Maxmatmurod et al. (2026) Transfer Learning-Based Digital Models: Algorithmic Provisioning of Smart Irrigation Systems Under Data Scarcity

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Modern American Journal of Biological and Environmental Sciences
• Year: 2026
• Date: 2026-08-31
• Authors: Chuliev Maxmatmurod, Yuldosheva Shirin
• DOI: None

Research Groups

Not specified in the provided text.

Short Summary

This paper proposes a Transfer Learning (TL) framework to enable high-accuracy smart irrigation systems in data-scarce regions like Uzbekistan, overcoming the limitations of deep learning models without requiring localized historical big data.

Objective

• To develop and integrate a Transfer Learning (TL) framework to optimize software-driven decision-making algorithms for smart irrigation systems, ensuring high-accuracy predictions in data-scarce environments like Uzbekistan.

Study Configuration

• Spatial Scale: Global source domains (data-rich) to local target domains (data-scarce, e.g., Uzbekistan).
• Temporal Scale: Real-time decision making for irrigation, addressing the challenge posed by the absence of multi-decadal historical data.

Methodology and Data

• Models used: Deep learning models within a Transfer Learning (TL) framework.
• Data sources: Data-rich global source domains (unspecified types) and data-scarce local target domains.

Main Results

• The proposed Transfer Learning framework successfully optimizes software-driven decision-making algorithms for smart irrigation systems.
• It ensures high-accuracy predictions for irrigation management even without requiring localized historical big data.

Contributions

• Provides a novel Transfer Learning framework to enable the deployment of modern water-saving smart irrigation systems in regions lacking multi-decadal historical data.
• Overcomes the constraints of deep learning models in small data regimes, offering a practical solution for data-scarce environments.
• Outlines the theoretical foundations, mathematical formalization, and algorithmic integration for knowledge transfer from global to local domains.

Funding

Not specified in the provided text.

Citation

@article{Maxmatmurod2026Transfer,
  author = {Maxmatmurod, Chuliev and Shirin, Yuldosheva},
  title = {Transfer Learning-Based Digital Models: Algorithmic Provisioning of Smart Irrigation Systems Under Data Scarcity},
  journal = {Modern American Journal of Biological and Environmental Sciences},
  year = {2026},
  url = {https://openalex.org/W7220064786}
}