Predictive Modeling of Soil Moisture Variability Using Machine Learning: Insights from Dry and Wet Soil Cultures

Soil moisture prediction is crucial for optimizing irrigation practices and advancing precision agriculture. This study presents a predictive modeling approach leveraging machine learning techniques to analyze soil moisture variability in dry and wet soil cultures. Utilizing data collected through IoT-enabled sensors, this research applies regression-based algorithms to forecast moisture trends. A robust data preprocessing framework, including interpolation and augmentation, was implemented to address missing data challenges. Experimental results demonstrate high prediction accuracy, with minute-wise temporal granularity outperforming other datasets. The findings highlight the potential for integrating machine learning models into sustainable irrigation practices.