Research Article
Detecting Citrus in Orchard Environment by Using Improved YOLOv4
Table 5
Identification parameters of four methods for different occlusion degrees.
| Occlusion condition | Model | Citrus count | Correctly identified | Falsely identified | Missed | Amount | Rate (%) | Amount | Rate (%) | Amount | Rate (%) |
| Less than 50% | Faster-RCNN | 200 | 162 | 81.24 | 19 | 9.71 | 31 | 15.44 | YOLOv3 | 200 | 156 | 78.22 | 23 | 11.52 | 38 | 18.96 | YOLOv4 | 200 | 173 | 86.38 | 14 | 7.10 | 22 | 11.21 | Improved YOLOv4 | 200 | 187 | 93.58 | 12 | 5.98 | 16 | 8.15 |
| More than 50% | Faster-RCNN | 200 | 156 | 78.24 | 26 | 12.81 | 39 | 19.34 | YOLOv3 | 200 | 148 | 74.22 | 35 | 17.52 | 46 | 22.96 | YOLOv4 | 200 | 166 | 83.18 | 20 | 10.05 | 26 | 13.07 | Improved YOLOv4 | 200 | 182 | 90.82 | 14 | 7.18 | 21 | 10.36 |
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