A Neutrosophic Machine Learning-Based Intelligent Ensemble Model for Sustainable Tea Yield Prediction Under Climatic Variability

Abstract: For efficient agricultural planning, resource management, and enhancing farmer livelihoods in significant tea-producing regions, tea production prediction is essential. However, climate variability—including temperature, rainfall, humidity, and sunlight duration—has a significant impact on tea output, making precise forecasting difficult. Using meteorological data from 2015 to 2025, this study suggests a hybrid machine learning approach for predicting tea production. Initially, four models are created as separate predictors: Random Forest, XG Boost, Light GBM, and Cat Boost. Three ensemble models are shown to increase prediction accuracy: a Neutrosophic Ensemble Model, a Fuzzy Logic Weighted Ensemble, and an optimized weighted ensemble utilizing Sequential Least Squares Programming (SLSQP). According to experimental data, the optimized ensemble outperforms individual and alternative ensemble models, achieving the best performance with an R2 value of 0.86, an RMSE value of 130.89, and an MAE value of 103.96. The suggested methodology improves the accuracy of the tea yield forecast while managing climate variability.

Maitraya Dey, Pushpita Roy, Shubhendu Banerjee, Amrut Ranjan Jena, Rakesh Naskar, Suparna Dasgupta, Soumyabrata Saha, Sudarshan Nath and Bikash Mondal. “A Neutrosophic Machine Learning-Based Intelligent Ensemble Model for Sustainable Tea Yield Prediction Under Climatic Variability”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170427

@article{Dey2026,
title = {A Neutrosophic Machine Learning-Based Intelligent Ensemble Model for Sustainable Tea Yield Prediction Under Climatic Variability},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170427},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170427},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Maitraya Dey and Pushpita Roy and Shubhendu Banerjee and Amrut Ranjan Jena and Rakesh Naskar and Suparna Dasgupta and Soumyabrata Saha and Sudarshan Nath and Bikash Mondal}
}