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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2025-01-01
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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. |
| format | Article |
| id | doaj-art-2f46339406af44ffbafedb661d117e54 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| 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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