Cup-lump Rubber Quality Estimation from Historical Weather Data Using Machine Learning Regressors - Charisa Areewattana; Nattapon Pramotkul; Tanakorn Chanjatunat; Somrudee Deepaisarn (Science & Technology Asia, 2025)
Rubber is an essential raw material in many industries, with rubber trees requiring specific weather conditions to thrive. Thailand is a significant hub of rubber producers, particularly for cup-lump rubber, which is used in many products, such as automobile tires. The quality of this rubber is measured by its percentage of dry rubber content (DRC), indicating the usable portion after processing. Traditional DRC percentage methods often rely on human judgment, introducing potential bias. This study proposes a machine learning approach to predict the DRC percentage using historical weather data. We expanded upon earlier research that applied statistical models by incorporating a wider range of weather-based features and modern regression algorithms. Weather data, including temperature, precipitation, wind speed, and sunlight duration, was obtained from the Open-Meteo API and averaged over time windows, ranging between 3 to 50 days. These features are derived from 2,034 in-house DRC percentage records provided by Southland Rubber Company Ltd. The final dataset comprises 124 features. Several machine-learning regressors were evaluated using Scikit-learn, including XGBoost, LightGBM, Random Forest, and others. The XGBoost model achieved the highest performance, with an R2 score of 0.7450 and a root mean square error of 3.23%. These results indicate that machine learning can effectively predict rubber quality based on variable weather trends. This research offers manufacturers a more objective tool for production planning, resource allocation, and quality control.