Abstract
Predicting the outbreak of disease is essential when managing shrimp farms. Acute hepatopancreatic necrosis disease (AHPND) caused by Vibrio parahaemolyticus is a serious disease in shrimp. It is essential that shrimp farmers on the east coast of the Mekong Delta detect the disease as early as possible, because the mortality rate can reach 100%. Here, we used machine learning to predict AHPND development based on data collected since 2010 from shrimp farms in Tra Vinh, Ben Tre, Bac Lieu, and Ca Mau provinces. We initially hypothesized that the dependent variable, AHPND, was affected by 31 independent variables, but ultimately used 15 key variables to train the models. Logistic regression, artificial neural network, decision tree, and K-nearest neighbor analyses were performed, and the accuracy of the predictions was evaluated using hold-out and cross-validation tests. Logistic regression, as the most stable algorithm, was thus used to predict AHPND outbreaks in shrimp farms.
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Acknowledgements
This study was funded in part by the Can Tho University Improvement Project VN14-P6, supported by a Japanese Official Development Assistance (ODA) loan. Data collection was partially funded by the technical cooperation project “Building capacity for Can Tho University to be an excellent institution of education, scientific research and technology transfer” of the Japan International Cooperation Agency (JICA). We are grateful to the anonymous reviewers who made helpful comments.
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Khiem, N.M., Takahashi, Y., Oanh, D.T.H. et al. The use of machine learning to predict acute hepatopancreatic necrosis disease (AHPND) in shrimp farmed on the east coast of the Mekong Delta of Vietnam. Fish Sci 86, 673–683 (2020). https://doi.org/10.1007/s12562-020-01427-z
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DOI: https://doi.org/10.1007/s12562-020-01427-z