摘要
The quality of maize seeds affects the outcome of planting and harvesting,so seed quality inspection has become very important.Traditional seed quality detection methods are labor-intensive and time-consuming,whereas seed quality detection using computer vision techniques is efficient and accurate.In this study,we conducted migration learning training in AlexNet,VGG11 and ShuffleNetV2 network models respectively,and found that ShuffleNetV2 has a high accuracy rate for maize seed classification and recognition by comparing various metrics.In this study,the features of the seed images were extracted through image pre-processing methods,and then the AlexNet,VGG11 and ShuffleNetV2 models were used for training and classification respectively.A total of 2081 seed images containing four varieties were used for training and testing.The experimental results showed that ShuffleNetV2 could efficiently distinguish different varieties of maize seeds with the highest classification accuracy of 100%,where the parameter size of the model was at 20.65 MB and the response time for a single image was at 0.45 s.Therefore,the method is of high practicality and extension value.
