Experimental Evaluation of Agriculture and Horticulture Commodities Price Prediction Using Histogram Based Gradient Boosting Algorithm

S. Saranya, Arul Murugan N., Bharanidharan K., Jeevanantham C., Jeevak S.

2025

Abstract

Accurately predicting agricultural prices are critical to achieve the sustainable and healthy growth of agriculture, which is why agricultural price prediction is a prominent study issue in the sector. On the flip side, it's affected by a lot of things, the most important of which are the fluctuations in agricultural commodity prices. This paper dives into investigating the pricing patterns of important agricultural commodities among different producers worldwide, acknowledging the potential of Deep Learning in agricultural applications. Farmers, dealers, and lawmakers all have a critical responsibility for agricultural price prediction to enable them to make informed decisions regarding planting, pricing, and distribution. Agriculture is a notoriously complex and cluttered market, and typical price perspective models have proven themselves incapable. Recent research has shown that deep learning algorithms analyzing large volumes of historical data to identify nonlinear dependencies, significantly improves the accuracy of price predictions. We propose Histogram based Gradient Boosting (HGB), a time series dominate based deep learning model that predicts agricultural prices. The standard learning model Random Forest (RF) is used to cross-validate the effectiveness of the proposed model. Along with historical pricing, the proposed model is trained on other influencing factors such as seasonality, weather, and market demand indicators. The predictions made using the deep learning one were more accurate and robust in comparison to more traditional models. Experimental results suggest that this approach can improve on-farm decision-making and result in more efficient and stable market systems.

Paper Citation

in Harvard Style

Saranya S., N. A., K. B., C. J. and S. J. (2025). Experimental Evaluation of Agriculture and Horticulture Commodities Price Prediction Using Histogram Based Gradient Boosting Algorithm. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 730-737. DOI: 10.5220/0013871900004919

in Bibtex Style

@conference{icrdicct`2525,
author={S. Saranya and Arul N. and Bharanidharan K. and Jeevanantham C. and Jeevak S.},
title={Experimental Evaluation of Agriculture and Horticulture Commodities Price Prediction Using Histogram Based Gradient Boosting Algorithm},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={730-737},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013871900004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Experimental Evaluation of Agriculture and Horticulture Commodities Price Prediction Using Histogram Based Gradient Boosting Algorithm
SN - 978-989-758-777-1
AU - Saranya S.
AU - N. A.
AU - K. B.
AU - C. J.
AU - S. J.
PY - 2025
SP - 730
EP - 737
DO - 10.5220/0013871900004919
PB - SciTePress