An automated zizania quality grading method based on deep classification model

文献类型： 外文期刊

第一作者： Cao, Jingjun

作者： Cao, Jingjun;Sun, Tan;Zhang, Wenrong;Zhou, Guomin;Chai, Xiujuan;Cao, Jingjun;Sun, Tan;Zhang, Wenrong;Zhou, Guomin;Chai, Xiujuan;Zhong, Ming;Huang, Bo

作者机构：

关键词： Zizania; Automatic grading; Convolutional neural network; Deep learning; Object classification

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：3.858； 五年影响因子：4.008 ）

ISSN： 0168-1699

年卷期： 2021 年 183 卷

页码：

收录情况： SCI

摘要： Zizania, one of aquatic vegetables, needs to be graded before entering market for assuring the product quality. However, it is time-consuming, tedious, labor-intensive, inaccurate and expensive to assess qualitatively and grade zizanias manually. This paper gives an effective solution to automatically classify fresh zizania into two categories, high quality and defective quality, by using the deep learnt features from the appearances. A new architecture of convolutional neural network, called LightNet, has been proposed and described. Specifically, it is composed of many compressed blocks, which is designed to reduce the computation complexity mainly by converting serial down-sampling and convolution operation to parallel structure. We evaluate the proposed architecture on the zizania image dataset collected by ourselves and integrate the algorithm in the automatic grading device. The experimental results show that the accuracy rate achieves 95.62% and the speed of inference quality is around 47 ms per zizania image. The proposed LightNet for automate classification has less parameters and lower computation complexity than popular networks, while maintaining the comparable accuracy in the task of grading zizanias. It obtains 99.31% accuracy in the task of grading apples. The result proves that it can be extended to other tasks about classification.

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