TRANSFER LEARNING-BASED DIGITAL MODELS: ALGORITHMIC PROVISIONING OF SMART IRRIGATION SYSTEMS UNDER DATA SCARCITY

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.