计算机工程与应用2017，Vol.53Issue(21)：32-36,48,6.DOI:10.3778/j.issn.1002-8331.1707-0506
基于区分深度置信网络的病害图像识别模型
Recognition model of disease image based on discriminative deep belief networks
摘要
Abstract
To detect and identify the disease of Chinese Wolfberry in time and accurately is very important on the disease monitor, prediction, early warning,treatment and the construction of agricultural information and intelligence. The deep architecture of disease image classification and identification is proposed based on discriminative deep belief networks. First of all, this paper automatically crops the leaf disease image of Chinese Wolfberry into the sub-image containing typical spots, and then researches segmentation under complex background and the image feature extraction, the features is a total of 147 on color feature, texture feature and shape feature. Disease recognition model is established with discrimi-native deep belief networks and exponential loss function. Experimental results show that, the method has good effect on image recognition. Compared with the support vector machine, the disease image recognition model based on discriminative deep belief network not only can effectively use the high-level representation of low-level image features but also can solve the problem of data annotation image recognition.关键词
病害图像/区分深度置信网络/指数损失函数Key words
disease image/discriminative deep belief networks/exponential loss function分类
信息技术与安全科学引用本文复制引用
宋丽娟..基于区分深度置信网络的病害图像识别模型[J].计算机工程与应用,2017,53(21):32-36,48,6.基金项目
国家自然科学基金(No.61363018) （No.61363018）
宁夏高等学校科学技术研究项目(No.NGY2014055,No.NGY2016016). （No.NGY2014055,No.NGY2016016）
