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.
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Funding
This research was financially supported by National Science Foundation of China (61871176): Research of Abnormal Grain Conditions Detection using Radio Tomographic Imaging based on Channel State Information; National Science Foundation of China (61901159): Research on Beamspace Channel Estimation in Massive MIMO Systems by Fusing Multi-Dimensional Characteristic Information; Applied research plan of key scientific research projects in Henan colleges and Universities (19A510011): Research of Abnormal Grain Conditions Detection Based on Radio Tomographic Imaging based on RSSI; Scientific Research Foundation Natural Science Project In Henan University of Technology (2018RCJH18): Research of Abnormal Grain Conditions Detection using Radio Tomographic Imaging based on Received Signal Strength Information; the Innovative Funds Plan of Henan University of Technology Plan (2020ZKCJ02): Data-Driven Intelligent Monitoring and Traceability Technique for Grain Reserves.
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Chunhua Zhu and Tiantian Miao proposed the original idea and Tiantian Miao carried out the experiment. Chunhua Zhu and Tiantian Miao wrote the paper. Tiantian Miao supervised and reviewed the manuscript. All authors read and approved the final manuscript.
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Zhu, C., Miao, T. (2022). Research on Wheat Impurity Image Recognition Based on Convolutional Neural Network. In: Xiang, W., Han, F., Phan, T.K. (eds) Broadband Communications, Networks, and Systems. BROADNETS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-93479-8_23
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DOI: https://doi.org/10.1007/978-3-030-93479-8_23
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