Assessing Predictive Models for Tea Yield: A Statistical and Machine Learning Approach in Assam's Biswanath Chariali District
Pal Deka, Nabajit Tanti, Prasanta Neog
Journal of Experimental Agriculture International · pp. 526–534 · Published 22 Jun 2024
10.9734/jeai/2024/v46i72605Abstract
Climatic factors significantly impact Assam tea production. The tropical climate of Assam, characterized by high precipitation and temperatures up to 36°C during the monsoon, creates ideal conditions for tea cultivation, contributing to the region's unique malty flavor. Here, in this study an attempt has been made to bring a comparison among statistical and machine learning models in prediction of tea production and evaluate an optimal model among them. A time span of last 23 years data were collected from Biswanath College of Agriculture under Assam Agriculture University situated at Biswanath Chariali district. The study has found that mean absolute percentage error of random forest regression model is 6.49 percent followed by decision tree (7.3 percent) and linear regression model (7.5 percent). From the evaluation metrics, random forest algorithm fits well in comparison to decision tree and linear regression. This study could be generalized to comparison among more predictive machine learning models.
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