Data-driven model predictive control for precision irrigation management
The future of agriculture faces a threat from a changing climate and a rapidly growing population. This has put enormous pressure on water and land resources as more food is expected from less inputs. Advancement in smart agriculture through the use of the Internet of Things and improvement in compu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375522000399 |
| _version_ | 1818535578807828480 |
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| author | Erion Bwambale Felix K. Abagale Geophrey K. Anornu |
| author_facet | Erion Bwambale Felix K. Abagale Geophrey K. Anornu |
| author_sort | Erion Bwambale |
| collection | DOAJ |
| description | The future of agriculture faces a threat from a changing climate and a rapidly growing population. This has put enormous pressure on water and land resources as more food is expected from less inputs. Advancement in smart agriculture through the use of the Internet of Things and improvement in computational power has enabled extensive data collection from agricultural ecosystems. This review introduces model predictive control and describes its application in precision irrigation. An overview of the application of data-driven modelling and model predictive control for precision irrigation management is presented. Model predictive control has been applied in irrigation canal control, irrigation scheduling, stem water potential regulation, soil moisture regulation and prediction of plant disturbances. Finally, the benefits, challenges, and future perspectives of data-driven model predictive control in the context of irrigation scheduling are presented. This review provides useful information to researchers and agriculturalists to appreciate and use data collected in real-time to learn the dynamics of agricultural systems. |
| first_indexed | 2024-12-11T18:26:46Z |
| format | Article |
| id | doaj.art-5c1bc8dae29347bc9dd2d400f85ba0a3 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-12-11T18:26:46Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-5c1bc8dae29347bc9dd2d400f85ba0a32022-12-22T00:55:02ZengElsevierSmart Agricultural Technology2772-37552023-02-013100074Data-driven model predictive control for precision irrigation managementErion Bwambale0Felix K. Abagale1Geophrey K. Anornu2West African Center for Water, Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, P. O. Box TL 1882, Tamale, Ghana; Department of Agricultural Engineering, University for Development Studies, P. O. Box TL 1882, Tamale, Ghana; Department of Agricultural and Biosystems Engineering, Makerere University, P. O. Box 7062, Kampala, Uganda; Correspondence author.West African Center for Water, Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, P. O. Box TL 1882, Tamale, Ghana; Department of Agricultural Engineering, University for Development Studies, P. O. Box TL 1882, Tamale, GhanaDepartment of Civil Engineering, Regional Water and Environmental Sanitation Center Kumasi (RWESCK), Kwame Nkrumah University of Sciences and Technology, Kumasi, GhanaThe future of agriculture faces a threat from a changing climate and a rapidly growing population. This has put enormous pressure on water and land resources as more food is expected from less inputs. Advancement in smart agriculture through the use of the Internet of Things and improvement in computational power has enabled extensive data collection from agricultural ecosystems. This review introduces model predictive control and describes its application in precision irrigation. An overview of the application of data-driven modelling and model predictive control for precision irrigation management is presented. Model predictive control has been applied in irrigation canal control, irrigation scheduling, stem water potential regulation, soil moisture regulation and prediction of plant disturbances. Finally, the benefits, challenges, and future perspectives of data-driven model predictive control in the context of irrigation scheduling are presented. This review provides useful information to researchers and agriculturalists to appreciate and use data collected in real-time to learn the dynamics of agricultural systems.http://www.sciencedirect.com/science/article/pii/S2772375522000399Data-driven modelsModel predictive controlPrecision irrigationSystem identification |
| spellingShingle | Erion Bwambale Felix K. Abagale Geophrey K. Anornu Data-driven model predictive control for precision irrigation management Smart Agricultural Technology Data-driven models Model predictive control Precision irrigation System identification |
| title | Data-driven model predictive control for precision irrigation management |
| title_full | Data-driven model predictive control for precision irrigation management |
| title_fullStr | Data-driven model predictive control for precision irrigation management |
| title_full_unstemmed | Data-driven model predictive control for precision irrigation management |
| title_short | Data-driven model predictive control for precision irrigation management |
| title_sort | data driven model predictive control for precision irrigation management |
| topic | Data-driven models Model predictive control Precision irrigation System identification |
| url | http://www.sciencedirect.com/science/article/pii/S2772375522000399 |
| work_keys_str_mv | AT erionbwambale datadrivenmodelpredictivecontrolforprecisionirrigationmanagement AT felixkabagale datadrivenmodelpredictivecontrolforprecisionirrigationmanagement AT geophreykanornu datadrivenmodelpredictivecontrolforprecisionirrigationmanagement |