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Irrigation decision-making has evolved from solely depending on farmers’ decisions taken based on the visual analysis of field conditions to making decisions based on crop water need predictions generated using machine learning (ML) techniques. This paper reviews ML related articles to discuss how ML has been used to enhance irrigation decision making. We reviewed 16 studies that used ML approaches for irrigation scheduling prediction and decision-making focusing on the input features, algorithms used and their applicability in real world conditions. ML performances in terms of accuracy, water conservation compared to fixed or threshold-based methods are discussed along with modeling performances. Informed by the 16 research studies, we assessed constraints to the adoption of ML in irrigation decision making at field scale, which include limited data availability coupled with data sharing constraints, and a lack of uncertainty quantification as well as the need for physics informed ML based irrigation scheduling models. To address these limitations, we discussed approaches in future research such as integrating process-based models with ML, incorporating expert knowledge into the modeling procedure, and making data and tools Findable, Accessible, Interoperable, and Reusable (FAIR). These approaches will improve ML modeling outcomes and boost the availability of farm-related data and tools for FAIRer data-driven applications of irrigation modeling. |
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