To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment.In this paper,an advanced neural network model to identify t...To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment.In this paper,an advanced neural network model to identify the characteristics of the oplegnathus puncta-tus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set.First of all,a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effective-ness of the method in this paper.And then,the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set,which combines the edge fea-tures extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model.Finally,an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure.The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved,which reach 98.55%and 69.18%.展开更多
基金The work of this paper is jointly supported by the National Natural Science Foundation of China (U1706220,61472172)the Yantai Key R&D Project (2017ZH057,2018ZDCX003,2019XDHZ084).
文摘To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment.In this paper,an advanced neural network model to identify the characteristics of the oplegnathus puncta-tus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set.First of all,a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effective-ness of the method in this paper.And then,the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set,which combines the edge fea-tures extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model.Finally,an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure.The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved,which reach 98.55%and 69.18%.
