The effect of climatic factors on the cotton productivity using machine learning approaches

Abstract

Agriculture and the farming are two of the most important sectors of the economy. An accurate
and timely assessment of cotton field productivity is useful for management decisions about
cotton supply and sales. Cotton production concentration is influenced by a variety of factors.
The impact of climatic conditions (rainfall, temperature, wind, etc.) on cotton productivity is
studied in order to determine the quantitative relationship between these parameters and
productivity. Several machine learning techniques have been researched and used to estimate
crop yield. Errors like RMSE, MSE, MAE, and R2 were employed as indicators, and the
polynomial regression model was chosen as the best among them.