| 罗浩伦1, 李国志1, 尤彦辰1, 李 彬2, 吕 军1, 李文冬3, 姚 青4*.烟草仓储害虫性诱智能监测系统设计与实现[J].植物保护,2025,51(1):123-131. |
| 烟草仓储害虫性诱智能监测系统设计与实现 |
| Design and implementation of an intelligent monitoring system for tobacco storage pests trapped by sex pheromone |
| 投稿时间：2024-01-30 修订日期：2024-03-25 |
| DOI：10.16688/j.zwbh.2024048 |
| 中文关键词: 烟草仓储害虫 智能性诱捕器 烟草甲 烟草粉螟 害虫图像 YOLOX-TP模型 |
| 英文关键词:tobacco storage pests intelligent trap based on sex pheromone Lasioderma serricorne Ephestia elutella pest images YOLOX-TP model |
| 基金项目:浙江省科技计划(2022C02004) |
| 作者 | 单位 | E-mail | | 罗浩伦1, 李国志1, 尤彦辰1, 李 彬2, 吕 军1, 李文冬3, 姚 青4* | 1. 浙江理工大学信息科学与工程学院, 杭州 310018
2. 贵州航天智慧农业有限公司, 贵阳 550000
3. 吉林省长春市农业信息中心, 长春 130051
4. 浙江理工大学计算机科学与技术学院, 杭州 310018 | q-yao@zstu.edu.cn |
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| 中文摘要: |
| 为了实时智能监测烟草仓储害虫, 设计并实现了烟草仓储害虫性诱智能监测系统?该系统由基于机器视觉的智能性诱捕器?性诱害虫识别模型?服务器和Web端系统平台组成?智能性诱捕器通过性诱剂将害虫诱集至粘虫板, 机器视觉模块每天定时采集一幅粘虫板图像, 并通过4G网络将图像上传至服务器?服务器接收到图像后调用性诱害虫识别模型进行害虫的检测与识别, 并将检测结果返回到Web客户端?用户可通过系统平台Web端查看诱集的害虫图像和数量?针对粘虫板图像上的性诱害虫烟草甲Lasioderma serricorne和烟草粉螟Ephestia elutella, 建立了YOLOX-TP识别模型, 在YOLOX的基础上添加了SEnet注意力机制?与Faster-RCNN?YOLOv4?YOLOX检测模型相比, YOLOX-TP平均精确率和平均召回率最高, 达到98.97%和97.12%?烟草仓储害虫性诱智能监测系统实现了烟草性诱害虫图像的定时采集?害虫准确检测与计数?结果可视化和可追溯, 为烟草仓储害虫防治决策提供依据? |
| 英文摘要: |
| For automatically monitoring tobacco storage pests in real-time, an intelligent monitoring system was designed and implemented. The system consists of a machine vision-based intelligent sex pheromone trap, a pest identification model, a server, and a Web-based platform. The intelligent trap attracts pests to a sticky board using sex pheromones, and the machine vision module captures an image of the sticky board at regular intervals every day. The image is then uploaded to the server via a 4G network. Once the server receives the image, it uses the pest identification model to detect and recognize the pests in the image, and returns the results to the network client. Users can view the images and quantities of trapped pests through the network platform. For accurately identifying the Lasioderma serricorne and Ephestia elutella on the sticky board images, a YOLOX-TP recognition model was developed, which incorporates the SEnet attention mechanism into the YOLOX framework. Compared with Faster-RCNN, YOLOv4, and YOLOX models, YOLOX-TP achieved the highest average precision and recall rates, reaching 98.97% and 97.12%, respectively. The intelligent monitoring system for tobacco storage pests achieves real-time image acquisition, accurate pest detection and counting, result visualization, and data traceability, providing a basis for decision-making in the monitoring and controlling tobacco storage pests. |
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