Rice Science ›› 2025, Vol. 32 ›› Issue (6): 868-884.DOI: 10.1016/j.rsci.2025.08.005
• • 上一篇
收稿日期:2025-04-16
接受日期:2025-06-30
出版日期:2025-11-28
发布日期:2025-12-04
. [J]. Rice Science, 2025, 32(6): 868-884.
Fig. 1. Images of rice insects captured using augmented reality (AR) glasses for disk photography method: field image acquisition (A), overall view of white flat plate (B), and localized enlargment (C). The solid red line in B is used to split the white porcelain plate into upper and lower parts for independent shooting.
| Species | Morphological type | Labeled name | Number of training sets | Number of testing sets |
|---|---|---|---|---|
| Nilaparvata lugens | Macropterous female | M-BPH-female | 2 115 | 413 |
| Macropterous male | M-BPH-male | 704 | 217 | |
| Brachypterous female | B-BPH-female | 1 934 | 454 | |
| Brachypterous male | B-BPH-male | 421 | 112 | |
| Senior-instar nymph | S-BPH-nymph | 6 814 | 1 831 | |
| Third-instar nymph | BPH 3-nymph | 3 195 | 1 113 | |
| Sogatella furcifera | Macropterous female | M-WBPH-female | 3 404 | 784 |
| Macropterous male | M-WBPH-male | 2 216 | 548 | |
| Brachypterous adult | B-WBPH-adult | 961 | 247 | |
| Senior-instar nymph | S-WBPH-nymph | 7 105 | 1 561 | |
| Third-instar nymph | WBPH 3-nymph | 9 785 | 2 250 | |
| Laodelphax striatellus | Macropterous female | M-SBPH-female | 507 | 131 |
| Macropterous male | M-SBPH-male | 527 | 133 | |
| Brachypterous adult | B-SBPH-adult | 564 | 134 | |
| Senior-instar nymph | S-SBPH-nymph | 3 006 | 761 | |
| Third-instar nymph | SBPH 3-nymph | 833 | 204 | |
| Early-instar nymph of rice planthopper | Early-instar nymph | RPH 1‒2 nymph | 102 410 | 23 458 |
| Cicadellidae | Leafhopper nymph | RLH-nymph | 508 | 111 |
| Leafhopper adult | RLH-adult | 11 352 | 2 740 | |
| Cyrtorhinus lividipennis Reuter | Miridae | Miridae | 8 266 | 2 178 |
| Araneae | Wolf spider | Spider-Langzhu | 4 094 | 1 061 |
| Micryphantid spider | Spier-Weizhu | 3 716 | 960 | |
| Tetragnathidae | Spider-Xiaoxiao | 572 | 164 | |
| Others | Spider-Others | 11 590 | 2 840 | |
| Staphylinidae Latreille | Rove beetle | Rove-beetle | 318 | 85 |
Table 1. Annotation information of pests and their natural predators in dataset.
| Species | Morphological type | Labeled name | Number of training sets | Number of testing sets |
|---|---|---|---|---|
| Nilaparvata lugens | Macropterous female | M-BPH-female | 2 115 | 413 |
| Macropterous male | M-BPH-male | 704 | 217 | |
| Brachypterous female | B-BPH-female | 1 934 | 454 | |
| Brachypterous male | B-BPH-male | 421 | 112 | |
| Senior-instar nymph | S-BPH-nymph | 6 814 | 1 831 | |
| Third-instar nymph | BPH 3-nymph | 3 195 | 1 113 | |
| Sogatella furcifera | Macropterous female | M-WBPH-female | 3 404 | 784 |
| Macropterous male | M-WBPH-male | 2 216 | 548 | |
| Brachypterous adult | B-WBPH-adult | 961 | 247 | |
| Senior-instar nymph | S-WBPH-nymph | 7 105 | 1 561 | |
| Third-instar nymph | WBPH 3-nymph | 9 785 | 2 250 | |
| Laodelphax striatellus | Macropterous female | M-SBPH-female | 507 | 131 |
| Macropterous male | M-SBPH-male | 527 | 133 | |
| Brachypterous adult | B-SBPH-adult | 564 | 134 | |
| Senior-instar nymph | S-SBPH-nymph | 3 006 | 761 | |
| Third-instar nymph | SBPH 3-nymph | 833 | 204 | |
| Early-instar nymph of rice planthopper | Early-instar nymph | RPH 1‒2 nymph | 102 410 | 23 458 |
| Cicadellidae | Leafhopper nymph | RLH-nymph | 508 | 111 |
| Leafhopper adult | RLH-adult | 11 352 | 2 740 | |
| Cyrtorhinus lividipennis Reuter | Miridae | Miridae | 8 266 | 2 178 |
| Araneae | Wolf spider | Spider-Langzhu | 4 094 | 1 061 |
| Micryphantid spider | Spier-Weizhu | 3 716 | 960 | |
| Tetragnathidae | Spider-Xiaoxiao | 572 | 164 | |
| Others | Spider-Others | 11 590 | 2 840 | |
| Staphylinidae Latreille | Rove beetle | Rove-beetle | 318 | 85 |
Fig. 2. Diagrammatic representation of the two-stage cascaded detection method RiceInsectID. BCM, Balanced classification mosaic; TTA, Test-time augmentation; NMS, Non-maximum suppression; BN, Batch normalization; ReLU, Rectified linear unit; FC, Fully connected.
