Journal of Zhejiang Agricultural Sciences ›› 2026, Vol. 67 ›› Issue (1): 136-142.DOI: 10.16178/j.issn.0528-9017.20250569

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Development of a yellow sticky trap pest identification platform for open-field vegetables

CHU Hangjian1(), LOU Weidong1, GU Qing1, LIU Qing1, WU Hongyang2, HUANG Xiaohua3,*(), ZHANG Xiaobin1,*()   

  1. 1. Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, Zhejiang
    2. Zhejiang Top Cloud-Agri Technology Co., Ltd., Hangzhou 310015, Zhejiang
    3. General Station of Plant Protection Quarantine and Pesticide Management in Zhejiang Province, Hangzhou 310020, Zhejiang
  • Received:2025-08-11 Online:2026-01-11 Published:2026-01-19
  • Contact: HUANG Xiaohua,ZHANG Xiaobin

Abstract:

Pest infestation in open fields has long been a critical factor constraining the high-quality development of China's vegetable industry. Timely and effective pest early warning is of great significance for ensuring vegetable production safety. This study established a yellow sticky trap dataset featuring 5 common vegetable pests, including Bemisia tabaci, Bactrocera tau, Hymenia recurvalis, Phyllotreta striolata, and Cicadellidae by collecting field data using a “field+darkbox” imaging approach. Based on this dataset, YOLO-series models were trained and compared for pest detection performance. The results showed that YOLOv11s performed the best, achieving a mean average precision (mAP50) of 0.973. The trained model was deployed as a WeChat Mini Program called “PhenoAI”, providing the public with pest identification services and offering a more practical technical solution for field pest control.

Key words: open-field vegetable, pest identification, YOLO-series model, online detection

CLC Number: