Table 2.
Different object-based crop/plant classification methods
| Ref. | Method | Backbone | Plant types | Sensor | GSD (cm) | Altitude (m) | Average Precision (%) | Precision (%) | Recall (%) | F1-Score (%) | Inference time (ms) | FPS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [76] | Faster R-CNN | Inception-v2 | Banana plant | RGB | 1.78 | 40 | 99.3 | 96.4 | 97.82 | |||
| 2.03 | 50 | 97.9 | 85.1 | 91.05 | ||||||||
| 2.54 | 60 | 98.5 | 75.8 | 85.67 | ||||||||
| 40 + 50 | 98.3 | 99 | 98.64 | |||||||||
| 40 + 50 + 60 | 97.9 | 98.6 | 98.24 | |||||||||
| [9] | RetinaNet | ResNet | Ornamental plants | RGB | 73.41 | |||||||
| YOLOv3 | DarkNet-53 | 79.85 | ||||||||||
| [116] | YOLOv2 | Mango fruits | RGB | 1.5 − 2 | 86.4 | 96.1 | 89 | 92.41 | 80 | 40 | ||
| [54] | MangoYOLO | / | Mango fruits | RGB | 2 | 98.3 | 96.8 | 15 | 14 | |||
| YOLOv2 | DarkNet-19 | 95.9 | 93.3 | 20 | ||||||||
| YOLOv2-tiny | / | 95.3 | 91.7 | 10 | ||||||||
| YOLOv3 | DarkNet-53 | 96.7 | 95.1 | 25 | ||||||||
| SSD | VGG-16 | 98.3 | 95.9 | 70 | ||||||||
| Faster R-CNN | VGG | 95.3 | 94.5 | 67 | ||||||||
| Faster R-CNN | ZF | 95 | 93.9 | 37 | ||||||||
| [97] | Faster R-CNN | ResNet-50 | Trees | RGB | 0.82 | 20 − 40 | 82.48 | 163 | ||||
| YOLOv3 | DarkNet-53 | 85.88 | 26 | |||||||||
| RetinaNet | FPN | 92.64 | 67 | |||||||||
| [39] | YOLOv4 | Wheatears | RGB | 0.01 − 0.06 | 1.2 − 3 | 62.75 | 88.23 | 57 | ||||
| Modified YOLOv4 | 77.81 | 96.71 | 72 | |||||||||
| [4] | YOLOv3 | DarkNet-53 | Citrus trees | Multi-spectral | 75 | 99.9 | 99.7 | 99.79 | ||||
| [20] | CNN + Classifier (with refinement) | Citrus trees | Multi-spectral | 104 | 94.59 | 97.94 | 96.24 | |||||
| CNN + Classifier (without refinement) | 65 | 98 | 78 | |||||||||
| [78] | RetinaNet | Citrus trees | Multi-spectral | 12.9 | 120 | 62 | 92 | 74 | ||||
| Faster R-CNN | 86 | 39 | 54 | |||||||||
| Proposed approach | VGG-16 | 95 | 96 | 95 | 40 (8 stages) | 25 | ||||||
| [112] | Faster R-CNN | Apple trees | RGB | 91.1 | 94.1 | 92.5 |