Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms
Machine learning (ML) methods, which are one of the subfields of artificial intelligence (AI) and have gained popularity in applications in recent years, play an important role in solving many challenges in aquaculture. In this study, the relationship between changes in the physico-chemical characte...
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| Format: | Article |
| Language: | English |
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Faculty of Agriculture, Ankara University
2025-01-01
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| Series: | Journal of Agricultural Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3870094 |
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| author | Hava Şimşek Mükerrem Oral Mesut Yılmaz Mustafa Çakır Nedim Özdemir Okan Oral |
| author_facet | Hava Şimşek Mükerrem Oral Mesut Yılmaz Mustafa Çakır Nedim Özdemir Okan Oral |
| author_sort | Hava Şimşek |
| collection | DOAJ |
| description | Machine learning (ML) methods, which are one of the subfields of artificial intelligence (AI) and have gained popularity in applications in recent years, play an important role in solving many challenges in aquaculture. In this study, the relationship between changes in the physico-chemical characteristics of water and feed consumption was evaluated using machine learning methods. Eleven physico-chemical characteristics (temperature, pH, dissolved oxygen, electrical conductivity, salinity, Nitrite nitrogen, nitrate nitrogen, ammonium nitrogen, total phosphorus, total suspended solids, and biological oxygen demand) of water were evaluated in terms of fish feed consumption by using ML methods. Among all the measured physico-chemical characteristics of water, temperature was determined to be the most important parameter to be evaluated in fish feeding. Moreover, pH2, eC2, TP2, TSS2, S2 and NO2 parameters detected in the outlet water are more important than those detected in the inlet water in terms of feed consumption. In the regression analysis carried out using ML techniques, the models developed with RF, GBM and XGBoost algorithms yielded better results. |
| format | Article |
| id | doaj-art-d22bb859cd6640ecb4e4749b96334ce8 |
| institution | Kabale University |
| issn | 1300-7580 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Faculty of Agriculture, Ankara University |
| record_format | Article |
| series | Journal of Agricultural Sciences |
| spelling | doaj-art-d22bb859cd6640ecb4e4749b96334ce82025-01-31T10:57:52ZengFaculty of Agriculture, Ankara UniversityJournal of Agricultural Sciences1300-75802025-01-01311717910.15832/ankutbd.147011145Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish FarmsHava Şimşek0https://orcid.org/0009-0001-1893-6777Mükerrem Oral1https://orcid.org/0000-0001-7960-1148Mesut Yılmaz2https://orcid.org/0000-0001-8799-3452Mustafa Çakır3https://orcid.org/0000-0002-1794-9242Nedim Özdemir4https://orcid.org/0000-0001-7410-6113Okan Oral5https://orcid.org/0000-0002-6302-4574MUĞLA SITKI KOÇMAN ÜNİVERSİTESİAKDENİZ ÜNİVERSİTESİAKDENİZ ÜNİVERSİTESİİSKENDERUN TEKNİK ÜNİVERSİTESİMUĞLA SITKI KOÇMAN ÜNİVERSİTESİAkdeniz Üniversitesi Mühendislik Fakültesi Mekatronik Mühendisliği BölümüMachine learning (ML) methods, which are one of the subfields of artificial intelligence (AI) and have gained popularity in applications in recent years, play an important role in solving many challenges in aquaculture. In this study, the relationship between changes in the physico-chemical characteristics of water and feed consumption was evaluated using machine learning methods. Eleven physico-chemical characteristics (temperature, pH, dissolved oxygen, electrical conductivity, salinity, Nitrite nitrogen, nitrate nitrogen, ammonium nitrogen, total phosphorus, total suspended solids, and biological oxygen demand) of water were evaluated in terms of fish feed consumption by using ML methods. Among all the measured physico-chemical characteristics of water, temperature was determined to be the most important parameter to be evaluated in fish feeding. Moreover, pH2, eC2, TP2, TSS2, S2 and NO2 parameters detected in the outlet water are more important than those detected in the inlet water in terms of feed consumption. In the regression analysis carried out using ML techniques, the models developed with RF, GBM and XGBoost algorithms yielded better results.https://dergipark.org.tr/en/download/article-file/3870094aquaculturefeed intakeartificial intelligencerainbow troutsustainability |
| spellingShingle | Hava Şimşek Mükerrem Oral Mesut Yılmaz Mustafa Çakır Nedim Özdemir Okan Oral Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms Journal of Agricultural Sciences aquaculture feed intake artificial intelligence rainbow trout sustainability |
| title | Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms |
| title_full | Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms |
| title_fullStr | Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms |
| title_full_unstemmed | Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms |
| title_short | Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms |
| title_sort | application of the machine learning methods to assess the impact of physico chemical characteristics of water on feed consumption in fish farms |
| topic | aquaculture feed intake artificial intelligence rainbow trout sustainability |
| url | https://dergipark.org.tr/en/download/article-file/3870094 |
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