Fig. 3. Full-region overlapping sliding slices and target pasting algorithm visualization.
Fig. 4. Illustration of multiple online data augmentation strategies.
| Class | Recall50 (%) | Precision50 (%) | AP50 (%) |
|---|---|---|---|
| M-BPH-female | 96.4 | 71.9 | 92.8 |
| M-BPH-male | 54.7 | 92.6 | 52.4 |
| B-BPH-female | 88.9 | 79.9 | 87.2 |
| B-BPH-male | 84.8 | 81.1 | 80.4 |
| S-BPH-nymph | 91.1 | 73.6 | 85.7 |
| BPH 3-nymph | 53.8 | 72.2 | 46.0 |
| M-WBPH-female | 94.6 | 73.9 | 90.1 |
| M-WBPH-male | 72.7 | 81.5 | 66.8 |
| B-WBPH-adult | 78.8 | 69.7 | 72.5 |
| S-WBPH-nymph | 93.1 | 72.7 | 88.8 |
| WBPH 3-nymph | 47.8 | 85.7 | 43.9 |
| M-SBPH-female | 61.5 | 73.4 | 51.3 |
| M-SBPH-male | 72.6 | 87.7 | 70.2 |
| B-SBPH-adult | 77.7 | 72.7 | 68.7 |
| S-SBPH-nymph | 76.0 | 62.0 | 70.2 |
| SBPH 3-nymph | 41.5 | 52.0 | 26.2 |
| RPH 1‒2 nymph | 91.2 | 80.7 | 86.8 |
| RLH-nymph | 90.5 | 85.9 | 87.8 |
| RLH-adult | 85.6 | 73.8 | 80.2 |
| Miridae | 93.0 | 83.7 | 91.5 |
| Spider-Langzhu | 93.6 | 91.2 | 92.2 |
| Spier-Weizhu | 82.5 | 73.3 | 74.8 |
| Spider-Xiaoxiao | 86.4 | 73.9 | 81.1 |
| Spider-Others | 78.3 | 77.3 | 68.7 |
| Rove beetle | 82.6 | 78.3 | 79.9 |
Table 2. Detection results of YOLOv6_s on 25 classes of rice insect datasets.
