Recognition of abnormal body surface characteristics of oplegnathus punctatus

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture Pub Date : 2022-12-01 DOI:10.1016/j.inpa.2021.04.009

Beibei Li , Jun Yue , Shixiang Jia , Qing Wang , Zhenbo Li , Zhenzhong Li

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引用次数: 2

Abstract

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 punctatus 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 effectiveness 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 features 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%.

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斑胸蛇体表异常特征的识别

鉴定斑点鳞鱼的异常特征对养殖环境中虹膜病毒病的检测具有重要意义。本文在建立数据集的基础上，提出了一种先进的神经网络模型，用于识别斑鳞鱼的特征并预测其患虹膜病毒病的不同时期。首先，为了验证本文方法的有效性，建立了马尾蛇标准格式数据集和异常格式数据集。然后，针对异常格式数据集，采用特征提取融合方法，将改进的多模板Sobel算子提取的边缘特征与HSV模型提取的颜色特征结合起来进行预处理。最后，通过对VGG和GoogleNet神经网络结构的融合和改进，形成改进的VGG-GoogleNet网络识别模型。实验结果表明，异常格式数据集和标准格式数据集对虹膜病毒病的预测准确率均有提高，分别达到98.55%和69.18%。

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来源期刊

Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology

CiteScore

21.10

自引率

0.00%

发文量

期刊介绍： Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining