Optimization Accuracy of Soil Moisture for Precision Agriculture using SVM machine learning

Abstract

Soil moisture plays a critical role in crop growth, irrigation planning, and overall agricultural productivity, making its accurate estimation essential for precision agriculture. Traditional soil moisture measurement methods such as gravimetric sampling, tensiometer readings, and time-domain reflectometry provide accurate point measurements but are limited in scalability and real-time monitoring. To address these limitations, machine learning techniques have emerged as effective tools for predicting soil moisture using environmental, climatic, and remote sensing datasets. Among these, Support Vector Machine (SVM) has gained significant attention due to its robust generalization capability, ability to handle nonlinear relationships, and effectiveness with limited training data. However, prediction accuracy using SVM largely depends on proper feature selection and hyperparameter optimization. This research focuses on optimizing soil moisture prediction accuracy using an enhanced SVM model by tuning key parameters such as kernel function, penalty constant (C), and gamma (γ) using optimization approaches. The proposed method integrates multisource data including soil properties, temperature, humidity, rainfall, vegetation indices, and sensor-based field measurements. Performance evaluation is conducted using metrics such as RMSE, MAPE, MAE, and R² to assess improvements over conventional SVM and baseline regression models. The optimized SVM model demonstrates improved prediction accuracy, making it suitable for smart irrigation systems, drought assessment, and real-time agricultural decision support.