Table 9.
Summary of different classification techniques.
| S. No. | Year and Reference | Classification Technique Used |
|---|---|---|
| 1. | 2010 [2] | SVM |
| 2. | 2012 [35] | Backpropagation networks |
| 3. | 2012 [3] | Multi-class SVM |
| 4. | 2013 [72] | Spectral disease indices |
| 5. | 2013 [37] | Feed-forward back propagation neural network |
| 6. | 2016 [38] | Two support vector machines (serial combination) |
| 7. | 2016 [9] | CNN |
| 8. | 2017 [41] | SqueezeNet, AlexNet |
| 9. | 2017 [10] | CNN |
| 10. | 2017 [42] | AlexNet |
| 11. | 2018 [61] | CNN models |
| 12. | 2018 [4] | Random forest |
| 13. | 2018 [13] | Deep CNN |
| 14. | 2018 [14] | CNN model based on LVQ |
| 15. | 2018 [5] | SVM |
| 16. | 2019 [16] | Deep CNN |
| 17. | 2019 [62] | CNN |
| 18. | 2019 [17] | Convolutional neural network with global average pooling |
| 19. | 2019 [7] | SVM |
| 20. | 2019 [15] | NASNet |
| 21. | 2019 [64] | Deep CNN |
| 22. | 2019 [46] | CNN |
| 23. | 2019 [47] | ANN and SVM |
| 24. | 2020 [20] | CNN |
| 25. | 2021 [24] | AlexNet and GoogleNet |
| 26. | 2021 [26] | DM deep learning optimizer |
| 27. | 2020 [22] | CNN |
| 28. | 2021 [73] | CNN and convolutional autoencoders |
| 29. | 2021 [28] | DenseNet |
| 30. | 2021 [31] | VGG, DenseNet, and ResNet |
| 31. | 2021 [32] | GoogleNet, VGG16 |
| 32. | 2021 [27] | SVM, stochastic gradient descent, and random forest (machine learning) Inception-v3, VGG-16, and VGG-19 (deep learning) |
| 33. | 2022 [66] | Optimal mobile network-based CNN |