Research on quality monitoring method of transplanting operation based on edge computing
In order to achieve real-time monitoring and statistical analysis of transplanter,improve the overall informatization level of transplanting operations,and expand the application fields of transplanting technology,an efficient identification method suitable for monitoring the quality information of transplanter operations was studied.Built an image acquisition and recognition platform through industrial cameras and edge computing equipment;Expanded the images of the transplanting environment us-ing image enhancement technology;Based on YOLOv5,a lightweight convolutional neural network MobileNetv2 was intro-duced as the feature extraction backbone to reduce the number of model parameters.Relu activation function was used to re-duce computational complexity and convolutional computation time.The improved model was quantized and trained to im-prove running speed and deployability.Finally,SORT multi-objective tracking algorithm was used to count and statistically analyze the quality information of transplanted seedlings.The experiment shows that:2ZB-2J high-speed fully automatic transplanter in normal operation,the platform meets real-time requirements,and has high accuracy and statistical rate in identi-fying bowl seedlings,which can basically meet the overall system requirements.
万方数据
维普
