Table 6.
Comparison of deep learning techniques for tomato leaf disease classification.
| Year | Ref | Networks | Dataset | Training | Testing | Accuracy |
|---|---|---|---|---|---|---|
| 2021 | 6 | DenseNet121 and C-GAN | PlantVillage | – | – | 98.65% |
| 2020 | 18 | DCGAN + CNN | PlantVillage | 1500 | – | 94.33% |
| 2018 | 28 | CNN | PlantVillage | 6300 | 2700 | 95.54% |
| 2017 | 46 | Faster RCNN + VGG16 + Resnet-X | Self-collected | 4000 | 1000 | 86% |
| 2020 | 30 | Attention Residual CNNs + Residual CNN | PlantVillage | 70% | 30% | 98% |
| 2019 | 47 | Multi-Scale + AlexNet | PlantVillage | 5766 | – | 92.70% |
| 2021 | 31 | VGG16 | PlantVillage | 54,303 | – | 95.71% |
| 2020 | 8 | Attention Residual CNNs + Residual CNN | PlantVillage | 70% | 30% | 98% |
| 2024 | Our Method | ResNet-152 + improved DCGAN | PlantVillage | 11,204 | 4808 | 99.69% |