Leveraging Machine Learning for Accurate Soil Moisture Prediction to Enhance Agricultural Productivity in India

Abstract

Accurate soil moisture prediction is vital for enhancing agricultural productivity, especially in a country like India, where diverse soil types and climatic conditions significantly affect crop yields. Traditional methods, such as physical sensors and hydrological models, often fail to provide precise results due to environmental variability and limited spatial coverage. In response, this paper proposes a machine learning-based approach to soil moisture prediction, incorporating historical meteorological data, soil characteristics, and satellite-based remote sensing data. We assess various machine learning models, including Random Forest (RF), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, to determine the most effective algorithm for diverse terrains and conditions. The study finds that LSTM models outperform traditional methods, delivering more accurate and robust soil moisture predictions. These advancements offer significant potential for improving decision-making in precision agriculture and managing water resources, helping to address the challenges of declining soil moisture and food production in the face of climate change.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.99-104