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Plant Science Today (PST)

Research Articles

Vol. 12 No. 2 (2025)

Predicting small millets productivity based on machine learning models: A comprehensive study

DOI
https://doi.org/10.14719/pst.7933
Submitted
26 February 2025
Published
24-04-2025 — Updated on 30-04-2025
Versions

Abstract

People in rural and urban areas prefer small millet-based food products and they are more popular in markets. Before the green revolution, little millets were widely cultivated for food and fodder, but dietary preferences changed and they were no longer widely planted. Machine learning techniques have been applied in agriculture recently to analyze and predict crop yields. A major concern for farmers during the growing season is estimating their expected yield. In this study, data on area and production for small millets in Tamil Nadu are collected over 50 years. The machine learning models are used to predict productivity with the available small millets dataset. Four different machine learning models are used to estimate small millets productivity. With an accuracy of 95.46 %, a mean absolute error (MAE) of 0.0709, a root mean square error (RMSE) of 0.014 and an R-square value of 0.94, the Random Forest regressor performed better than the other models. The current research helps the farmers in mitigating potential losses, as their financial stability and productivity output are closely related. Additionally, the study provides valuable insights for better planning and implementation. Furthermore, the Random Forest regressor offers insightful information to help farmers maximize their farming techniques and make well-informed judgments.

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