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
| Autor: | Nobuo Kimura, Yuki Takahashi, Tran Ngoc Hai, Dang Thi Hoang Oanh, Hiroki Yasuma, Nguyen Minh Khiem |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
East coast animal structures biology business.industry 010604 marine biology & hydrobiology Mortality rate Vibrio parahaemolyticus fungi Outbreak 04 agricultural and veterinary sciences Disease Aquatic Science biology.organism_classification Machine learning computer.software_genre Logistic regression 01 natural sciences Shrimp Shrimp farming 040102 fisheries 0401 agriculture forestry and fisheries Artificial intelligence business computer |
| Zdroj: | Fisheries Science. 86:673-683 |
| ISSN: | 1444-2906 0919-9268 |
| DOI: | 10.1007/s12562-020-01427-z |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |