Potato bud eye detection is a key factor in realizing automatic seed potato cutting.In order to quickly and accurately detect potato seed bud eyes and improve the cutting efficiency of intelligent seed cutting device,a potato seed bud eye detection method based on convolutional neural network was proposed.Firstly,according to the characteristics of the diverse morphology of bud eyes,a potato bud eye database was established by image augmentation and image diversification processing to enhance the generalization ability of the detection network.Then,using the efficient feature expression ability of YOLOx for small-size targets,a network model for bud eye detection was constructed to achieve rapid and accurate detection of potato bud eye.The results showed that the YOLOx network can achieve good detection results for samples containing single and multiple unobstructed bud eyes,as well as samples containing scars,spots,insect eyes and mechanical damage.Among them,the final detection accuracy P was 95.86%,the recall rate R was 96.95%,the average accuracy mAP was 95.37%,and the detection speed was 42.3 frames/s.Compared with the YOLOv3 and YOLOv4 network models,the YOLOx model can meet the accuracy and speed requirements of potato bud eye recognition detection at the same time.This method can provide technical support for intelligent potato cutting.
NETL
NSTL
万方数据
维普
