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Related Experiment Video

Updated: Jan 10, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture

Md Babul Islam^1,2, Antonio Guerrieri², Raffaele Gravina¹

¹DIMES, University of Calabria, Via P. Bucci, 87036 Rende, CS, Italy.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

Accurate soil moisture forecasting in smart agriculture is crucial for efficient water management. This review provides a structured taxonomy and analyzes AI models, highlighting future directions like TinyML and explainable AI for better farming.

Keywords:

deep learning fine-tuning LLM hybrid model machine learning review smart agriculture soil moisture forecast

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Area of Science:

Agricultural Science
Computer Science
Environmental Science

Background:

Smart Agriculture (SA) integrates IoT, AI, and sensing for enhanced farming productivity and sustainability.
Continuous Soil Moisture (SM) monitoring is vital for crop growth, water management, and informed irrigation decisions.
Existing reviews lack structured frameworks and overlook recent AI advancements like Federated Learning (FL) and Large Language Models (LLMs).

Purpose of the Study:

To propose a novel taxonomy for Soil Moisture (SM) forecasting.
To comprehensively review existing SM forecasting approaches, including traditional, deep learning, and hybrid models.
To identify emerging research directions in AI-driven SM forecasting for Smart Agriculture.

Main Methods:

Systematic literature review using PRISMA methodology, analyzing 68 peer-reviewed studies (2017-2025).
Categorization of studies based on sensor types, input features, AI techniques, data durations, and evaluation metrics.
Development of a novel taxonomy to structure the field of SM forecasting.

Main Results:

Analysis of diverse AI techniques applied to SM forecasting, from traditional machine learning to advanced deep learning models.
Identification of key factors influencing SM forecasting accuracy, including sensor data and AI model choices.
Overview of current trends and gaps in SM forecasting research.

Conclusions:

The proposed taxonomy offers a structured framework for understanding and advancing SM forecasting research.
Future research should focus on TinyML for edge deployment, explainable AI (XAI) for transparency, and privacy-aware FL.
Accurate and trustworthy SM forecasting systems are essential for the continued development of Smart Agriculture.