基于YOLOv5的物联网草莓病虫害监测系统设计
Design of IoT Strawberry Disease and Pest Monitoring System Based on YOLOv5
黄伟州 1周小杰 2汪婵 2冯智 1陈子琪1
作者信息
- 1. 安徽科技学院 智能制造学院,安徽 凤阳 233100
- 2. 安徽科技学院 电气与电子工程学院,安徽 凤阳 233100
- 折叠
摘要
针对传统农业模式下草莓生长周期监测存在依赖人工经验,无法实时掌握病虫害,以及生长环境监测耗费大量人力物力的问题,设计了一种基于物联网与深度学习算法(YOLOv5)的草莓病虫害监测系统.系统通过物联网传感器,实时采集大棚的土壤PH值、温湿度、光照、空气质量等数据,将传感器监测的数据传输至STM32 上,并通过Wi-Fi通信方式将数据上传至云平台.同时,结合改良的YOLOv5 算法,将草莓植株的分类图像识别平均准确率mAP 提升至 84.5%,从而迅速发现病虫害并检测草莓成熟度.
Abstract
Aiming at the problems that strawberry growth cycle monitoring in traditional agricultural mode relies on manual experience,cannot grasp diseases and pests in real time,and the monitoring of growth environment costs a lot of manpower and material resources,a strawberry pest monitoring system based on the Internet of Things and deep learning algorithm(YOLOv5)is designed.The system collects soil PH value,temperature and humidity,light,air quality and other data of the greenhouse in real time through the Internet of Things sensor,transmits the data monitored by the sensor to the STM32,and uploads the data to the cloud platform through Wi-Fi communication.Meanwhile,combined with the improved YOLOv5 algorithm,the average accuracy of mAP of classification image recognition of strawberry plants is increased to 84.5%,so as to quickly detect diseases and pests and detect strawberry maturity.
关键词
物联网/深度学习/病虫害监测/STM32Key words
Internet of Things/deep learning/pest and disease surveillance/STM32引用本文复制引用
出版年
2024
NETL
NSTL
万方数据