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

Xusheng Ji, Jiaxin Li, Xiaoxu Cai, Xinhai Ye, Mostafa Gouda, Yong He, Gongyin Ye () and Xiaoli Li ()
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Xusheng Ji: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Jiaxin Li: College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
Xiaoxu Cai: The Rural Development Academy, Zhejiang University, Hangzhou 310058, China
Xinhai Ye: Collect of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou 311300, China
Mostafa Gouda: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Yong He: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Gongyin Ye: College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
Xiaoli Li: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Agriculture, 2025, vol. 15, issue 13, 1-22

Abstract: Rice ( Oryza sativa L.) has long been threatened by the brown planthopper (BPH, Nilaparvata lugens ) and white-backed planthopper (WBPH, Sogatella furcifera ). 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 2 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.

Keywords: publicly available dataset; rice planthopper; detection and counting; Swin Transformer-based module; YOLOv8-p2 architecture; field landscapes (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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