The comparison of the hidden Markov model with machine learning techniques in agricultural prediction

Muraleedharan  Vyshnavi; Madaswamy Muthukumar

doi:https://doi.org/10.59139/stattrans-2026-007

The comparison of the hidden Markov model with machine learning techniques in agricultural prediction

Muraleedharan Vyshnavi Department of Statistics, PSG College of Arts & Science, Coimbatore-641014, Tamil Nadu, India ORCID:https://orcid.org/0009-0000-1098-8161 , Madaswamy Muthukumar Department of Statistics, PSG College of Arts & Science, Coimbatore-641014, Tamil Nadu, India Statistics in Transition new series, vol. 27, 2026, 1, pages: 119-135 Published online: 11 March 2026 https://doi.org/10.59139/stattrans-2026-007 Citation: Muraleedharan V., Madaswamy M., 2026. The comparison of the hidden Markov model with machine learning techniques in agricultural prediction. Statistics in Transition new series, 27(1), pp. 119- 135 https://doi.org/10.59139/stattrans-2026-007

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ABSTRACT

This study compares hidden Markov models (HMMs) with various machine learning approaches to assess their effectiveness in forecasting agricultural data based on Python. Accurate forecasts are essential to promote sustainability and increase agricultural productivity. Through the use of an extensive dataset of agricultural parameters, specifically the cultivated area of oilseeds, the study explores historical trends and correlations. Model performance is evaluated using the Mean Absolute Error (MAE) along with the R-squared, and residual analysis is used to analyse how well the models represent the underlying trends. The findings demonstrate that HMMs are able to predict agricultural trends with higher accuracy than their other counterparts, thereby providing useful information for improved agricultural planning and decision-making. Future studies should concentrate on improving forecast accuracy and resolving any issues associated with agricultural data prediction.

KEYWORDS

machine learning, mean absolute error, R-squared, hidden Markov model, residual analysis

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