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.
Similar content being viewed by others
References
ADB–NACA (1998) Aquaculture sustainability and the environment, a report on a regional study and workshop. Asian Development Bank and Network of Aquaculture Centers in the Asia-Pacific, Bangkok
Boonyawiwat V, Patanasatienkul T, Kasornchandra J, Poolkhet C, Yaemkasem S, Hammell L, Davidson J (2017) Impact of farm management on expression of early mortality syndrome/acute hepatopancreatic necrosis disease (EMS/AHPND) in penaeid shrimp farms in Thailand. J Fish Dis. https://doi.org/10.1111/jfd.12545
Boonyawiwat V, Nga NTV, Bondadreantaso MG (2018) Risk factors associated with acute hepatopancreatic necrosis disease (AHPND) outbreak in the Mekong Delta, Viet Nam. Asian Fish Sci 31:226–241
Boyd C, Truong P (2019) Environmental factors and acute hepatopancreatic necrosis disease (AHPND) in shrimp ponds in Viet Nam: practices for reducing risks. Asian Fish Sci 31:121–136
COFI (2019) Fishery and aquaculture country profiles: the Socialist Republic of Viet Nam. FAO, Rome
Cournapeau D (2007) Scikit-learn: machine learning in Python. JMLR 12:2825–2830
Crane M (2019) Hepatopancreatic necrosis disease. In: OIE - manual of diagnostic tests for aquatic animals. World organisation for Animal Heath, Paris
Dang TL, Pham AT, Phan TV (2018) Acute Hepatopancreatic Necrosis Disease (AHPND) in Vietnam. Asian Fish Sci 31:274–282
Dhar AK, Piamsomboon P, Aranguren Caro LF, Kanrar S, Adami R Jr, Juan YS (2019) First report of acute hepatopancreatic necrosis disease (AHPND) occurring in the USA. Dis Aquat Organ. https://doi.org/10.3354/dao03330
Harston CT (1990) The neurological basis for neural computations. In: Maren AJ, Harston C, Pap RZ (eds) Handbook of neural computing applications. Academic Press, San Diego, pp 29–44
Hoffman GL (1976) Fish diseases and parasites in relation to the environment. Fish Pathol 10(2):123–128
Lopes JNS, Gonçalves ANA, Fujimoto RY, Carvalho JCC (2011) Diagnosis of fish diseases using artificial neural networks. Int J Comput Sci 8(6):68–74
Molnar C (2019) Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Lulu, Germany
Peña LD, Cabillon NAR, Catedral DD, Amar EC, Usero RC, Monotilla WD, Saloma CP (2015) Acute hepatopancreatic necrosis disease (AHPND) outbreaks in Penaeus vannamei and P. monodon cultured in the Philippines. Dis Aquat Organ 116(3):251–254
Ping SL, Liem TT (2000) Predicting shrimp disease occurrence: artificial neural networks vs logistic regression. Aquaculture 187(49):35–49. https://doi.org/10.1016/S0044-8486(00)00300-8
Rahman A, Tasnim S (2014) Application of machine learning techniques in aquaculture. Int J Comput Trends Technol. https://doi.org/10.14445/22312803/IJCTT-V10P137
Shinn AP, Pratoomyot J, Griffiths D, Trong TQ, Vu NT, Jiravanichpaisal P, Briggs M (2018) Asian shrimp production and the economic costs of disease. Asian Fish Sci 31:29–58
Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231
Venkateswara Rao P (2017) Computer aided shrimp disease diagnosis in aquaculture. IJRASET. https://doi.org/10.22214/ijraset.2017.2079
Zhang Z (2016) Introduction to machine learning: k-nearest neighbors. Ann Transl Med 4(11):218. https://doi.org/10.21037/atm.2016.03.37
Zheng Z, Aweya JJ, Wang F, Yao D, Lun J, Li S, Ma H, Zhang Y (2018) Acute Hepatopancreatic Necrosis Disease (AHPND)-related microRNAs in Litopenaeus vannamei infected with an AHPND-causing strain of Vibrio parahemolyticus. BMC Genom. https://doi.org/10.1186/s12864-018-4728-4
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12562-020-01427-z