Table 7.
Chilli vegetable classification using AI.
| References | AI Methods | Dataset | Disease | Accuracy |
|---|---|---|---|---|
| (Naik et al., 2022) | 12 pre-trained DL networks | Plant leaf datasets from 43 different groups | Down curl, gemini virus, cercospora, leaf spot, yellow leaf, and up curl | SECNN=99.28% |
| (Ahmad Loti et al., 2021) | SVM, RF, and ANN | Self-collected data | Bacterial, cercospora, mosaic, mottle virus, leaf curl, healthy, aphids-infestation, and whitefly-infestation | SVM=92.10% |
| (Sachdeva et al., 2021) | DCNN with Bayesian | PlantVillage | Black spot, botrytis blight, leaf spot, powdery mildew, and rust spores | DCNN=98.9% |
| (Aminuddin et al., 2022) | CNN, ResNet-18 | Self-collected data | Healthy and diseased chilli leaf | CNN=97% |
| (Mustafa et al., 2023) | Optimized-CNN | Augmented dataset with 20000 images | Health status of chilli plant leaves | CNN=99.99% |
| (Karadağ et al., 2020) | ANN, NB, and KNN | 80 leaves samples collected by GATEAM Turkey | Healthy, fusarium, mycorrhizal fungus, combine fusarium, and mycorrhizal | KNN=100% |
| (Kanaparthi and Ilango, 2023) | Optimized-SqueezeNet | Kaggle dataset | Gemni virus and mosaic | SqueezeNet =100% |
| (KM et al., 2023) | YOLOv5 | Kaggle dataset | Leaf spot and leaf curl | YOLOv5 = 75.64% |