摘要
不同形式的机械损伤对蓖麻种子发芽生长和榨油后的蓖麻油质量影响不同,因此对产生机械损伤的蓖麻种子进行识别分类非常重要。提出了基于卷积神经网络的蓖麻种子损伤分类算法。以种壳缺失、裂纹和完整蓖麻种子(无损伤)的分类为例,构建了蓖麻种子训练集和测试集,搭建2个卷积层(每个卷积层8个卷积核)、2个池化层和1个全连接层(128个节点),实现分类。为提高分类的准确性和实时性,调整网络结构以及优化批量尺寸参数,得到较优的网络结构和批量尺寸;利用上下左右翻转扩充样本,改变优化器、学习率以及正则化系数对该网络进行组合试验,获得准确率及效率较优的组合。通过Dropout优化减小卷积神经网络模型的过拟合。试验结果表明:卷积层为5层、池化层为5层、批量尺寸为32时,该网络模型平均测试准确率为92.52%。在组合试验中,Sgdm优化器更新网络可以提高网络的分类性能;数据扩增可以增加样本的多样性,减小过拟合现象;通过Dropout优化卷积神经网络模型的过拟合;选择学习率为0.01,正则化系数为0.0005时,模型分类准确率达到94.82%,其中种壳缺失蓖麻种子准确率为95.60%,裂纹蓖麻种子准确率为93.33%,完整蓖麻种子准确率为95.51%,平均检测单粒蓖麻种子的时间为0.1435 s。最后,开发蓖麻种子损伤分类系统,验证结果为:种壳缺失蓖麻种子的准确率为96.67%,裂纹蓖麻种子的准确率为80.00%,完整蓖麻种子的准确率为86.67%。该卷积神经网络模型在损伤蓖麻种子分类时具有较高的识别准确率,可在蓖麻种子在线实时分类的检测系统中应用。
Different forms of mechanical damage affect the germination and growth of castor seeds and the quality of castor oil after oil extraction.Therefore,it is very important to identify and classify castor seeds with mechanical damage.The classification of castor seeds with seed shells missing and castor seeds with cracks and intact castor seeds(without damage)was taken as an example.The training set and test set of castor seeds were constructed,which included two convolutional layers(eight convolution nuclei per convolutional layer),two pooling layers and one full connecting layer(128 nodes).In order to improve the accuracy and real-time performance of the convolutional neural network,the network structure was adjusted and the batch_size parameters were optimized to obtain better network structure and batch_size.The sample was expanded by turning up and down,and the learning rate and regularization coefficient of the optimizer were changed to conduct a combination test on the network,so as to obtain a combination with better accuracy and efficiency.Finally,the over-fitting of the convolutional neural network model was reduced through Dropout optimization.The experimental results showed that the average test accuracy of the network model was 92.52%when the convolution layer was 5,pooling layers was 5 and the batch_size was 32.In combination test,the Sgdm optimizer can improve the classification performance of the network by updating the network.Data amplification can increase the diversity of samples and thus reduce the over-fitting phenomenon.After the over-fitting of the convolutional neural network model was reduced by Dropout optimization,the average test accuracy of the convolutional model was 93.45%,which was 0.93 percentage points higher than that before optimization.When the learning rate was 0.01 and the regularization coefficient was 0.0005,the classification accuracy of the model could reach 94.82%after dropout optimization.The accuracy of missing seed shell castor seeds was 95.60%,the accuracy rate of cracked castor seeds was 93.33%,the accuracy rate of intact castor seeds was 95.51%,and the average detection time of a single castor seed image was 0.1435 s.Finally,the system for castor seeds damage classification was developed.The results of verification of the algorithm showed that the accuracy of seed shell missing castor seeds was 96.67%,that of cracked castor seeds was 80.00%,and that of complete castor seeds was 86.67%.The combined test convolutional neural network model had a high recognition accuracy in the classification of damaged castor seeds,and the convolutional model can be applied to the detection system for the real-time classification of castor seeds.
作者
侯俊铭
姚恩超
朱红杰
HOU Junming;YAO Enchao;ZHU Hongjie(College of Engineering, Shenyang Agricultural University, Shenyang 100866, China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第S01期440-449,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(51475312)。
关键词
蓖麻种子
损伤特征
分类
组合试验
卷积神经网络
castor seeds
damage feature
classification
composite test
convolutional neural network
