Temporal fusion transformer-based prediction in aquaponics
<p dir="ltr">Aquaponics offers a soilless farming ecosystem by merging modern hydroponics with aquaculture. The fish food is provided to the aquaculture, and the ammonia generated by the fish is converted to nitrate using specialized bacteria, which is an essential resource for veget...
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2023
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| _version_ | 1864513530579910656 |
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| author | Ahmet Metin (12832543) |
| author2 | Ahmet Kasif (17787560) Cagatay Catal (6897842) |
| author2_role | author author |
| author_facet | Ahmet Metin (12832543) Ahmet Kasif (17787560) Cagatay Catal (6897842) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmet Metin (12832543) Ahmet Kasif (17787560) Cagatay Catal (6897842) |
| dc.date.none.fl_str_mv | 2023-06-06T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11227-023-05389-8 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Temporal_fusion_transformer-based_prediction_in_aquaponics/24997703 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Horticultural production Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Time series forecasting Aquaponics Transformers Anomaly detection Deep learning |
| dc.title.none.fl_str_mv | Temporal fusion transformer-based prediction in aquaponics |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Aquaponics offers a soilless farming ecosystem by merging modern hydroponics with aquaculture. The fish food is provided to the aquaculture, and the ammonia generated by the fish is converted to nitrate using specialized bacteria, which is an essential resource for vegetation. Fluctuations in the ammonia levels affect the generated nitrate levels and influence farm yields. The sensor-based autonomous control of aquaponics can offer a highly rewarding solution, which can enable much more efficient ecosystems. Also, manual control of the whole aquaponics operation is prone to human error. Artificial Intelligence-powered Internet of Things solutions can reduce human intervention to a certain extent, realizing more scalable environments to handle the food production problem. In this research, an attention-based Temporal Fusion Transformers deep learning model was proposed and validated to forecast nitrate levels in an aquaponics environment. An aquaponics dataset with temporal features and a high number of input lines has been employed for validation and extensive analysis. Experimental results demonstrate significant improvements of the proposed model over baseline models in terms of MAE, MSE, and Explained Variance metrics considering one-hour sequences. Utilizing the proposed solution can help enhance the automation of aquaponics environments.</p><h2>Other Information</h2><p dir="ltr">Published in: The Journal of Supercomputing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11227-023-05389-8" target="_blank">https://dx.doi.org/10.1007/s11227-023-05389-8</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a2815f56197e7c64343edff1debcd64c |
| identifier_str_mv | 10.1007/s11227-023-05389-8 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24997703 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Temporal fusion transformer-based prediction in aquaponicsAhmet Metin (12832543)Ahmet Kasif (17787560)Cagatay Catal (6897842)Agricultural, veterinary and food sciencesHorticultural productionInformation and computing sciencesArtificial intelligenceDistributed computing and systems softwareMachine learningTime series forecastingAquaponicsTransformersAnomaly detectionDeep learning<p dir="ltr">Aquaponics offers a soilless farming ecosystem by merging modern hydroponics with aquaculture. The fish food is provided to the aquaculture, and the ammonia generated by the fish is converted to nitrate using specialized bacteria, which is an essential resource for vegetation. Fluctuations in the ammonia levels affect the generated nitrate levels and influence farm yields. The sensor-based autonomous control of aquaponics can offer a highly rewarding solution, which can enable much more efficient ecosystems. Also, manual control of the whole aquaponics operation is prone to human error. Artificial Intelligence-powered Internet of Things solutions can reduce human intervention to a certain extent, realizing more scalable environments to handle the food production problem. In this research, an attention-based Temporal Fusion Transformers deep learning model was proposed and validated to forecast nitrate levels in an aquaponics environment. An aquaponics dataset with temporal features and a high number of input lines has been employed for validation and extensive analysis. Experimental results demonstrate significant improvements of the proposed model over baseline models in terms of MAE, MSE, and Explained Variance metrics considering one-hour sequences. Utilizing the proposed solution can help enhance the automation of aquaponics environments.</p><h2>Other Information</h2><p dir="ltr">Published in: The Journal of Supercomputing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11227-023-05389-8" target="_blank">https://dx.doi.org/10.1007/s11227-023-05389-8</a></p>2023-06-06T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11227-023-05389-8https://figshare.com/articles/journal_contribution/Temporal_fusion_transformer-based_prediction_in_aquaponics/24997703CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249977032023-06-06T03:00:00Z |
| spellingShingle | Temporal fusion transformer-based prediction in aquaponics Ahmet Metin (12832543) Agricultural, veterinary and food sciences Horticultural production Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Time series forecasting Aquaponics Transformers Anomaly detection Deep learning |
| status_str | publishedVersion |
| title | Temporal fusion transformer-based prediction in aquaponics |
| title_full | Temporal fusion transformer-based prediction in aquaponics |
| title_fullStr | Temporal fusion transformer-based prediction in aquaponics |
| title_full_unstemmed | Temporal fusion transformer-based prediction in aquaponics |
| title_short | Temporal fusion transformer-based prediction in aquaponics |
| title_sort | Temporal fusion transformer-based prediction in aquaponics |
| topic | Agricultural, veterinary and food sciences Horticultural production Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Time series forecasting Aquaponics Transformers Anomaly detection Deep learning |