Predictive Modelling of Agricultural Factors to Maximize Crop Yield

Anvesha Nayak, Pramathi Vummadi, Apoorva Raj, Nasam Saimani, Suresh Jamadagni

2025

Abstract

Crop yield prediction and factor analysis are methods through which technology can be utilized to improve the quality of current Agricultural practices. This study focuses on improving crop yields based on different factors and ascertaining how climate change affects these factors and their prediction. The aim is to create a tool for farmers to practice precision agriculture and to be made aware of what controllable factors can lead to better yield. The study proposes a three-step methodology for this process. First, we will analyse past years' data and also take into consideration the impact of climate change to know how this relates to these variables as well as crop yield. Secondly, we suggest some spatial variable management practices that could improve the overall agricultural output. Along with that, preventative measures to ensure crop safety are also suggested. Regular updates on these spatial variables will play an important role in helping the farmer make key decisions during the life cycle of the crop. Finally, in the third step of this process, we aim to perform anomaly analysis on pests, weeds, diseases, and climatic anomalies, and suggest relevant countermeasures to the farmer.

Paper Citation

in Harvard Style

Nayak A., Vummadi P., Raj A., Saimani N. and Jamadagni S. (2025). Predictive Modelling of Agricultural Factors to Maximize Crop Yield. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1360-1367. DOI: 10.5220/0013369400003890

in Bibtex Style

@conference{icaart25,
author={Anvesha Nayak and Pramathi Vummadi and Apoorva Raj and Nasam Saimani and Suresh Jamadagni},
title={Predictive Modelling of Agricultural Factors to Maximize Crop Yield},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1360-1367},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013369400003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Predictive Modelling of Agricultural Factors to Maximize Crop Yield
SN - 978-989-758-737-5
AU - Nayak A.
AU - Vummadi P.
AU - Raj A.
AU - Saimani N.
AU - Jamadagni S.
PY - 2025
SP - 1360
EP - 1367
DO - 10.5220/0013369400003890
PB - SciTePress