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

Khiem, Nguyen Minh; Takahashi, Yuki; Oanh, Đặng Thị Hoàng; Hải, Trần Ngọc; Yasuma, Hiroki; Kimura, Nobuo

doi:10.1007/s12562-020-01427-z

Cited by 16 publications

(12 citation statements)

References 11 publications

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“…Fish infections caused by protozoans and bacteria have been diagnosed using artificial neural networks (Nayan et al, 2021). In 2020, machine learning was used to evaluate historical shrimp farm data and predict acute hepatopancreatic necrosis disease in shrimp farmed on the east coast of the Mekong Delta of Vietnam (Khiem et al, 2020). Hold‐out and cross‐validation tests were used to evaluate the accuracy of predictions based on logistic regression, artificial neural networks, decision trees, and K‐nearest neighbor analysis (Khiem et al, 2020).…”

Section: Role Of Ai In Fish Health Managementmentioning

confidence: 99%

“…In 2020, machine learning was used to evaluate historical shrimp farm data and predict acute hepatopancreatic necrosis disease in shrimp farmed on the east coast of the Mekong Delta of Vietnam (Khiem et al, 2020). Hold‐out and cross‐validation tests were used to evaluate the accuracy of predictions based on logistic regression, artificial neural networks, decision trees, and K‐nearest neighbor analysis (Khiem et al, 2020). Consumption of contaminated shellfish can cause severe illness and even death in humans, which is one of the major concerns for farmers.…”

Section: Role Of Ai In Fish Health Managementmentioning

confidence: 99%

“…Additionally, these technologies will be used soon. In addition to biological information evaluation, behavior analysis, and water quality prediction, this technology may diagnose fish disease, identify dead fish, and apply in other sectors (Khiem et al, 2020).…”

Section: Role Of Ai In Fish Health Managementmentioning

confidence: 99%

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Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Islam,

Ahammad,

Mohammed

2024

J World Aquaculture Soc

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Aquaculture is now the main source of seafood in human diets and is one of its fastest‐growing industries worldwide. However, the industry is facing several difficulties, including infectious diseases, the most significant limiting factor for aquaculture expansion. The impact of diseases on aquaculture growth, fecundity, mortality rates, and marketability is profound. Hence, the ability to predict disease outbreaks is crucial to overcoming these challenges. Various infectious agents such as bacteria, viruses, fungi, and parasites can cause significant losses of fish in intensive aquaculture practices. In an aquaculture environment, the high host density coupled with restricted water flow promotes pathogen spread. Early detection of disease is crucial for farmers as mortality rates can reach as high as 100% if left untreated. Therefore, new techniques and technical solutions for disease management in aquaculture are required. In this context, data analytics technologies, such as internet of things (IoT) sensors, artificial intelligence, and machine learning, allow farmers to proactively monitor their farms and detect potential disease outbreaks before they strike. Here, we highlighted the potential of machine learning algorithms in early pathogen detection and the possibilities of intelligent aquaculture in controlling disease outbreaks at the farm level. IoT is currently a popular study topic for smarter and sustainable aquaculture, as seen by the growing interest and broad overall assumptions. Therefore, this review aims to provide comprehensive information on the various aspects and challenges associated with modern technologies for controlling pathogenic microorganisms, as well as the potential benefits of using the IoT to improve fish health and welfare in aquaculture.

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Section: Role Of Ai In Fish Health Managementmentioning

confidence: 99%

Section: Role Of Ai In Fish Health Managementmentioning

confidence: 99%

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Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Islam,

Ahammad,

Mohammed

2024

J World Aquaculture Soc

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“…Although machine learning algorithms are useful for making predictions, they still depend on accurate datasets and each algorithm has its own strength of prediction. For example, the random forest algorithm was outperformed in terms of prediction by a dataset of power generation and power system security [ 25 ], and a logistic regression performed better than a neural network in the prediction of occurrence of early mortality syndrome [ 17 ]. A study preferred a probabilistic neural network to a logistic regression model when analyzing a dataset of general shrimp diseases [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has been used for predicting disease occurrence in cultured shrimp. Previous studies used machine learning to predict the occurrence of shrimp disease [16][17][18] and create applications for aquaculture [19]. Machine learning has also been used in sales forecasting [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A novel machine learning approach to predict the export price of seafood products based on competitive information: The case of the export of Vietnamese shrimp to the US market

et al. 2022

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Predicting the export price of shrimp is important for Vietnam’s fisheries. It not only promotes product quality but also helps policy makers determine strategies to develop the national shrimp industry. Competition in global markets is considered to be an important factor, one that significantly influences price. In this study, we predicted trends in the export price of Vietnamese shrimp based on competitive information from six leading exporters (China, India, Indonesia, Thailand, Ecuador, and Chile) who, alongside Vietnam, also export shrimp to the US. The prediction was based on a dataset collected from the US Department of Agriculture (USDA), the Food and Agriculture Organization of the United Nations (FAO), and the World Trade Organization (WTO) (May-1995 to May-2019) that included price, required farming certificates, and disease outbreak data. A super learner technique, which combined 10 single algorithms, was used to make predictions in selected base periods (3, 6, 9, and 12 months). It was found that the super learner obtained results in all base periods that were more accurate and stable than any candidate algorithms. The impacts of variables in the predictive model were interpreted by a SHapley Additive exPlanations (SHAP) analysis to determine their influence on the price of Vietnamese exports. The price of Indian, Thai, and Chinese exports highlighted the advantages of being a World Trade Organization member and the disadvantages of the prevalence of shrimp disease in Vietnam, which has had a significant impact on the Vietnamese shrimp export price.

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Predicting the price of Vietnamese shrimp products exported to the US market using machine learning

et al. 2021

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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

Cited by 16 publications

References 11 publications

Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

A novel machine learning approach to predict the export price of seafood products based on competitive information: The case of the export of Vietnamese shrimp to the US market

Predicting the price of Vietnamese shrimp products exported to the US market using machine learning

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