| 基于图像识别和卷积神经网络的大豆优良籽粒筛选研究 |
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| 引用本文： | 朱荣胜,闫学慧,陈庆山.基于图像识别和卷积神经网络的大豆优良籽粒筛选研究[J].大豆科学,2020(2):189-197. |
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| 作者姓名： | 朱荣胜 闫学慧 陈庆山 |
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| 作者单位： | 东北农业大学理学院;东北农业大学工程学院;东北农业大学农学院 |
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| 基金项目： | 国家重点研发计划子课题(科技部)(2016YED0100201-21-1)。 |
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| 摘 要： | 为实现通过籽粒图像识别方法对大豆籽粒的品质进行快速、准确检测,以大豆正常品质籽粒及非正常品质籽粒的分类为例,提出一种基于卷积神经网络的大豆优良籽粒图像筛选分类识别方法。建立大豆籽粒品质数据集,设计卷积神经网络,提取大豆籽粒图像特征。为提高分类准确率和实时性,从设计选择卷积神经网络结构、减小过拟合、加快训练收敛速度、增强网络的鲁棒性等方面对卷积神经网络进行优化,最终选择含有4个卷积层、4个池化层、2个全连接层的6层卷积神经网络,采用L2正则化和小批量训练学习方法对网络进行优化训练测试。将结果与传统机器学习分类方法进行比较,试验结果表明:优化的卷积神经网络对大豆籽粒品质分类的准确率达到98.8%,平均检测一幅大豆单籽粒图像的时间为2.96 ms,可为大豆籽粒品质划分提供重要参考。
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| 关 键 词： | 大豆籽粒 品质 图像处理 分类识别 卷积神经网络 |
Study on the Optimization of Soybean Seed Selection based on Image Recognition and Convolution Neural Network |
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| ZHU Rong-sheng,YAN Xue-hui,CHEN Qing-shan.Study on the Optimization of Soybean Seed Selection based on Image Recognition and Convolution Neural Network[J].Soybean Science,2020(2):189-197. |
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| Authors: | ZHU Rong-sheng YAN Xue-hui CHEN Qing-shan |
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| Institution: | (College of Science,Northeast Agricultural University,Harbin 150030,China;College of Engineering,Northeast Agricultural University,Harbin 150030,China;College of Agricultural,Northeast Agricultural University,Harbin 150030,China) |
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| Abstract: | In order to quickly and accurately detect the quality of soybean seeds by the method of seed images recognition, a method of image screening and recognition based on convolution neural network is proposed, taking the classification of normal and abnormal quality seeds as an example. The data set of soybean seed quality was established, and convolution neural network was designed to extract the image features of soybean seed. In order to improve the classification accuracy and real-time performance, the convolution neural network was optimized from the aspects of design and selection of convolution neural network structure, reduction of over fitting, acceleration of training convergence speed, and enhancement of network robustness. Finally, the 6-layer convolution neural network with 4 convolution layers, 4 pooling layers and 2 fully connected layers were selected, L2 regularization and mini batch training methods were used for the network′s optimization training and test. Comparing the results with the traditional machine learning classification methods, the experimental results show that the accuracy of the optimized convolution neural network is 98.8%, and the average detection time of a single soybean seed image is 2.96 ms, which can provide an important reference for soybean seeds quality classification. |
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| Keywords: | Soybean seed Quality Image processing Classification Convolution neural network |
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