CAJ | 학술논문

试验首先提取甘肃大麦病斑的颜色和纹理特征，以特征向量为输入向量来构造大麦病害神经网络分类器模型.然后利用神经网络对采集到的训练集病害图像进行分类模型训练，最后以随机选取的两组测试图像进行了分类试验.结果表明：大麦病害神经网络分类器模型对甘肃大麦病害的整体识别正确率达到86.7%以上.因而，基于神经网络的大麦病害图像识别研究为大麦田间病害归类诊治提供了新型技术，为西北特别是甘肃大麦病害的早期诊断与科学防治奠定了技术基础.
시험수선제취감숙대맥병반적안색화문리특정，이특정향량위수입향량래구조대맥병해신경망락분류기모형.연후이용신경망락대채집도적훈련집병해도상진행분류모형훈련，최후이수궤선취적량조측시도상진행료분류시험.결과표명：대맥병해신경망락분류기모형대감숙대맥병해적정체식별정학솔체도86.7%이상.인이，기우신경망락적대맥병해도상식별연구위대맥전간병해귀류진치제공료신형기술，위서북특별시감숙대맥병해적조기진단여과학방치전정료기술기출.
Barley diseases have a great negative impact on the yield and quality.Firstly,disease spot col-or and texture features were extracted from barley field images in Gansu Province,and the feature vectors were used as input vector to establish barley diseases classifier model.Then,the neural network was ap-plied to train classified model with collected images as training set.Finally,two groups of random selected images as test sets were used to perform classified verification experiments.The experimental results showed that the overall accuracy of barley diseases recognition model was above 86.7%.Therefore,Barley disease image recognition based on neural network provides a new technology for the classified treatment of barley diseases,and also can lay a solid technical foundation for the early diagnosis and the scientific pre-vention of barley diseases.