露地蔬菜黄板害虫识别平台研发

doi:10.16178/j.issn.0528-9017.20250569

浙江农业科学 ›› 2026, Vol. 67 ›› Issue (1): 136-142.DOI: 10.16178/j.issn.0528-9017.20250569

露地蔬菜黄板害虫识别平台研发

褚航剑¹(), 娄卫东¹, 顾清¹, 刘庆¹, 吴弘洋², 黄晓华³^,^*(), 张小斌¹^,^*()

1.浙江省农业科学院数字农业研究所,浙江杭州 310021
2.浙江托普云农科技股份有限公司,浙江杭州 310015
3.浙江省植保检疫与农药管理总站,浙江杭州 310020

收稿日期:2025-08-11 出版日期:2026-01-11 发布日期:2026-01-19
通讯作者: 黄晓华,张小斌
作者简介:张小斌,E-mail: zhangxb@mail.zaas.ac.cn。
黄晓华,E-mail: 99483162@qq.com;
褚航剑,主要从事机器视觉、作物表型分析等数字农业技术研究与应用。E-mail: chuhj@zaas.ac.cn。
基金资助:
浙江省“三农九方”农业科技协作项目(2024SNJF074)

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. 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

摘要/Abstract

摘要：

大田虫害长期以来都是影响我国蔬菜产业高质量发展的关键因素之一,及时有效的虫害预警对保障蔬菜安全具有重要意义。本研究采用“大田+暗箱”的图像采集方法,构建了包含烟粉虱(Bemisia tabaci)、南亚果实蝇(Bactrocera tau)、甜菜白带野螟(Hymenia recurvalis)、黄曲条跳甲(Phyllotreta striolata)和叶蝉(Cicadellidae)等5种露地蔬菜常见害虫的黄板图像数据集,并基于该数据集对YOLO系列目标检测模型进行了系统性训练与性能对比分析。结果表明,YOLOv11s效果最佳,平均精度(mAP50)可达0.973。将该模型部署于云端并发布至“表型达人”微信小程序为公众提供害虫识别服务,为田间虫害防治提供了更实用的技术手段。

关键词: 露地蔬菜, 害虫识别, YOLO系列模型, 在线检测

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

中图分类号:

褚航剑, 娄卫东, 顾清, 刘庆, 吴弘洋, 黄晓华, 张小斌. 露地蔬菜黄板害虫识别平台研发[J]. 浙江农业科学, 2026, 67(1): 136-142.

CHU Hangjian, LOU Weidong, GU Qing, LIU Qing, WU Hongyang, HUANG Xiaohua, ZHANG Xiaobin. Development of a yellow sticky trap pest identification platform for open-field vegetables[J]. Journal of Zhejiang Agricultural Sciences, 2026, 67(1): 136-142.

图/表 7

图1 田间黄板图像采集 A,德清农博家庭农场田间黄板;B,黄板图像采集方法。

Fig.1 Image acquisition from field-deployed yellow sticky traps

图2 害虫标注样例 A,烟粉虱;B,叶蝉;C,黄曲条跳甲;D,南亚果实蝇;E,甜菜白带野螟。

Fig.2 Pest annotation examples

表1 数据集中目标框数量分布

Table 1 Distribution of bounding box counts in the dataset

害虫名称	训练集目标框数量	验证集目标框数量
烟粉虱	736	178
南亚果实蝇	1 141	307
甜菜白带野螟	796	210
黄曲条跳甲	963	189
叶蝉	862	228

表2 不同YOLO模型表现结果

Table 2 Performances of different YOLO models 单位:%

YOLO模型	精确度	召回率	mAP50	mAP50-95
v5n	0.937	0.935	0.965	0.726
v5s	0.934	0.926	0.963	0.756
v8n	0.919	0.958	0.967	0.738
v8s	0.932	0.940	0.968	0.763
v11n	0.948	0.939	0.975	0.753
v11s	0.932	0.959	0.973	0.779

表3 YOLOv11s模型的总体表现

Table 3 Overall performance of YOLOv11s model

类别	标注信息	精确度/%	召回率/%	mAP50/%	mAP50- 95/%
烟粉虱	178	0.889	0.959	0.952	0.745
南亚果实蝇	307	0.911	0.944	0.957	0.767
甜菜白带野螟	210	0.976	0.928	0.993	0.861
黄曲条跳甲	189	0.963	0.985	0.995	0.861
叶蝉	228	0.921	0.995	0.967	0.663
总计	1 112	0.932	0.959	0.973	0.779

图3 YOLOv11s模型的检测结果编号0为烟粉虱,1为南亚果实蝇,2为甜菜白带野螟,3为黄曲条跳甲,4为叶蝉。

Fig.3 Detection results of YOLOv11s model

图4 微信小程序界面 A,“表型达人”主界面;B,黄板害虫识别界面;C~E,黄板害虫识别结果;F,分析记录界面。

Fig.4 WeChat mini program interface

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露地蔬菜黄板害虫识别平台研发

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

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