ViT-RoT: Vision Transformer-Based Robust Framework for Tomato Leaf Disease Recognition
Abstract
:1. Introduction
- ViT-RoT, as shown in Figure 1, a novel benchmarking framework, is introduced to systematically evaluate the performance of ViT architectures in tomato leaf disease recognition.
- A comprehensive comparative and empirical analysis of multiple state-of-the-art ViT variants is conducted under consistent experimental settings. This enables an objective evaluation of each model’s capability in recognising complex disease patterns in tomato leaf images.
- Extensive performance benchmarking is conducted on three benchmark datasets using standard evaluation metrics to comprehensively assess the classification effectiveness of each ViT variant to classify images into high-, moderate-, and low-performing ViT variants. The results demonstrate that ConvNeXt-Small and Swin-Small consistently outperform all other ViT variants in tomato disease recognition.
2. Related Work
2.1. CNN-Based Approaches
2.2. ViT-Based Approaches
3. Proposed Method
3.1. LeafPrep—Preprocessing Pipeline
- RandomHorizontalFlip: Flips the image horizontally with a probability of 0.5.
- RandomVerticalFlip: Flips the image vertically with a probability of 0.5.
- RandomRotation: Applies random rotations within a specified angle range.
- RandomResizedCrop: Randomly crops and resizes the image to enhance spatial variability.
- ColorJitter: Adjusts brightness and contrast to introduce colour variations.
- Normalise: Standardises pixel values to zero mean and unit variance.
3.2. ViT Zoo—Model Variant Module
Algorithm 1 Tomato Leaf Disease Classification using ViT |
|
3.2.1. CCT
3.2.2. Swin Transformer
3.2.3. MobileViT
3.2.4. ConvNeXt–ViT
3.2.5. EfficientViT
3.3. AgriTrain—Training Strategy
Algorithm 2 AgriTrain—Supervised Training Strategy for ViT-RoT |
|
3.4. PlantScore—Evaluation Metrics
4. Experiments
4.1. Datasets
4.2. Research Setup
4.3. Results and Discussions
4.3.1. Top Performers
4.3.2. Moderate Performers
4.3.3. Low Performers
4.3.4. Performance Trends and Architectural Insights
- Efficiency vs. Accuracy: Optimised models like ConvNeXt-Small, Swin-Small, and EfficientViT-B0 outperform larger models (e.g., ViT-Base), suggesting that architectural efficiency is critical for the dataset’s moderate size.
- Hierarchical Attention: Hierarchical models (ConvNeXt, Swin) consistently outperform global attention models (ViT), leveraging localised feature extraction to handle noise and class imbalance effectively. This is evident in ConvNeXt-Small and Swin-Small’s top accuracies (0.9904).
- Lightweight Models: EfficientViT-B0 (0.9900) and MobileViT-XXSmall/XSmall (0.9855) achieve high accuracies with reduced computational demands, ideal for edge deployment in agricultural diagnostics.
- Training Stability: Lower losses correlate with higher accuracies (e.g., EfficientViT-B0: 0.0427, 0.9900; ConvNeXt-Small: 0.0542, 0.9904), except for EfficientViT-B2 (0.0834, 0.9779), indicating optimisation challenges.
- ConvNeXt: ConvNeXt-Small’s top performance (0.9904) stems from its modernised convolutional design with transformer-inspired elements, excelling in noisy and imbalanced data. ConvNeXt-Tiny (98.99%) reinforces this robustness.
- Swin Transformers: Swin-Small and Swin-Base leverage hierarchical shifted window-based attention, ideal for multi-scale feature extraction in mixed image conditions. Swin-Tiny (0.9885) highlights scalability.
- EfficientViT: EfficientViT-B0’s low loss and high accuracy reflect optimised multi-scale attention. EfficientViT-B2’s lower performance suggests scaling limitations.
- ViT: ViT-Small (0.9898) outperforms ViT-Base (98.83%) due to better generalisation, as larger ViTs risk overfitting on moderate-sized datasets.
- MobileViT and CCT: MobileViT-XXSmall/XSmall offer efficiency, but MobileViT-Small (0.9813) and CCT models (0.9791–0.9835) underperform due to limited capacity or high losses.
4.3.5. Efficiency and Practical Implications
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Hyperparameter | ViT | Swin | EfficientViT | MobileViT | ConvNeXt | CCT |
---|---|---|---|---|---|---|
Number of Epochs | 100 | 100 | 100 | 100 | 100 | 100 |
Batch Size | 32 | 32 | 16 | 32 | 32 | 32 |
Learning Rate | ||||||
Early Stopping Patience | 10 | 10 | 10 | 10 | 10 | 10 |
Early Stopping Delta | 0 | 0 | 0 | 0 | 0 | 0 |
Optimizer | AdamW | AdamW | AdamW | AdamW | AdamW | AdamW |
Loss Function | CE | CE | CE | CE | CE | LS-CE |
Image Size | 224 × 224 | 224 × 224 | 224 × 224 | 224 × 224 | 224 × 224 | 224 × 224 |
Mixed Precision | No | No | Yes | Yes | No | Yes |
Model | Year | Citation | Dataset | Accuracy |
---|---|---|---|---|
AlexNet | 2020 | [32] | PlantVillage | 98.93% |
GoogleNet | 2020 | [32] | PlantVillage | 99.39% |
Inception V3 | 2020 | [32] | PlantVillage | 98.65% |
ResNet 18 | 2020 | [32] | PlantVillage | 99.06% |
ResNet 50 | 2020 | [32] | PlantVillage | 99.15% |
DenseNet121 | 2021 | [47] | PlantVillage | 99.51% (5-class) |
DenseNet201 | 2021 | [48] | PlantVillage | 98.05% (10-class) |
VGG-19 | 2023 | [49] | Not standard | 98.27% |
MobileNet-V2 | 2023 | [49] | Not standard | 94.98% |
ResNet-50 | 2023 | [49] | Not standard | 99.53% |
Faster-RCNN (ResNet-34) | 2022 | [50] | PlantVillage | 99.97%, mAP 0.981 |
ViT | 2024 | [30] | PlantVillage | 90.99% |
Model | Epoch | Loss | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|---|---|
ViT Models | ||||||
ViT-Tiny | 17 | 0.05949 | 0.986710 | 0.986810 | 0.986710 | 0.986710 |
ViT-Small | 14 | 0.04904 | 0.98986 | 0.98986 | 0.98986 | 0.98986 |
ViT-Base | 11 | 0.05828 | 0.98838 | 0.98848 | 0.98838 | 0.98838 |
MobileViT Models | ||||||
MobileViT-XXSmall | 55 | 0.059710 | 0.985511 | 0.985711 | 0.985511 | 0.985511 |
MobileViT-XSmall | 38 | 0.066512 | 0.985511 | 0.985512 | 0.985511 | 0.985511 |
MobileViT-Small | 15 | 0.066111 | 0.981314 | 0.981314 | 0.981314 | 0.981314 |
EfficientViT Models | ||||||
EfficientViT-M5 | 48 | 0.05266 | 0.98838 | 0.98839 | 0.98838 | 0.98838 |
EfficientViT-B0 | 19 | 0.04271 | 0.99004 | 0.99004 | 0.98995 | 0.98995 |
EfficientViT-B2 | 44 | 0.083414 | 0.977916 | 0.977916 | 0.977916 | 0.977816 |
Swin Transformer Models | ||||||
Swin-Tiny | 13 | 0.05155 | 0.98857 | 0.98857 | 0.98857 | 0.98857 |
Swin-Small | 14 | 0.069713 | 0.99041 | 0.99042 | 0.99041 | 0.99041 |
Swin-Base | 8 | 0.04832 | 0.99033 | 0.99033 | 0.99033 | 0.99033 |
ConvNeXt Models | ||||||
ConvNeXt-Tiny | 5 | 0.04853 | 0.98995 | 0.99004 | 0.99004 | 0.99004 |
ConvNeXt-Small | 10 | 0.05427 | 0.99041 | 0.99051 | 0.99041 | 0.99041 |
CCT Models | ||||||
CCT-7×7×2×224 | 31 | 0.159016 | 0.979115 | 0.979115 | 0.979115 | 0.979015 |
CCT-14×7×2×224 | 21 | 0.143415 | 0.983513 | 0.983713 | 0.983513 | 0.983513 |
Model | Params (M) | FLOPs (B) | Average Inference Time (ms) |
---|---|---|---|
ViT-Tiny | 5.72 | 0.91 | 8.575 |
ViT-Small | 22.05 | 3.22 | 10.469 |
ViT-Base | 86.57 | 12.02 | 25.566 |
MobileViT-XXS | 1.27 | 0.25 | 3.965 |
MobileViT-XS | 2.32 | 0.66 | 4.734 |
MobileViT-S | 5.58 | 1.25 | 5.840 |
EfficientViT-B0 | 3.41 | 0.10 | 5.126 |
EfficientViT-M5 | 12.47 | 0.52 | 9.603 |
EfficientViT-B2 | 24.33 | 1.57 | 16.337 |
Swin-Tiny | 28.29 | 4.38 | 16.919 |
Swin-Small | 49.61 | 8.56 | 19.583 |
Swin-Base | 87.77 | 15.19 | 48.148 |
ConvNext-Tiny | 28.59 | 4.48 | 18.604 |
ConvNext-Small | 50.22 | 8.72 | 20.408 |
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Share and Cite
Nishankar, S.; Pavindran, V.; Mithuran, T.; Nimishan, S.; Thuseethan, S.; Sebastian, Y. ViT-RoT: Vision Transformer-Based Robust Framework for Tomato Leaf Disease Recognition. AgriEngineering 2025, 7, 185. https://doi.org/10.3390/agriengineering7060185
Nishankar S, Pavindran V, Mithuran T, Nimishan S, Thuseethan S, Sebastian Y. ViT-RoT: Vision Transformer-Based Robust Framework for Tomato Leaf Disease Recognition. AgriEngineering. 2025; 7(6):185. https://doi.org/10.3390/agriengineering7060185
Chicago/Turabian StyleNishankar, Sathiyamohan, Velalagan Pavindran, Thurairatnam Mithuran, Sivaraj Nimishan, Selvarajah Thuseethan, and Yakub Sebastian. 2025. "ViT-RoT: Vision Transformer-Based Robust Framework for Tomato Leaf Disease Recognition" AgriEngineering 7, no. 6: 185. https://doi.org/10.3390/agriengineering7060185
APA StyleNishankar, S., Pavindran, V., Mithuran, T., Nimishan, S., Thuseethan, S., & Sebastian, Y. (2025). ViT-RoT: Vision Transformer-Based Robust Framework for Tomato Leaf Disease Recognition. AgriEngineering, 7(6), 185. https://doi.org/10.3390/agriengineering7060185