SMART IRRIGATION SYSTEMS IN DATA-SCARCE ARID REGIONS: A SYSTEMATIC REVIEW OF HARDWARE TOPOLOGIES, ALGORITHMIC TAXONOMIES, AND DECENTRALIZED BLUEPRINTS FOR UZBEKISTAN

Abstract

Global agricultural production faces severe hydro-climatic volatility, demanding a transition from conventional irrigation to automated Smart Irrigation Systems (SIS) to optimize Water-Use Efficiency (WUE). However, high-precision predictive irrigation models remain heavily dependent on data-intensive infrastructures, creating an operational bottleneck in developing, data-scarce arid zones. This systematic review establishes a multi-dimensional taxonomy of breakthrough smart irrigation studies up to 2026. It critically evaluates embedded hardware topologies, algorithmic frameworks, and cloud-edge software interfaces. A systematic screening of literature from Scopus, Web of Science, and ScienceDirect was conducted following PRISMA guidelines. Methodologies were classified into four discrete generations: deterministic threshold automata, bio-agrometeorological crop models (e.g., AquaCrop), data-driven stochastic AIoT networks (LSTM/XGBoost), and cyber-physical digital twins integrated with numerical solvers (HYDRUS). The critical analysis reveals a profound "Data-Scarcity vs. Algorithmic Overhead" paradox: state-of-the-art predictive architectures achieve 35%–50% water savings but require dense field-level sensor networks and continuous cloud computing infrastructure. To bridge this division, this paper presents a decentralized conceptual blueprint tailored for arid, data-scarce regions like Uzbekistan. By proposing the integration of macro-level, open-access satellite telemetry (ESA Copernicus) via Transfer Learning and Unsupervised Domain Adaptation onto low-power edge units (ESP32/LoRaWAN), this study establishes a scalable pathway for sustainable water management in data-sparse landscapes.