networks" Electronics 12, no. 1: 229. https://doi.org/10.3390/electronics12010229