Table 1 The application of ML-assisted diagnosis of plant viral diseases
Plant | Viruses/viral diseases | Algorithms/methodologies used | Accuracy | References |
|---|---|---|---|---|
Cassava | Cassava mosaic disease | Convolutional neural networks (CNN) | 96% | [31] |
Cassava brown streak disease | 98% | Â | ||
Cucumber | (i) Melon yellow spot virus; (ii) Zucchini yellow mosaic virus | CNN | 94.9% | [30] |
Mungbean | Yellow mosaic disease | CNN | 91.234% for VirLeafNet-1 | [37] |
96.429% for VirLeafNet-2 | ||||
97.403% for VirLeafNet-3 | ||||
Potato | Potato virus Y | Support vector machine (SVM) classifier | 89.8% | [36] |
Sweet pepper | Tomato spotted wilt virus (TSWV) | Outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN) | 96.25% (before the onset of visible symptoms) | [32] |
Tobacco | Tobacco mosaic virus (TMV) | Successive projections algorithm (SPA) with extreme learning machine (ELM) classifier | 98.33% | [34] |
Tobacco | TMV | SVM | 93.5% on the training set | [41] |
92.7% on the independent set | ||||
Tobacco | TSWV | Model by boosted regression tree (BRT) algorithm and Wavelength selection by SPA | 85.2% | [35] |
Tobacco | Tomato leaf curl New Delhi virus and Tomato leaf curl Gujarat virus | CNN [Visual Geometry Group 16] | 97.21% | [38] |
Tomato | Groundnut bud necrosis virus (GBNV) | SVM | 97.8% | [33] |