AI-Powered Soil Temperature Modeling for Sustainable Agriculture in Arid Regions: A Case Study of Bustan, Uzbekistan

Abstract

Soil temperature is a key determinant of soil health and agricultural productivity, especially in arid regions vulnerable to climate change. This study investigates the use of advanced machine learning models to predict soil temperature variations in Bustan, Uzbekistan, a region facing significant climatic stress. Using 16 years of meteorological data, including atmospheric temperature, humidity, and wind speed, eight machine learning models were evaluated for their ability to predict surface and subsurface (10 cm depth) soil temperatures. Among the models tested, the bi-directional long short-term memory (Bi-LSTM) algorithm demonstrated superior predictive accuracy with R² values exceeding 0.94 for subsurface temperatures. The two-step modeling approach utilized Bi-LSTM outputs from surface temperature predictions to inform subsurface estimates, reflecting a novel methodology for climate-sensitive agriculture. The results provide a practical framework for improving irrigation planning, crop yield forecasting, and sustainable land management in data-scarce arid environments. By demonstrating high accuracy and real-world applicability, this AI-driven model offers a scalable solution for enhancing agricultural resilience in Uzbekistan and similar contexts.

Received: 13 June 2025 | Revised: 27 August 2025 | Accepted: 21 November 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data are available on request from the corresponding author upon reasonable request.

Author Contribution Statement

Lakindu Mampitiya: Methodology, Software, Formal analysis, Investigation, Writing — original draft, Visualization. Namal Rathnayake: Validation, Investigation, Writing — original draft, Writing — review & editing. Kenjabek Rozumbetov: Resources, Data curation. Valery Erkudov: Resources, Data curation. Mirzohid Koriyev: Resources, Data curation. Komali Kantamaneni: Writing — review & editing. Upaka Rathnayake: Conceptualization, Validation, Writing — review & editing, Supervision, Project administration.