Transfer Learning-based Digital Models: Algorithmic Provisioning of Smart Irrigation Systems Under Data Scarcity
Year: 2026
Type: article
Abstract: This paper addresses the critical challenge of deploying modern water-saving smart irrigation systems in Uzbekistan, where multi-decadal open digital repositories for soil moisture, salinity, and microclimate dynamics are virtually non-existent. To overcome the constraints of deep learning models under small data regimes, a Transfer Learning (TL) framework is proposed. This study outlines the theoretical foundations, mathematical formalization, and algorithmic integration of knowledge transfer from data-rich global source domains to data-scarce local target domains. The proposed approach optimizes the software-driven decision-making algorithms of smart irrigation systems, ensuring high-accuracy predictions without requiring localized historical big data.
Authors: Chuliev Maxmatmurod, Yuldosheva Shirin
Institution: Karshi State University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7220064786
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