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Broadband Communications, Networks, and Systems. 12th EAI International Conference, BROADNETS 2021, Virtual Event, October 28–29, 2021, Proceedings

Research Article

Research on Wheat Impurity Image Recognition Based on Convolutional Neural Network

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  • @INPROCEEDINGS{10.1007/978-3-030-93479-8_23,
        author={Chunhua Zhu and Tiantian Miao},
        title={Research on Wheat Impurity Image Recognition Based on Convolutional Neural Network},
        proceedings={Broadband Communications, Networks, and Systems. 12th EAI International Conference, BROADNETS 2021, Virtual Event, October 28--29, 2021, Proceedings},
        proceedings_a={BROADNETS},
        year={2022},
        month={1},
        keywords={Convolutional neural network Wheat grains Impurities},
        doi={10.1007/978-3-030-93479-8_23}
    }
    
  • Chunhua Zhu
    Tiantian Miao
    Year: 2022
    Research on Wheat Impurity Image Recognition Based on Convolutional Neural Network
    BROADNETS
    Springer
    DOI: 10.1007/978-3-030-93479-8_23
Chunhua Zhu1,*, Tiantian Miao1
  • 1: Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou
*Contact email: zhuchunhua@haut.edu.cn

Abstract

The doping rate is one of the important indexes to evaluate the quality grade and price of wheat. In order to accurately and quickly recognize impurities (wheat husk) in wheat grains, images of doped wheat were collected and Convolutional Neural Network (CNN) was used to realize the classification and recognition of grains and impurities in wheat grains. In this study, image segmentation and image enhancement were used to preprocess the acquired images to establish the image database of wheat grains and impurities. According to the characteristics of image data, the classic CNN, VGGNet and ResNet network models for wheat impurity images recognition were established. Simulation analysis shows that, compared with the classical CNN and VGGNet network models, the ResNet network model has the best recognition performance. The recognition accuracy of the test set is 96.94%, the recognition time is 5.60 ms.

Keywords
Convolutional neural network Wheat grains Impurities
Published
2022-01-01
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-93479-8_23
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