Review Paper on Soil Moisture for Precision Agriculture using Machine Learning Technique

Abstract

Soil moisture plays a pivotal role in crop growth, nutrient uptake, irrigation planning, and climate variability assessment. Accurate and timely estimation of soil moisture is essential for precision agriculture, enabling resource‐efficient and sustainable farming practices. Traditional measurement methods, such as gravimetric sampling and sensor-based techniques, offer high accuracy but are often limited by high cost, labor-intensive deployment, and insufficient spatial coverage. Recent advancements in machine learning have introduced alternative data-driven approaches capable of predicting soil moisture using multisource inputs, including weather parameters, remote sensing imagery, soil characteristics, and vegetation indices. This review presents a comprehensive analysis of machine learning techniques employed for soil moisture estimation and prediction, including Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), Gradient Boosting models, and Deep Learning frameworks such as LSTM and CNN. The study further examines model performance metrics, dataset challenges, feature selection strategies, sensor fusion techniques, and approaches to overcome data sparsity. Additionally, the review highlights the integration of IoT-based monitoring systems and cloud-enabled platforms to support real-time soil moisture analysis for precision farming. The findings indicate that hybrid deep learning models and remote‐sensing–enabled prediction approaches achieve superior accuracy compared to conventional models. Future research should focus on scalable frameworks, transfer learning, and adaptive models to improve prediction robustness across diverse agro-climatic regions.