Agricultural Crop Recommendations Based on Productivity and Season

Boya Ashwini, Gundampalle Mamatha, Ampireddygari Durga Bhavani, Ediga Gayathri, Vemula Rohini

2025

Abstract

Agriculture plays a vital role in ensuring global food security and economic stability. However, the increasing uncertainty of environmental conditions poses significant challenges to farmers, affecting crop yields and sustainability. Agriculture and machine learning have a significant relationship, as machine learning can be applied to various aspects of agriculture to improve efficiency, productivity, and sustainability. To address this issue, our project aims to develop an innovative agricultural crop recommendation system that integrates farmer specific data and real-time environmental data to provide personalized, climate-resilient suggestions. The proposed system leverages decision tree algorithms for interpretability and accuracy. Additionally, it incorporates Geographical Information Systems (GIS) for spatial analysis of soil and weather patterns. By integrating farmer- specific data, such as resource availability, and risk tolerance, with real-time environmental data, including weather patterns, soil moisture, and temperature, the system provides farmers with sustainable, location-specific crop recommendations. The outcomes of this project include improved crop yields and productivity, reduced risks associated with climate uncertainty, enhanced economic outcomes and stability, and the adoption of climate-resilient agricultural practices. The proposed system which is based on Decision tree algorithm has resulted in 17.46% more accurate than the previous recommender system that is based on the SVM algorithm.

Paper Citation

in Harvard Style

Ashwini B., Mamatha G., Bhavani A., Gayathri E. and Rohini V. (2025). Agricultural Crop Recommendations Based on Productivity and Season. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 13-17. DOI: 10.5220/0013907200004919

in Bibtex Style

@conference{icrdicct`2525,
author={Boya Ashwini and Gundampalle Mamatha and Ampireddygari Bhavani and Ediga Gayathri and Vemula Rohini},
title={Agricultural Crop Recommendations Based on Productivity and Season},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={13-17},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013907200004919},
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 - ICRDICCT`25
TI - Agricultural Crop Recommendations Based on Productivity and Season
SN - 978-989-758-777-1
AU - Ashwini B.
AU - Mamatha G.
AU - Bhavani A.
AU - Gayathri E.
AU - Rohini V.
PY - 2025
SP - 13
EP - 17
DO - 10.5220/0013907200004919
PB - SciTePress