Maize Seed Quality Detection Based on Convolutional Neural Network
In order to improve the accuracy of maize seed quality detection,a convolutional neu-ral network based maize seed quality detection method was proposed with single maize seed of different quality as the research object.The existing data set was extended by rotation,mirror image,shading and noise addition.Contrast adjustment and Blob analysis were used to enhance image features.Resnet50 classification model of convolutional neural network(CNN)and Squeezenet model were selected to conduct experiments respectively.Data sets were grouped in different proportions,and the classification detection of maize seeds according to good,bad and impurity was realized through training.Finally,by comparing the comprehensive evaluation in-dexes of the six groups of experiments,it was concluded that when the training and testing sets were allocated in a 9:1 ratio,the Resnet50 model had the best training effect and the average comprehensive index(F1)was 96.45%.
万方数据
维普
