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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

A study on trade performance of Indian black tea: An artificial neural network and Markov chain approach

DOI
https://doi.org/10.14719/pst.10214
Submitted
24 June 2025
Published
10-09-2025 — Updated on 29-09-2025
Versions

Abstract

This study explores the use of Artificial Neural Networks (ANN) and Markov Chain models to forecast India’s black tea exports to major international markets. While the Markov Chain approach provided a simplified, state-based view of export behaviour, the ANN models were designed to capture continuous patterns and subtle market dynamics. Forecasts were generated for the next five years across ten key importing countries. Model performance was assessed using standard evaluation metrics- Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R-squared (R²). The results clearly favoured the ANN model, which consistently delivered more accurate and reliable forecasts. Notably, the United Arab Emirates (UAE) emerged as the top growth market, with predicted export values from India reaching ₹1.23 lakhs by the fifth year. Russia and the USA also showed strong forecasted demand, with expected values of ₹64767 lakhs and ₹45238 lakhs, respectively. These insights offer practical value for exporters, traders and policymakers by highlighting priority markets and supporting more informed decision-making. Overall, this research reinforces the importance of intelligent forecasting systems in managing the complexity of international tea trade.

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