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Discrimination method for disease degree of large yellow croaker infected with Cryptocaryon irritans

A technology that stimulates Cryptocaryoniasis and stimulates Cryptocaryonia, applied in fish farming, application, climate change adaptation, etc., achieves high accuracy, reduces workload, and has good application prospects

Inactive Publication Date: 2012-07-11
XIAMEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Moreover, currently using machine learning methods to analyze nonlinear problems, analyze the impact of a large number of factors on certain events and predict the level at which certain events may occur is relatively advanced, reasonable, and highly accurate, and there are also many patent reports, such as Northwest Power Grid "Wind Speed ​​Prediction Method Based on Neural Network" (Chinese Patent: 200910219123.31) invented by Co., Ltd.; "A Short-Term Wind Speed ​​Prediction Method for Wind Farm" invented by Zhejiang University (Chinese Patent: 201019146035.5); invented by Sinopec Corporation "Prediction Method of Pyrolysis Product Yield Based on Support Vector Machine" (Chinese Patent: 200810225363.X); "Random Forest Classification Method and Classifier Based on Contrast Mode" invented by Peking University (Chinese Patent: 201010265846.X), etc. However, there is no report on the use of machine learning to analyze and predict the impact of water quality factors on the occurrence of aquatic animal diseases. Moreover, many literatures show that the random forest method is better than other methods in terms of classification and prediction performance.

Method used

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  • Discrimination method for disease degree of large yellow croaker infected with Cryptocaryon irritans
  • Discrimination method for disease degree of large yellow croaker infected with Cryptocaryon irritans
  • Discrimination method for disease degree of large yellow croaker infected with Cryptocaryon irritans

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Experimental program
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Effect test

Embodiment 1

[0045] 1. Data preprocessing

[0046] The monthly monitoring data of water quality and other environmental factors (see Table 1) and the corresponding stimuli were obtained from Sandu Bay in Ningde, Fujian, Luoyuan Bay in Fuzhou, and Shacheng Port in Fuding. The occurrence of cryptocystosis (water quality data comes from the East China Sea Sub-bureau of the State Oceanic Administration, Mindong Marine Environmental Monitoring Center Station, disease data comes from the Disease Prevention Department of Fujian Marine Aquaculture Technology Extension General Station, and data collection and analysis are in accordance with national standards). For convenience, the impact factors are recorded as 1 to 14 according to the order in Table 1; the disease is divided into four grades according to its severity: normal, a small amount of disease, a moderate disease, and a large area of ​​disease, and the records are 1 to 4; the months are recorded as 1 to 12 Months are recorded as 1 to 12; ...

Embodiment 2

[0061] Example 2: Construction and screening of dimensionality reduction methods

[0062] 1. Calculation of the weight value of each impact factor

[0063] On the basis of the above-mentioned establishment of the discriminant method, the influence weight of each influencing factor on the disease condition is analyzed. There are many ways to calculate weight values ​​in RF, such as variance impurity, entropy impurity, etc. In this experiment, the commonly used Gini impurity (Gini impurity) decline number is selected as the index for importance ranking.

[0064] The calculation rule of Gini impurity is as follows:

[0065] i(N)=∑ i≠j P(ω i )P(ω j )=1-∑ k P 2 (ω k )

[0066] Gini impurity drop number can be recorded as:

[0067] Δi(N)=i(N)-i(N L ) P L -i(N R )(1-P L )

[0068] Where: P(ω j ) means that the Nth node belongs to ω j The frequency of the sample number of the disease type in the total sample number; P L Indicates that when a tree in RF performs node s...

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Abstract

Disclosed is a discrimination method for disease degree of large yellow croakers infected with Cryptocaryon irritans, and the method relates to large yellow croakers. The method comprises firstly performing data preprocessing, building a mathematical method by using the 14 impact factors, and building a dimensionality reduction method. The method has features of being quantitative in discrimination, simple and convenient, and high in accuracy and reliability. Random sampling test shows that by use of the method, the severe degree of disease of large yellow croaker infected with Cryptocaryon irritans can be accurately determined by use of measured values of water environment factors, and the accuracy rate is up to more than 90%. The method can be applied to determine the sea area and severity of possible Cryptocaryon irritans infection, and used to direct the prevention and control of Cryptocaryon irritans infection in large yellow croakers. The method is suitable for discrimination on occurrence of Cryptocaryon irritans infection, and also provides an important reference for Cryptocaryon irritans infection in other marine fishes.

Description

technical field [0001] The invention relates to large yellow croaker, in particular to a method for discriminating the disease degree of cryptocystitis stimulated by large yellow croaker. Background technique [0002] Cryptocaryon stimuli is a highly transmissible parasite that seriously threatens marine fishes at present. It has been listed as a new version of the "List of Animal Diseases Types I, II, and III" issued by the Ministry of Agriculture on December 11, 2008 Class II animal diseases. Especially this disease has brought serious loss to large yellow croaker breeding industry. For example, since 2005, Cryptocaryoniasis and bacterial secondary infection have caused economic losses of more than 300 million yuan to the large yellow croaker farming industry every year. The severe outbreak of cryptocystosis stimulated by large yellow croaker is closely related to the dynamic and unstable open and complex aquaculture ecosystem in the main production area of ​​large yello...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A01K61/00
CPCY02A40/81
Inventor 蔡晓鹏吕伟航王洪杰毛勇苏永全王军丁少雄
Owner XIAMEN UNIV
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