Predictive Modelling of Soil Drying Using Machine Learning for Sustainable Agriculture

Abstract

Soil drying significantly influences agricultural productivity, making accurate prediction essential for efficient irrigation planning and water management. This research investigates how machine learning (ML) models can analyse and forecast soil moisture loss using diverse datasets, including weather conditions, soil properties, and remote sensing inputs. By leveraging ML, the study aims to offer practical solutions for optimising resource use in agriculture while addressing the challenges posed by climate change. The findings highlight ML's ability to support sustainable farming practices through improved soil moisture predictions.