Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (21): 32-36.DOI: 10.3778/j.issn.1002-8331.1707-0506
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 discriminative 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.
discriminative deep belief networks,
exponential loss function
SONG Lijuan. Recognition model of disease image based on discriminative deep belief networks[J]. Computer Engineering and Applications, 2017, 53(21): 32-36.
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.
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