Abstract
Modern greenhouse operations collect diverse environmental and substrate data at high temporal resolution, yet most irrigation decisions still depend on a single measured variable. Peer-reviewed literature documents individual sensor technologies and control algorithms in detail but offers no systematic framework for evaluating how multiple data streams are, or could be, combined within a single irrigation decision. To propose a structured classification of data-driven irrigation approaches based on the number of integrated data streams and the temporal horizon of the resulting control decision. An analytical review of published sensor-based, radiation-based, and model-based irrigation strategies was combined with retrospective operational observations from commercial hydroponic strawberry cultivation on coir substrate. A three-tier integration model is introduced, progressing from single-parameter threshold control (Tier 1) through dual-parameter coordination (Tier 2) to multi-parameter feed-forward architectures that couple environmental anticipation with root-zone feedback (Tier 3). Most peer-reviewed implementations operate at Tier 1 or Tier 2; Tier 3 configurations appear in proprietary commercial platforms but lack open documentation. The proposed classification identifies a persistent gap between available sensor infrastructure and the decision logic that utilizes it, and positions multi-parameter feed-forward integration as a priority for further applied research. The analytical observations presented in this article were informed by the author’s operational management of commercial hydroponic strawberry production in Kyiv Oblast, Ukraine, involving up to 10 hectares of greenhouse area and approximately 640,000 plants cultivated under closed-loop fertigation systems. The operational experience referenced throughout this work is used as contextual support for interpretation and discussion and is not presented as controlled experimental evidence.
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