A Method to Predict Hemorrhage Disease of Grass Carp Tends

Authors: Zhongxu Chen, Jun Yang, Heyue Mao, Xiaoyu Zheng

Abstract:

Hemorrhage Disease of Grass Carp (HDGC) is a kind of commonly occurring illnesses in summer, and the extremely high death rate result in colossal losses to aquaculture. As the complex connections among each factor which influences aquiculture diseases, there-s no quit reasonable mathematical model to solve the problem at present.A BP neural network which with excellent nonlinear mapping coherence was adopted to establish mathematical model; Environmental factor, which can easily detected, such as breeding density, water temperature, pH and light intensity was set as the main analyzing object. 25 groups of experimental data were used for training and test, and the accuracy of using the model to predict the trend of HDGC was above 80%. It is demonstrated that BP neural network for predicating diseases in HDGC has a particularly objectivity and practicality, thus it can be spread to other aquiculture disease.

Keywords: Aquaculture, Hemorrhage Disease of Grass Carp, BP Neural Network

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074509

References:

[1] K. Bergerson and D. Wunsch. A Commodity Trading Model Based on a Neural Network-Expert System Hybrid. In Neural Networks in Finance and Investing, Chapter 23, Pages 403-410. Probus Publishing Company, 1993.
[2] Y. Yoon and G. Swales. Predicting Stock Price Performance: A Neural Network Approach. In Neural Networks in Finance and Investing, Chapter 19, Pages 329-342. Probus Publishing Company, 1993.
[3] Wei Chuang and Huang Lin. Research on the Monitoring System of Aquaculture with Multi-Environmental Factors. In Electronic Sci.&Tech, Chapter 23, Pages 29-30.
[4] Zhan Defeng, Neural Network Application Design. Beijing: China Machine Press,2009.1. Chapter 23, Pages 227. (in Chinese)
[5] Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning Internal Representations by Error Propagation, Chapter 8 (318-362) in Parallel Distributed Processing I D. E. Rumelhart and J. L. McClelland (eds.) MIT Press.
[6] Marco Dorigo, Gianni Di Caro and Luca M. Gambardella, Ant Algorithms for Discrete Optimization. In Artificial Life, Spring 1999, Vol. 5, No. 2, Pages 137-172.
[7] Zhan Defeng, Neural Network Application Design. Beijing: China Machine Press,2009.1. Chapter 23, Pages 261. (in Chinese)