| 基于X射线成像与卷积神经网络的核桃内部品质检测 |
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| 引用本文： | 张淑娟,高庭耀,任锐,孙海霞.基于X射线成像与卷积神经网络的核桃内部品质检测[J].农业机械学报,2022,53(1):383-388. |
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| 作者姓名： | 张淑娟 高庭耀 任锐 孙海霞 |
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| 作者单位： | 山西农业大学农业工程学院,太谷030800 |
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| 基金项目： | 山西省重点研发计划项目（201903D221027） |
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| 摘 要： | 针对目前我国核桃内部品质混杂、不易检测等问题,提出利用X射线成像技术结合卷积神经网络对核桃内部品质进行快速检测.对获取的核桃X射线图像进行预处理和数据扩充,采用GoogLeNet、ResNet 101、MobileNet v2和VGG 19共4种迁移学习模型构建卷积神经网络,对核桃数据集进行训练.通过预测集准确率、预测...
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| 关 键 词： | 核桃 X射线成像技术 内部品质 卷积神经网络 检测系统 |
| 收稿时间： | 2021/9/4 0:00:00 |
Detection of Walnut Internal Quality Based on X-ray Imaging Technology and Convolution Neural Network |
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| ZHANG Shujuan,GAO Tingyao,REN Rui,SUN Haixia.Detection of Walnut Internal Quality Based on X-ray Imaging Technology and Convolution Neural Network[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(1):383-388. |
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| Authors: | ZHANG Shujuan GAO Tingyao REN Rui SUN Haixia |
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| Institution: | Shanxi Agricultural University |
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| Abstract: | In order to solve the problems of export mixed internal quality and not easily to detect of walnuts in China, X-ray imaging technology combined with convolution neural network was proposed to quickly detect the internal quality of walnut. Using X-ray transmittance, X-ray images containing internal information were obtained. Firstly, X-ray images of walnut were preprocessed and data expanded. Then, four transfer learning models, including GoogLeNet, ResNet 101, MobileNet v2 and VGG 19, were used to construct convolutional neural networks to train walnut data sets. The model was analyzed through prediction set accuracy, loss value, test set accuracy and running time, and the model parameters were optimized. Finally, the walnut internal quality detection and sorting system was developed and applied to model verification. The results showed that among the four different transfer learning models, GoogLeNet model had the highest prediction accuracy. When the learning rate of GoogLeNet model was set to 0.001 and the epoch was set to 25, the prediction effect was the best, and the prediction accuracy was 96.67%. The results of system verification showed that the discriminant accuracy of shell walnut reached 100%, and the average discriminant accuracy was 96.39%. The system could realize the non-destructive testing and sorting of walnut internal quality, and provide further theoretical basis and technical reference for the equipment research and development. |
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| Keywords: | walnut X-ray imaging technology internal quality convolutional neural network detection system |
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