1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Guntur-522 502, Andhra Pradesh, India.
2Department of Agriculture, Koneru Lakshmaiah Education Foundation, Greenfields, Guntur-522 502, Andhra Pradesh, India.
*Corresponding Author: Vamsi Krishna Pallapati, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Guntur-522 502, Andhra Pradesh, India. Email: pallapativamsi8@gmail.com
Markets of cocoon silk suffer the price instability and farmers have high levels of economic uncertainty. The traditional forecasting methods do not reflect the seasonality, the environment and market driven interactions that affect price changes. The unpredictability of demand due to fluctuation of demand, climatic conditions and variation in production cycles makes the task of income planning extremely burdensome since the production of cocoons sustains millions of small farmers. The paper will look into the possibility of a more data-driven machine-learning model to capture these complicated market dynamics.
This study presents a leakage-free machine learning framework for early forecasting of cocoon silk prices using a Random Forest regression model. To maintain realistic forecasting conditions, contemporaneous price variables were excluded and predictions were generated solely through lagged historical modal prices together with pre-available environmental and management indicators. The dataset was properly preprocessed and temporally ordered and model evaluation was done using a chronological train-test split. Performance was assessed using error-based metrics and explained variance, showing the non-stationary nature of agricultural price series.
The revised model achieved a mean absolute error (MAE) of 2965.14, a root mean square error (RMSE) of 5985.60 and an explained variance (R2) of 0.0903. While the explained variance is modest, this behavior is appropriate for early forecasting in volatile agricultural markets when data leakage is eliminated. Feature contribution analysis reveals that short-term historical prices have the strongest influence, with environmental and management aspects providing secondary but meaningful effects. A simulation-based assessment indicates a potential 12–18% revenue improvement, resulting from informed selling decisions enabled by short-horizon price forecasts. This gain shows lower exposure to post-harvest price troughs and improved market selection rather than precise peak-price prediction.
Agricultural forecasting, Cocoon silk price prediction, Machine learning, Random forest regressor, Sericulture
