A Machine Learning Approach for Forecasting Vegetable Prices in Indian APMC Markets Using XGBoost | IJCT Volume 12 – Issue 6 | IJCT-V12I6P35
International Journal of Computer Techniques
ISSN 2394-2231
Author
Mr. Umesh Nanavare, Mr. Shantanu Patil, Mr. Om Patil,
Mr. Sunny Khokle, Mr. Harpalsing Rajput
Abstract
Agricultural price volatility of perishable commodi- ties is a persistent driver of rural distress in India. This work develops an extensive machine-learning pipeline for short-term forecasting of modal prices for vegetables (cabbage, cauliflower, and green chilli) in Pune district APMC markets (2023–2025). We design a robust feature engineering suite (lagged, rolling, Fourier- based seasonal encodings), formulate the XGBoost learning ob- jective with regularization rigorously, and deploy an operational inference API. Extensive experiments compare XGBoost against ARIMA and LSTM baselines across multiple performance met- rics, showing 25–40% improvement in MAE and RMSE. The paper discusses interpretability, deployment concerns, and pol- icy implications for integrating forecasts into market decision systems. Code artifacts and model metadata are structured for reproducibility and productionization.
Keywords
Agricultural forecasting, APMC markets, XG- Boost, time-series forecasting, feature engineering, model deploy- ment.
Conclusion
This paper presents a comprehensive XGBoost-based fore- casting pipeline for vegetable prices in Pune APMC markets. The model outperforms ARIMA and LSTM, achieving MAE of 81–251. The system is deployed with monitoring and retraining, making it suitable for real-world adoption.
References
[1]R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications, 4th ed., Springer, 2017.
[2]R. K. Paul, A. K. Mahto, et al., “Machine learning techniques for forecasting agricultural prices: a case of brinjal in Odisha, India,” PLOS ONE, vol. 17, no. 7, e0270553, 2022.
[3]T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD, 2016, pp. 785–794.
[4]S. Li, et al., “Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India,” Scientific Reports, vol. 14, no. 1, 16803, 2024.
How to Cite This Paper
Mr. Umesh Nanavare, Mr. Shantanu Patil, Mr. Om Patil,
Mr. Sunny Khokle, Mr. Harpalsing Rajput (2025). A Machine Learning Approach for Forecasting Vegetable Prices in Indian APMC Markets Using XGBoost. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
