Crop Optimizer: Efficient Data Driven Solutions

Authors

  • Mehul Kumar SRMIST, Chennai, 603203, Tamil Nadu, India Author
  • Maanas Gulati SRMIST, Chennai, 603203, Tamil Nadu, India Author

DOI:

https://doi.org/10.21467/proceedings.7.6.46

Keywords:

Crop recommendation, Geographic Information System (GIS), weather data

Abstract

This project leverages a data-driven framework to enhance agricultural productivity in India, by combining Geographic Information System (GIS) technology and meteorological data. The system uses machine learning model like Long Short-Term Memory (LSTM) and various algorithms to suggest the best crop options to farmers based on vicinity-particular environmental conditions and forecasted weather data in a region. Preliminary results from over 6,000 fact entries suggest a baseline accuracy of about 35 to over 85 answer targets to provide actionable insights that empower farmers to make informed selections, probably increasing crop yields via as many as 30 practices. The solution is to provide insights and help farmers make informed decisions that will lead to better crop yields and sustainable agriculture.

References

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Published

2025-11-21

Issue

Vol. 7 No. 6 (2025): Proceedings of the 3rd International Conference on Artificial Intelligence, Machine Learning and Cybersecurity

Section

Articles

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Copyright (c) 2025 Mehul Kumar, Maanas Gulati (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

[1]
M. Kumar and M. Gulati, “Crop Optimizer: Efficient Data Driven Solutions”, AIJR Proc., vol. 7, no. 6, pp. 404–411, Nov. 2025, doi: 10.21467/proceedings.7.6.46.