A Completeness Detection Method for Melon Seeds Based on Deep Learning Caffe Framework
In order to further develop the automation and unmanned agricultural direction,improve the current situation of slow speed,low accuracy,time-consuming and laborious use of manual detection in farm product inspection,reduce the amount of data used in image processing calculation,and also improve the legibility of image information,the melon seed image acquisition system will be built to obtain the image of the melon seeds to be detected with obvious color contrast with the environment.A deep convolu-tional neural network model in the Deep Learning Caffe framework was used for comprehensive recognition of the completeness of the melon basket.The combined recognition rate of the degree of neatness of the melon basket is obtained and then compared with that of the Support Vector Machine Language(SVM).The recognition rate of deep convolutional neural network patterns under the deep learning Caffe architecture is improved and more effective relative to the more support vector machine(SVM)approach.
deep learningconvolutional neural networksmelon seed integrityCaffe frameworksupport vector machine
万方数据
维普
