Design of IoT Strawberry Disease and Pest Monitoring System Based on YOLOv5
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.
Internet of Thingsdeep learningpest and disease surveillanceSTM32
NETL
NSTL
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