Cup-lump Rubber Quality Estimation from Historical Weather Data Using Machine Learning Regressors
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Abstract
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.
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References
Xayyaphet B, Kohkhoo R, Pakoktom T. The relationship between meteorological factors and yield of para rubber tree. In: Proc. 12th National Academic Conference, Kasetsart University, Kamphaeng Saen Campus, Thailand; 2020.
Thamchoti P, Kongrit V. Adaptation of rubber production system to climate change in upper southern Thailand. J Agric Sci. 2021;15(3):45–58.
Sangchanda N, et al. Effect of climate on growth and physiological characteristics of rubber tree in northeast Thailand. Thai J Plant Physiol. 2019;10(2):120–135.
Thaiburi N, Sinnarong N, Autchariyapanitkul K, Nunthasen K. Impacts of climate change on rubber production in lower southern Thailand. Narathiwat Rajanagarindra University, College of Agriculture and Technology; year unknown.
Evaluation of the impact of climatic factors on latex yield of Hevea brasiliensis. Int J Res Stud Agric Sci. 2017;3(5). doi:10.20431/2454-6224.0305004.
Zhang F, Lin Y, Song Y, Zhang J. Prediction of dry rubber content in Hevea latex based on SPSS multiple regression model. IOP Conf Ser Mater Sci Eng. 2019;563:022030.
Puttipipatkajorn A, Puttipipatkajorn A. Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques. Int J Agric Biol Eng. 2021;14(3):207–213.
Wadugoda HWNO, Perera TANT, Abhiram G, Jayasinghe GY. Machine learning-based yield prediction for rubber (Hevea brasiliensis) cultivation. Uva Wellassa University and University of Ruhuna, Sri Lanka; 2025.
Open-Meteo API. Available from: https://open-meteo.com
Thaler P, Jeanneau C, Sotta B, Maillard M, Michaud A, Ramel J, Prioul JP, Montpied T. Effects of drought and tapping for latex production on water relations of Hevea brasiliensis trees. Tree Physiol. 2008;28(12):1791–1799. doi:10.1093/treephys/28.12.1791.
Thaiburi N, Sinnarong N, Autchariyapanitkul K, Nunthasen K. Impacts of climate change on rubber production in lower southern Thailand. Narathiwat Rajanagarindra University, College of Agriculture and Technology; 2021.
Niu P, Ma Z, Zhou T, Chen W, Shen L, Jin R, Sun L. Utilizing strategic pre-training to reduce overfitting: Baguan – a pretrained weather forecasting model. arXiv Preprint. 2025;arXiv:2505.13873.
Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proc. 22nd ACM SIGKDD Int Conf Knowl Discov Data Min. 2016. p. 785-794.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825–2830.
