Machine Learning Based Peach Leaf Temperature Prediction Model for Measuring Water Stress

When utilizing the Crop Water Stress Index (CWSI), the most critical factor is accurately measuring canopy temperature, which is typically done using infrared sensors and imaging cameras. In this study, however, we aimed to develop a machine learning model capable of predicting leaf temperature based on environmental data, without relying on sensors, for calculating CWSI. The data underwent preprocessing to remove outliers and missing values. The number of training data points for each factor was 307,924. After data preprocessing, a Pearson correlation analysis (bivariate correlation coefficient) was conducted to select the training data for model operation. The relationship between leaf temperature and air temperature showed a strong positive correlation of 0.928 (p < 0.01). Solar radiation and relative humidity were also found to have high correlations. However, wind speed and soil moisture tension showed very low correlations with leaf temperature and were excluded from the model operation. The Decision Tree, Random Forest, and Gradient Boosting models were selected, and each model was evaluated using RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Squared Error), and R2 (coefficient of determination). The evaluation results showed that the Gradient Boosting model had a high R2 (0.97) and low RMSE (0.88) and MAE (0.54), making it the most suitable model for predicting leaf temperature. Through the leaf temperature prediction model developed in this study, labor and costs associated with sensors can be reduced, and by applying it to real agricultural settings, it can improve crop quality and enhance the sustainability of agriculture.