Bwambale, ErionAbagale, Felix K.Anornu, Geophrey K.2023-06-162023-06-162023Bwambale, E., Abagale, F. K., & Anornu, G. K. (2023). Data-driven model predictive control for precision irrigation management. Smart Agricultural Technology, 3, 100074.https://doi.org/10.1016/j.atech.2022.1000742772-3755https://nru.uncst.go.ug/handle/123456789/8941The 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.enSystem identificationData-driven modelsPrecision irrigationModel predictive controlArticle