| Class | Recall50 (%) | Precision50 (%) | AP50 (%) |
|---|---|---|---|
| M-BPH-female | 96.4 | 71.9 | 92.8 |
| M-BPH-male | 54.7 | 92.6 | 52.4 |
| B-BPH-female | 88.9 | 79.9 | 87.2 |
| B-BPH-male | 84.8 | 81.1 | 80.4 |
| S-BPH-nymph | 91.1 | 73.6 | 85.7 |
| BPH 3-nymph | 53.8 | 72.2 | 46.0 |
| M-WBPH-female | 94.6 | 73.9 | 90.1 |
| M-WBPH-male | 72.7 | 81.5 | 66.8 |
| B-WBPH-adult | 78.8 | 69.7 | 72.5 |
| S-WBPH-nymph | 93.1 | 72.7 | 88.8 |
| WBPH 3-nymph | 47.8 | 85.7 | 43.9 |
| M-SBPH-female | 61.5 | 73.4 | 51.3 |
| M-SBPH-male | 72.6 | 87.7 | 70.2 |
| B-SBPH-adult | 77.7 | 72.7 | 68.7 |
| S-SBPH-nymph | 76.0 | 62.0 | 70.2 |
| SBPH 3-nymph | 41.5 | 52.0 | 26.2 |
| RPH 1‒2 nymph | 91.2 | 80.7 | 86.8 |
| RLH-nymph | 90.5 | 85.9 | 87.8 |
| RLH-adult | 85.6 | 73.8 | 80.2 |
| Miridae | 93.0 | 83.7 | 91.5 |
| Spider-Langzhu | 93.6 | 91.2 | 92.2 |
| Spier-Weizhu | 82.5 | 73.3 | 74.8 |
| Spider-Xiaoxiao | 86.4 | 73.9 | 81.1 |
| Spider-Others | 78.3 | 77.3 | 68.7 |
| Rove beetle | 82.6 | 78.3 | 79.9 |
Fig. 5. The first layer of coarse-grained object detection model based on improved YOLOv6. FOSTP, Full-region overlapping sliding slices and targe pasting; BCM, Balanced classification mosaic; ECA-PAFPN&ASFF, Efficient channel attention path aggregation feature pyramid network & adaptive structure feature fusion; NMS, Non-maximum suppression; TTA, Test-time augmentation.
Fig. 6. Balanced classification mosaic method flow.
Fig. 7. Test-time augmentation execution process.
Fig. 8. ResNet50 network structure.
| Model | Recall | Precision | F1-score | Accuracy |
|---|---|---|---|---|
| VGG16 | 79.90 | 82.88 | 81.36 | 92.76 |
| Mobile-v3-large | 72.62 | 76.24 | 74.39 | 82.42 |
| ResNet18 | 82.61 | 84.58 | 83.58 | 93.57 |
| ResNet34 | 82.69 | 84.75 | 83.71 | 93.74 |
| ResNet50 | 82.95 | 85.46 | 84.19 | 93.95 |
| ResNet101 | 82.67 | 85.32 | 83.97 | 93.79 |
| Efficient_v1-b0 | 73.70 | 81.87 | 77.57 | 91.82 |
| Efficient_v2-b0 | 76.31 | 68.20 | 72.03 | 88.96 |
| Swin-Transformer | 69.46 | 56.25 | 62.16 | 82.71 |
| Poolformer-S12 | 76.93 | 84.69 | 80.62 | 92.80 |
| Convnext-base | 62.73 | 50.22 | 55.78 | 82.84 |
Table 3. Recognition results of different classification models on 16 dataset classes. %
| Model | Recall | Precision | F1-score | Accuracy |
|---|---|---|---|---|
| VGG16 | 79.90 | 82.88 | 81.36 | 92.76 |
| Mobile-v3-large | 72.62 | 76.24 | 74.39 | 82.42 |
| ResNet18 | 82.61 | 84.58 | 83.58 | 93.57 |
| ResNet34 | 82.69 | 84.75 | 83.71 | 93.74 |
| ResNet50 | 82.95 | 85.46 | 84.19 | 93.95 |
| ResNet101 | 82.67 | 85.32 | 83.97 | 93.79 |
| Efficient_v1-b0 | 73.70 | 81.87 | 77.57 | 91.82 |
| Efficient_v2-b0 | 76.31 | 68.20 | 72.03 | 88.96 |
| Swin-Transformer | 69.46 | 56.25 | 62.16 | 82.71 |
| Poolformer-S12 | 76.93 | 84.69 | 80.62 | 92.80 |
| Convnext-base | 62.73 | 50.22 | 55.78 | 82.84 |
| Model | Recall50 | Precision50 | mAP50 |
|---|---|---|---|
| YOLOv6_s | 78.79 | 76.83 | 73.45 |
| YOLOv8_s | 64.77 | 86.59 | 61.95 |
| YOLOv11_s | 76.21 | 76.46 | 71.01 |
| RTMDet_s | 47.52 | 75.51 | 45.36 |
| Faster R-CNN | 73.42 | 79.28 | 68.48 |
