Leveraging Microclimate Data, Proximal Canopy Sensing, and Machine Learning for Precise Soil Water Potential Prediction
Introduction Accurately predicting soil water potential is crucial for optimizing irrigation practices and minimizing water waste in agriculture. This project delves into the critical relationship between high resolution, in-field microclimate measurements, canopy surface temperature , time of day, and soil water content and potential. Building upon our previous work in biophysical modeling , we employ advanced machine learning techniques to investigate the impact of measurement timing on prediction accuracy. Enhanced Prediction with Machine Learning This project utilizes a range of machine learning algorithms, including: Linear Regression: Establishes a linear relationship between features and target variables. Decision Tree Regressor: Makes decisions based on a series of if-else questions. Random Forest Regressor: An ensemble method combining multiple decision trees for improved accuracy and reduced overfitting. Gradient Boosting Regressor: Iteratively b...