YOLO v 3-based Crop Health Detection and Design of a Cargo Transport Trolley

In the environment of the gradual prosperity of intelligent agriculture, in order to prevent the decline of branches and leaves in the growth of crops caused by the reduction of yield, take remedial measures; This paper presents a truck based on neural network identification and detection, which can monitor the growth of crops in real time, identify the health state of crops, assist farm workers to inspect the growth of crops, transport field crops and other articles. By integrating Raspberry PI camera into YOLO v3 convolutional neural network, it can judge the features of crops and identify the health status of target objects The information collected by the camera controls the tracking of the steering gear, motor and trolley, and the position information and health status can be viewed on the OneNet platform. Real-time detection and growth judgment of agricultural products can be realized without increasing the energy and financial cost of manual inspection.