| Cascade R-CNN | 73.63 | 79.34 | 68.55 |
| ATSS-DyHead | 71.30 | 75.66 | 66.21 |
| Swin Transformer | 57.44 | 67.15 | 51.26 |
Table 4. Experimental results of different models on 25 classes of rice insect dataset. %
| Model | Recall50 | Precision50 | mAP50 |
|---|---|---|---|
| YOLOv6_s | 78.79 | 76.83 | 73.45 |
| YOLOv8_s | 64.77 | 86.59 | 61.95 |
| YOLOv11_s | 76.21 | 76.46 | 71.01 |
| RTMDet_s | 47.52 | 75.51 | 45.36 |
| Faster R-CNN | 73.42 | 79.28 | 68.48 |
| Cascade R-CNN | 73.63 | 79.34 | 68.55 |
| ATSS-DyHead | 71.30 | 75.66 | 66.21 |
| Swin Transformer | 57.44 | 67.15 | 51.26 |
| Model | Recall50 | Precision50 | mAP50 |
|---|---|---|---|
| YOLOv6_s | 85.06 | 83.10 | 82.65 |
| YOLOv8_s | 80.07 | 88.01 | 77.59 |
| YOLOv11_s | 83.89 | 82.97 | 81.42 |
| RTMDet_s | 63.49 | 88.61 | 61.25 |
| Faster R-CNN | 81.52 | 82.46 | 78.05 |
| Cascade R-CNN | 82.75 | 83.66 | 79.50 |
| ATSS-DyHead | 78.44 | 79.98 | 78.32 |
| Swin Transformer | 71.41 | 67.55 | 64.69 |
Table 5. Experimental results of various target detection models across 15 dataset classes. %
| Model | Recall50 | Precision50 | mAP50 |
|---|---|---|---|
| YOLOv6_s | 85.06 | 83.10 | 82.65 |
| YOLOv8_s | 80.07 | 88.01 | 77.59 |
| YOLOv11_s | 83.89 | 82.97 | 81.42 |
| RTMDet_s | 63.49 | 88.61 | 61.25 |
| Faster R-CNN | 81.52 | 82.46 | 78.05 |
| Cascade R-CNN | 82.75 | 83.66 | 79.50 |
| ATSS-DyHead | 78.44 | 79.98 | 78.32 |
| Swin Transformer | 71.41 | 67.55 | 64.69 |
| Class | Recall50 | Precision50 | AP50 |
|---|---|---|---|
| M-BPH-adult | 94.5 | 91.6 | 95.3 |
| B-BPH-adult | 88.8 | 84.7 | 88.5 |
| S-BPH-nymph | 87.4 | 76.9 | 84.3 |
| M-WBPH-adult | 95.1 | 95.8 | 96.5 |
| B-WBPH-adult | 75.6 | 79.6 | 70.9 |
| S-WBPH-nymph | 90.2 | 76.6 | 87.8 |
| M-SBPH-adult | 69.8 | 82.1 | 62.7 |
| B-SBPH-adult | 73.4 | 77.8 | 67.1 |
| S-SBPH-nymph | 74.7 | 68.5 | 69.7 |
| J-RPH-nymph | 88.2 | 86.7 | 87.0 |
| RLH-nymph | 90.2 | 86.9 | 87.1 |
| RLH-adult | 80.9 | 77.2 | 76.9 |
| Miridae | 90.7 | 87.6 | 91.0 |
| Spider | 89.3 | 87.7 | 88.7 |
| Rove beetle | 87.1 | 86.8 | 86.3 |
Table 6. Detection results of YOLOv6_s across 15-class dataset. %
| Class | Recall50 | Precision50 | AP50 |
|---|---|---|---|
| M-BPH-adult | 94.5 | 91.6 | 95.3 |
| B-BPH-adult | 88.8 | 84.7 | 88.5 |
| S-BPH-nymph | 87.4 | 76.9 | 84.3 |
| M-WBPH-adult | 95.1 | 95.8 | 96.5 |
| B-WBPH-adult | 75.6 | 79.6 | 70.9 |
| S-WBPH-nymph | 90.2 | 76.6 | 87.8 |
| M-SBPH-adult | 69.8 | 82.1 | 62.7 |
| B-SBPH-adult | 73.4 | 77.8 | 67.1 |
| S-SBPH-nymph | 74.7 | 68.5 | 69.7 |
| J-RPH-nymph | 88.2 | 86.7 | 87.0 |
| RLH-nymph | 90.2 | 86.9 | 87.1 |
| RLH-adult | 80.9 | 77.2 | 76.9 |
| Miridae | 90.7 | 87.6 | 91.0 |
| Spider | 89.3 | 87.7 | 88.7 |
| Rove beetle | 87.1 | 86.8 | 86.3 |
| FOSTP | ECA-PAFPN | ASFF | BCM | TTA | mAP50 (%) |
|---|---|---|---|---|---|
| 49.25 | |||||
| √ | 82.65 | ||||
| √ | √ | 83.12 | |||
| √ | √ | √ | 83.47 | ||
| √ | √ | √ | √ | 84.10 | |
| √ | √ | √ | √ | √ | 84.71 |
Table 7. Impact of five improvement strategies on coarse-grained detection model performance.
