A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone

The cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilisti...

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Main Authors: Seung-Won Seo, Gyumin Choi, Ho-Jin Jung, Mi-Jin Choi, Young-Dae Oh, Hyun-Seok Jang, Han-Kyu Lim, Seongil Jo
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/2/708
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author Seung-Won Seo
Gyumin Choi
Ho-Jin Jung
Mi-Jin Choi
Young-Dae Oh
Hyun-Seok Jang
Han-Kyu Lim
Seongil Jo
author_facet Seung-Won Seo
Gyumin Choi
Ho-Jin Jung
Mi-Jin Choi
Young-Dae Oh
Hyun-Seok Jang
Han-Kyu Lim
Seongil Jo
author_sort Seung-Won Seo
collection DOAJ
description The cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilistic machine learning approach based on a Bayesian framework to predict abalone growth by modeling key environmental factors, including water temperature, pH, salinity, nutrient supply, and dissolved oxygen levels. The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. Our analysis reveals that time, dissolved oxygen, salinity, and nutrient supply are the most critical factors influencing growth, while water temperature and pH play relatively minor roles under controlled indoor farming conditions. Interaction analysis highlights the non-linear dependencies among factors, such as the combined effects of salinity and nutrient supply. The proposed model not only improves prediction accuracy compared to baseline methods, but also provides actionable insights into the environmental dynamics that optimize abalone growth. These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.
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spelling doaj-art-2f46339406af44ffbafedb661d117e542025-01-24T13:20:33ZengMDPI AGApplied Sciences2076-34172025-01-0115270810.3390/app15020708A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured AbaloneSeung-Won Seo0Gyumin Choi1Ho-Jin Jung2Mi-Jin Choi3Young-Dae Oh4Hyun-Seok Jang5Han-Kyu Lim6Seongil Jo7Silicogen Inc., Yongin 16954, Republic of KoreaDepartment of Statistics and Data Science, Inha University, Incheon 22212, Republic of KoreaSilicogen Inc., Yongin 16954, Republic of KoreaSmart Aqua Farm Convergence Research Center, Mokpo National University, Muan 58554, Republic of KoreaSmart Aqua Farm Convergence Research Center, Mokpo National University, Muan 58554, Republic of KoreaSmart Aqua Farm Convergence Research Center, Mokpo National University, Muan 58554, Republic of KoreaSmart Aqua Farm Convergence Research Center, Mokpo National University, Muan 58554, Republic of KoreaDepartment of Statistics and Data Science, Inha University, Incheon 22212, Republic of KoreaThe cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilistic machine learning approach based on a Bayesian framework to predict abalone growth by modeling key environmental factors, including water temperature, pH, salinity, nutrient supply, and dissolved oxygen levels. The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. Our analysis reveals that time, dissolved oxygen, salinity, and nutrient supply are the most critical factors influencing growth, while water temperature and pH play relatively minor roles under controlled indoor farming conditions. Interaction analysis highlights the non-linear dependencies among factors, such as the combined effects of salinity and nutrient supply. The proposed model not only improves prediction accuracy compared to baseline methods, but also provides actionable insights into the environmental dynamics that optimize abalone growth. These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.https://www.mdpi.com/2076-3417/15/2/708abalone growthheteroscedasticityindoor abalone farmingweighted Bayesian kernel machine regression
spellingShingle Seung-Won Seo
Gyumin Choi
Ho-Jin Jung
Mi-Jin Choi
Young-Dae Oh
Hyun-Seok Jang
Han-Kyu Lim
Seongil Jo
A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone
Applied Sciences
abalone growth
heteroscedasticity
indoor abalone farming
weighted Bayesian kernel machine regression
title A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone
title_full A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone
title_fullStr A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone
title_full_unstemmed A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone
title_short A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone
title_sort weighted bayesian kernel machine regression approach for predicting the growth of indoor cultured abalone
topic abalone growth
heteroscedasticity
indoor abalone farming
weighted Bayesian kernel machine regression
url https://www.mdpi.com/2076-3417/15/2/708
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