摘要
目的:对山核桃一次破壳后物料进行分类,提高山核桃深加工水平。方法:通过图像采集系统得到5类山核桃样本,分别为较完整壳仁未分、露仁、未破壳完整山核桃、不完整壳仁未分、壳。利用数据增广的方式,得到包含15 000个图像样本建立的数据集。在VGG16网络基础上构建模型,并按9∶1的比例在包含5类山核桃物料图像的数据集上进行训练和验证。结果:该模型训练准确率和验证准确率分别达到了97.3%,99.7%;对1 713张山核桃加工物料图像进行分类识别,准确度达到了99.5%。结论:该模型能够达到对山核桃一次破壳后的物料分类识别的精度要求。
Objective: In order to classify the pecan materials after the shell is broken, and to improve the deep processing level of pecans. Methods: 5 types of pecan samples were obtained through the image acquisition system, including relatively intact shell kernels, undivided kernels, unbroken intact pecans, incomplete shell kernels undivided, and shells. Using the data augmentation way, a sample containing 15 000 images created data sets were obtained. Build a model based on the VGG16 network, which was trained and verified on a data set containing 5 types of pecan material images according to the ratio of 9∶1. Results: The results showed that the accuracy of model training and validation accuracy reached 97.3% and 99.7%, respectively. Through classification and recognition of 1 713 hickory processed material images, the accuracy reached 99.5%. Conclusion: The model can be achieved after a break of pecan shell material classification accuracy requirements.
作者
李文宝
曹成茂
张金炎
彭美乐
LI Wen-bao;CAO Cheng-mao;ZHANG Jin-yan;PENG Mei-le(College of Engineering,Anhui Agricultural University,Hefei,Anhui 230036,China)
出处
《食品与机械》
北大核心
2021年第9期133-138,185,共7页
Food and Machinery
基金
国家自然基金面上项目(编号:52075003)。
关键词
山核桃
卷积神经网络
VGG16
分类识别
hickory
convolutional neural network
VGG16
classification and recognition
