Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Collection
2.2. Image Pre-Processing and Augmentation
2.3. Convolutional Neural Network (CNN)
2.4. Inception V3 and Inception ResNet V2 Models
2.5. Evaluation
3. Results
4. Performance Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Disease Name | Images from PlantVillage (Number) | Images Captured from the Field (Number) |
---|---|---|---|
0 | Early Blight | 1000 | 814 |
1 | Yellow Leaf Curl Virus | 938 | 544 |
2 | Healthy | 1591 | 338 |
Total | 3529 | 1696 |
Class | Description |
---|---|
Early Blight |
|
Yellow Leaf Curl Virus |
|
Parameter | Value |
---|---|
Batch Size | 32 |
Dropout | 5%, 10%, 15%, 20%, 25%, 30%, 40%, and 50% |
Activation function | ReLU, SoftMax |
Optimizer | Adam |
CNN Training | |
Epoch | 15 |
Learning Rate | 0.001 |
Fine-tuning | |
Epoch | 25 |
Learning Rate | 0.00001 |
Type of Layer | Inception V3 | Inception ResNet V2 | ||
---|---|---|---|---|
Output Shape | Parameters | Output Shape | Parameters | |
CNN Training | ||||
Input | (299, 299, 3) | 0 | (299, 299, 3) | 0 |
Sequential | (299, 299, 3) | 0 | (299, 299, 3) | 0 |
Functional (Inception V3, Inception ResNet V2) | (8, 8, 2048) | 21,802,784 | (8, 8, 1536) | 54,336,736 |
Average Pooling | (0, 2048) | 0 | (0, 1536) | 0 |
Dropout | (0, 2048) | 0 | (0, 1536) | 0 |
Dense | (0, 3) | 6147 | (0, 3) | 4611 |
Total parameters | 21,808,931 | 54,341,347 | ||
Trainable parameters | 6147 | 4611 | ||
Non-trainable parameters | 21,802,784 | 54,336,736 | ||
Fine-Tuning | ||||
Trainable parameters | 16,344,963 | 41,357,763 | ||
Non-trainable parameters | 5,463,968 | 12,983,584 |
Drop Out (%) | Train | Validation | Test | |||
---|---|---|---|---|---|---|
Accuracy (%) | Loss | Accuracy (%) | Loss | Accuracy (%) | Loss | |
Inception V3 model | ||||||
5 | 99.33 | 0.0166 | 97.56 | 0.0624 | 97.85 | 0.0685 |
10 | 99.57 | 0.0103 | 98.12 | 0.0466 | 98.83 | 0.0460 |
15 | 99.95 | 0.0034 | 99.06 | 0.0311 | 98.83 | 0.0342 |
20 | 99.55 | 0.0107 | 97.75 | 0.0446 | 99.02 | 0.0366 |
25 | 99.90 | 0.0046 | 98.87 | 0.0321 | 98.63 | 0.0411 |
30 | 99.81 | 0.0059 | 98.69 | 0.0361 | 98.63 | 0.0234 |
40 | 99.83 | 0.0061 | 98.12 | 0.0332 | 98.63 | 0.0308 |
50 | 99.78 | 0.0063 | 98.69 | 0.0252 | 99.22 | 0.0318 |
Inception ResNet V2 model | ||||||
5 | 99.83 | 0.0046 | 98.31 | 0.0598 | 99.02 | 0.0438 |
10 | 99.88 | 0.0037 | 98.87 | 0.0314 | 98.83 | 0.0322 |
15 | 99.93 | 0.0022 | 98.87 | 0.0277 | 99.22 | 0.0309 |
20 | 99.93 | 0.0021 | 98.69 | 0.0392 | 98.83 | 0.0396 |
25 | 99.78 | 0.0065 | 98.69 | 0.0457 | 99.22 | 0.0522 |
30 | 99.95 | 0.0024 | 99.06 | 0.0398 | 99.02 | 0.0495 |
40 | 99.40 | 0.0128 | 98.50 | 0.0632 | 98.83 | 0.0636 |
50 | 99.83 | 0.0058 | 98.50 | 0.0379 | 99.02 | 0.0467 |
No. | Author (s) | Method | Image Number | Image Source | Accuracy (%) |
---|---|---|---|---|---|
1 | Agarwal et al. (2020) [25] | CNN network | 17,500 | PlantVillage | 91.20 |
2 | Prajwala Tm et al. (2018) [26] | LeNet based CNN | 18,160 | PlantVillage | 95 |
3 | Widiyanto et al. (2019) [23] | CNN model | 5000 | PlantVillage | 96.60 |
4 | Keke Zhang et al. (2018) [22] | ResNet | 5550 | PlantVillage | 97.28 |
5 | Karthik et al. (2020) [30] | Attention-based Residual CNN | 95,999 | PlantVillage | 98 |
6 | Abbas et al. (2021) [21] | DenseNet, C-GAN | 16,012 | PlantVillage | 99.51 |
7 | Proposed approach | Inception V3 and Inception ResNet V2 | 5225 | PlantVillage and field | 99.22 |
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Saeed, A.; Abdel-Aziz, A.A.; Mossad, A.; Abdelhamid, M.A.; Alkhaled, A.Y.; Mayhoub, M. Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks. Agriculture 2023, 13, 139. https://doi.org/10.3390/agriculture13010139
Saeed A, Abdel-Aziz AA, Mossad A, Abdelhamid MA, Alkhaled AY, Mayhoub M. Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks. Agriculture. 2023; 13(1):139. https://doi.org/10.3390/agriculture13010139
Chicago/Turabian StyleSaeed, Alaa, A. A. Abdel-Aziz, Amr Mossad, Mahmoud A. Abdelhamid, Alfadhl Y. Alkhaled, and Muhammad Mayhoub. 2023. "Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks" Agriculture 13, no. 1: 139. https://doi.org/10.3390/agriculture13010139
APA StyleSaeed, A., Abdel-Aziz, A. A., Mossad, A., Abdelhamid, M. A., Alkhaled, A. Y., & Mayhoub, M. (2023). Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks. Agriculture, 13(1), 139. https://doi.org/10.3390/agriculture13010139