Smart aquaponics: An innovative machine learning framework for fish farming optimization
<p>This study presents an innovative approach to aquaponics by integrating artificial intelligence (AI). The system addresses sustainability challenges by utilizing a novel approach to machine learning to create a fully sustainable system that improves nutrition and fish growth in aquaponics....
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513555958595584 |
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| author | Amith Khandakar (14151981) |
| author2 | I.M. Elzein (19757043) Md. Nahiduzzaman (17873875) Mohamed Arselene Ayari (16869978) Azad Ibn Ashraf (19757046) Lino Korah (19757049) Alhareth Zyoud (19757052) Hassan Ali (3348749) Ahmed Badawi (19757055) |
| author2_role | author author author author author author author author |
| author_facet | Amith Khandakar (14151981) I.M. Elzein (19757043) Md. Nahiduzzaman (17873875) Mohamed Arselene Ayari (16869978) Azad Ibn Ashraf (19757046) Lino Korah (19757049) Alhareth Zyoud (19757052) Hassan Ali (3348749) Ahmed Badawi (19757055) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amith Khandakar (14151981) I.M. Elzein (19757043) Md. Nahiduzzaman (17873875) Mohamed Arselene Ayari (16869978) Azad Ibn Ashraf (19757046) Lino Korah (19757049) Alhareth Zyoud (19757052) Hassan Ali (3348749) Ahmed Badawi (19757055) |
| dc.date.none.fl_str_mv | 2024-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compeleceng.2024.109590 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Smart_aquaponics_An_innovative_machine_learning_framework_for_fish_farming_optimization/27130104 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Distributed computing and systems software Machine learning Hydroponics Aquaponics Agricultural smart system Machine learning Internet of things |
| dc.title.none.fl_str_mv | Smart aquaponics: An innovative machine learning framework for fish farming optimization |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This study presents an innovative approach to aquaponics by integrating artificial intelligence (AI). The system addresses sustainability challenges by utilizing a novel approach to machine learning to create a fully sustainable system that improves nutrition and fish growth in aquaponics. The study focuses on predicting the length and weight of fish species by analyzing different environmental parameters, including pH, ammonia, and nitrate levels. Data preprocessing integrates nearest-neighbor interpolation and feature standardization to ensure quality and consistency. The light gradient-boosting machine (LightGBM) machine learning model, optimized by five-fold cross-validation, emerges as the superior predictor. Moreover, a novel aspect of the study is the integration of local interpretable model-agnostic explanations (LIME) for enhanced model transparency. The outcome helps to understand the impacts of individual characteristics on the predictions. External validation using different data reaffirms the models' generalizability. Hence, the integration of renewable energy, artificial intelligence, and rigorous analysis shows the potential to improve sustainable agriculture, paving the way for efficient and environmentally conscious indoor farming practices. However, the main framework of this study has the advantage of replicating other fish species using a new set of parameters.</p><h2>Other Information</h2> <p> Published in: Computers and Electrical Engineering<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109590" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109590</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_11b5076950b378809d704bf4b4b6bc38 |
| identifier_str_mv | 10.1016/j.compeleceng.2024.109590 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27130104 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Smart aquaponics: An innovative machine learning framework for fish farming optimizationAmith Khandakar (14151981)I.M. Elzein (19757043)Md. Nahiduzzaman (17873875)Mohamed Arselene Ayari (16869978)Azad Ibn Ashraf (19757046)Lino Korah (19757049)Alhareth Zyoud (19757052)Hassan Ali (3348749)Ahmed Badawi (19757055)Information and computing sciencesDistributed computing and systems softwareMachine learningHydroponicsAquaponicsAgricultural smart systemMachine learningInternet of things<p>This study presents an innovative approach to aquaponics by integrating artificial intelligence (AI). The system addresses sustainability challenges by utilizing a novel approach to machine learning to create a fully sustainable system that improves nutrition and fish growth in aquaponics. The study focuses on predicting the length and weight of fish species by analyzing different environmental parameters, including pH, ammonia, and nitrate levels. Data preprocessing integrates nearest-neighbor interpolation and feature standardization to ensure quality and consistency. The light gradient-boosting machine (LightGBM) machine learning model, optimized by five-fold cross-validation, emerges as the superior predictor. Moreover, a novel aspect of the study is the integration of local interpretable model-agnostic explanations (LIME) for enhanced model transparency. The outcome helps to understand the impacts of individual characteristics on the predictions. External validation using different data reaffirms the models' generalizability. Hence, the integration of renewable energy, artificial intelligence, and rigorous analysis shows the potential to improve sustainable agriculture, paving the way for efficient and environmentally conscious indoor farming practices. However, the main framework of this study has the advantage of replicating other fish species using a new set of parameters.</p><h2>Other Information</h2> <p> Published in: Computers and Electrical Engineering<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109590" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109590</a></p>2024-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compeleceng.2024.109590https://figshare.com/articles/journal_contribution/Smart_aquaponics_An_innovative_machine_learning_framework_for_fish_farming_optimization/27130104CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/271301042024-11-01T00:00:00Z |
| spellingShingle | Smart aquaponics: An innovative machine learning framework for fish farming optimization Amith Khandakar (14151981) Information and computing sciences Distributed computing and systems software Machine learning Hydroponics Aquaponics Agricultural smart system Machine learning Internet of things |
| status_str | publishedVersion |
| title | Smart aquaponics: An innovative machine learning framework for fish farming optimization |
| title_full | Smart aquaponics: An innovative machine learning framework for fish farming optimization |
| title_fullStr | Smart aquaponics: An innovative machine learning framework for fish farming optimization |
| title_full_unstemmed | Smart aquaponics: An innovative machine learning framework for fish farming optimization |
| title_short | Smart aquaponics: An innovative machine learning framework for fish farming optimization |
| title_sort | Smart aquaponics: An innovative machine learning framework for fish farming optimization |
| topic | Information and computing sciences Distributed computing and systems software Machine learning Hydroponics Aquaponics Agricultural smart system Machine learning Internet of things |