| FOSTP | ECA-PAFPN | ASFF | BCM | TTA | mAP50 (%) |
|---|---|---|---|---|---|
| 49.25 | |||||
| √ | 82.65 | ||||
| √ | √ | 83.12 | |||
| √ | √ | √ | 83.47 | ||
| √ | √ | √ | √ | 84.10 | |
| √ | √ | √ | √ | √ | 84.71 |
| Model | Online data augmentation strategy | Transfer learning | Recall (%) | Precision (%) | F1-score (%) | Accuracy (%) | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| RRC | RA | RHF | RVF | CJ | ||||||
| B-BPH-adult_ResNet50 | √ | √ | √ | 97.78 | 96.25 | 97.01 | 98.06 | |||
| M-BPH-adult_ResNet50 | √ | √ | √ | √ | √ | 91.36 | 91.27 | 91.31 | 91.32 | |
| M-WBPH-adult_ResNet50 | √ | √ | √ | √ | 91.24 | 90.46 | 90.85 | 91.96 | ||
| M-SBPH-adult_ResNet50 | √ | √ | √ | √ | √ | 99.09 | 99.09 | 99.09 | 99.09 | |
| J-RPH-nymph_ResNet50 | √ | √ | √ | 86.84 | 88.43 | 87.63 | 96.62 | |||
| Spider_ResNet50 | √ | √ | √ | √ | 90.69 | 86.60 | 88.60 | 91.18 | ||
Table 8. Experimental results of six classification models under optimal strategy combinations.
| Model | Online data augmentation strategy | Transfer learning | Recall (%) | Precision (%) | F1-score (%) | Accuracy (%) | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| RRC | RA | RHF | RVF | CJ | ||||||
| B-BPH-adult_ResNet50 | √ | √ | √ | 97.78 | 96.25 | 97.01 | 98.06 | |||
| M-BPH-adult_ResNet50 | √ | √ | √ | √ | √ | 91.36 | 91.27 | 91.31 | 91.32 | |
| M-WBPH-adult_ResNet50 | √ | √ | √ | √ | 91.24 | 90.46 | 90.85 | 91.96 | ||
| M-SBPH-adult_ResNet50 | √ | √ | √ | √ | √ | 99.09 | 99.09 | 99.09 | 99.09 | |
| J-RPH-nymph_ResNet50 | √ | √ | √ | 86.84 | 88.43 | 87.63 | 96.62 | |||
| Spider_ResNet50 | √ | √ | √ | √ | 90.69 | 86.60 | 88.60 | 91.18 | ||
| Model | Recall50 (%) | Precision50 (%) | F1-score (%) | Detection speed per frame (s) |
|---|---|---|---|---|
| YOLOv6_s | 78.79 | 76.83 | 77.80 | 1.82 |
| YOLOv6_R50 | 79.81 | 77.24 | 78.50 | 2.84 |
| RiceInsectID | 82.85 | 80.62 | 81.72 | 2.87 |
Table 9. Results of RiceInsectID and other models on pests and their natural predators.
| Model | Recall50 (%) | Precision50 (%) | F1-score (%) | Detection speed per frame (s) |
|---|---|---|---|---|
| YOLOv6_s | 78.79 | 76.83 | 77.80 | 1.82 |
| YOLOv6_R50 | 79.81 | 77.24 | 78.50 | 2.84 |
| RiceInsectID | 82.85 | 80.62 | 81.72 | 2.87 |
Fig. 9. Detection results of 25 target classes by different models.
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