Table 9 Performance comparison of MDDM-WD with existing studies.
From: Deep learning framework using UAV imagery for multi-disease detection in cereal crops
Citation | Model | Classification | Image Complexity | Performance Accuracy |
|---|---|---|---|---|
Bukhari, H. R. et al.45 | Resnet-18 + U2 Net | Multiclass | Uniform background | 96.19% |
Shi, Y. et al.46 | CNN with Fourier blocks | Binary | Sentinel‑2 time-series under field conditions | 91–92% |
Hayıt, T. et al.47 | CNN‑CGLCM_HSV + SVM | Multiclass | Single leaf with uniform background | 92.4% |
Proposed MDDM-WD | Pre-trained VGG-16 using transfer learning + SVM | Multiclass | Real-time, multi-leaf, complex background UAV imagery | 97% |
