Crop Price Estimation Using Stacking Ensemble Technique
Keywords:
Price Prediction, Stack Regressor, XGB Regressor, Random Forest RegressorAbstract
The agricultural sector in India, contributing approximately 17% to the GDP and engaging over 60% of the workforce, is at a pivotal juncture between traditional practices and modern technological advancements. This research explores the frontier of agricultural economics by employing an innovative approach to crop price prediction through the utilization of the Stacking Regressor algorithm. This advanced ensemble learning technique amalgamates the strengths of diverse regression models, harnessing their collective predictive power to yield superior accuracy. The study synthesizes historical crop data to construct a robust predictive framework. By integrating Random Forest and XGBoost regressors, the Stacking Regressor not only captures intricate patterns within the dataset but also adapts dynamically to the ever-changing agricultural landscape. This research aims to redefine the precision of crop price forecasting, offering a holistic and adaptable solution to stakeholders in the agri-business sector. As the global demand for sustainable and resilient agricultural practices intensifies, this research endeavors to empower farmers with a state-of-the-art solution, fostering informed decision-making and contributing to the advancement of a more resilient and efficient agricultural ecosystem.
Downloads
References
Pandit Samuel, B.Sahithi, T.Saheli, D.Ramanika, N.Anil Kumar “Crop Price Prediction System using Machine learning Algorithms." Quest Journals Journal of Software Engineering And Simulation, Vol. 06, No. 01, 2020,Pp. 14-20.
Kumar, Y. Jeevan Nagendra, V. Spandana, V. S. Vaishnavi, K. Neha, and V. G. R. R. Devi. “Supervised Machine learning Approach for Crop Yield Prediction in Agriculture Sector”.
“Stacking Strong Ensembles of Classifiers” by Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas, Sotiris B. Kotsiantis, and Michael N. Vrahatis.
Rohith R, Vishnu R, Kishore A, Deeban Chakkarawarthi, "Crop Price Prediction and Forecasting System using Supervised Machine Learning Algorithms", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 9, Issue 3, March 2020.
R. Nagini, “Agriculture yield prediction using predictive analytic techniques”, 2nd International Conference on Contemporary Computing and Informatics, 2016.
S.Veenadhari, Dr. Bharat Misra, Dr. CD Singh.2019. Machine learning approach for forecasting crop yield based on climatic parameters.
Yung-HsinPeng, Chin-Shun Hsu, and Po-Chuang Huang Developing Crop Price Forecasting Service Using Open Data from Taiwan Markets, 2017 IEEE
Bharati Panigrahi, Krishna Chaitanya Rao Kathala, M. Sujatha. "A Machine Learning Based Comparative Approach to Predict the Crop Yield Using Supervised Learning With Regression Models" , Procedia Computer Science, 2023
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
