Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes
Rice (<i>Oryza sativa</i> L.) has long been threatened by the brown planthopper (BPH, <i>Nilaparvata lugens</i>) and white-backed planthopper (WBPH, <i>Sogatella furcifera</i>). It is difficult to detect and count rice planthoppers from RGB images, and there are a...
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MDPI AG
2025-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/13/1366 |
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| author | Xusheng Ji Jiaxin Li Xiaoxu Cai Xinhai Ye Mostafa Gouda Yong He Gongyin Ye Xiaoli Li |
| author_facet | Xusheng Ji Jiaxin Li Xiaoxu Cai Xinhai Ye Mostafa Gouda Yong He Gongyin Ye Xiaoli Li |
| author_sort | Xusheng Ji |
| collection | DOAJ |
| description | Rice (<i>Oryza sativa</i> L.) has long been threatened by the brown planthopper (BPH, <i>Nilaparvata lugens</i>) and white-backed planthopper (WBPH, <i>Sogatella furcifera</i>). It is difficult to detect and count rice planthoppers from RGB images, and there are a limited number of publicly available datasets for agricultural pests. This study publishes a publicly available planthopper dataset, explores the potential of YOLOv8-p2 and proposes an efficient improvement strategy, designated SwinT YOLOv8-p2, for detecting and counting BPH and WBPH from RGB images. The Swin Transformer was incorporated into the YOLOv8-p2 in the strategy. Additionally, the Spatial and Channel Reconstruction Convolution (SCConv) was applied, replacing Convolution (Conv) in the C2f module of YOLOv8. The dataset contains diverse pest small targets, and it is easily available to the public. YOLOv8-p2 can accurately detect different pests, with mAP50, mAP50:95, F1-score, Recall, Precision and FPS up to 0.847, 0.835, 0.899, 0.985, 0.826 and 16.69, respectively. The performance of rice planthopper detection was significantly improved by SwinT YOLOv8-p2, with increases in mAP50 and mAP50:95 ranging from 1.9% to 61.8%. Furthermore, the correlation relationship between the manually counted and detected insects was strong for SwinT YOLOv8-p2, with an R<sup>2</sup> above 0.85, and RMSE and MAE below 0.64 and 0.11. Our results suggest that SwinT YOLOv8-p2 can efficiently detect and count rice planthoppers. |
| format | Article |
| id | doaj-art-6a77b64f4e094f648d13f215d9baaa98 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-6a77b64f4e094f648d13f215d9baaa982025-08-20T03:28:24ZengMDPI AGAgriculture2077-04722025-06-011513136610.3390/agriculture15131366Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field LandscapesXusheng Ji0Jiaxin Li1Xiaoxu Cai2Xinhai Ye3Mostafa Gouda4Yong He5Gongyin Ye6Xiaoli Li7College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, ChinaThe Rural Development Academy, Zhejiang University, Hangzhou 310058, ChinaCollect of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaRice (<i>Oryza sativa</i> L.) has long been threatened by the brown planthopper (BPH, <i>Nilaparvata lugens</i>) and white-backed planthopper (WBPH, <i>Sogatella furcifera</i>). It is difficult to detect and count rice planthoppers from RGB images, and there are a limited number of publicly available datasets for agricultural pests. This study publishes a publicly available planthopper dataset, explores the potential of YOLOv8-p2 and proposes an efficient improvement strategy, designated SwinT YOLOv8-p2, for detecting and counting BPH and WBPH from RGB images. The Swin Transformer was incorporated into the YOLOv8-p2 in the strategy. Additionally, the Spatial and Channel Reconstruction Convolution (SCConv) was applied, replacing Convolution (Conv) in the C2f module of YOLOv8. The dataset contains diverse pest small targets, and it is easily available to the public. YOLOv8-p2 can accurately detect different pests, with mAP50, mAP50:95, F1-score, Recall, Precision and FPS up to 0.847, 0.835, 0.899, 0.985, 0.826 and 16.69, respectively. The performance of rice planthopper detection was significantly improved by SwinT YOLOv8-p2, with increases in mAP50 and mAP50:95 ranging from 1.9% to 61.8%. Furthermore, the correlation relationship between the manually counted and detected insects was strong for SwinT YOLOv8-p2, with an R<sup>2</sup> above 0.85, and RMSE and MAE below 0.64 and 0.11. Our results suggest that SwinT YOLOv8-p2 can efficiently detect and count rice planthoppers.https://www.mdpi.com/2077-0472/15/13/1366publicly available datasetrice planthopperdetection and countingSwin Transformer-based moduleYOLOv8-p2 architecturefield landscapes |
| spellingShingle | Xusheng Ji Jiaxin Li Xiaoxu Cai Xinhai Ye Mostafa Gouda Yong He Gongyin Ye Xiaoli Li Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes Agriculture publicly available dataset rice planthopper detection and counting Swin Transformer-based module YOLOv8-p2 architecture field landscapes |
| title | Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes |
| title_full | Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes |
| title_fullStr | Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes |
| title_full_unstemmed | Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes |
| title_short | Driving by a Publicly Available RGB Image Dataset for Rice Planthopper Detection and Counting by Fusing Swin Transformer and YOLOv8-p2 Architectures in Field Landscapes |
| title_sort | driving by a publicly available rgb image dataset for rice planthopper detection and counting by fusing swin transformer and yolov8 p2 architectures in field landscapes |
| topic | publicly available dataset rice planthopper detection and counting Swin Transformer-based module YOLOv8-p2 architecture field landscapes |
| url | https://www.mdpi.com/2077-0472/15/13/1366 |
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