Machine Learning-Based Agricultural Analysis for Accurate Crop Recommendation and Yield Prediction

DOI

10.2991/978-94-6463-858-5_54How to use a DOI?

Keywords

Crop recommendation; yield prediction; machine learning; agriculture; soil type; area under cultivation; crop selection

Abstract

Agriculture is a critical industry that ensures food security and economic development. Farmers, however, usually face challenges in choosing the appropriate crops and yield estimation because of differences in soil characteristics and land availability. This project proposes a Crop Recommender System based on Machine Learning to assist farmers in making informed decisions about crop choice and yield estimation. The system, through analyzing past agricultural statistics like soil condition, area cultivated, location, and type of crop, predicts the correct yields and recommends crops with high yield without the need for GPS parameters. Machine learning techniques are employed to analyze past trends, identify patterns, and generate accurate predictions. The system consists of two key components: a service provider module to train and validate machine learning models, and a remote user module where farmers input their soil and land information to receive crop suggestions and yield estimations. The web server and database components facilitate efficient data processing and storage. By integrating data-driven insights, this system aims to enhance agricultural productivity, optimize land use, and support farmers in making more efficient cultivation decisions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Vikas Reddy
AU  - M. Sai Harsha
AU  - K. Kaushal
AU  - K. Archana
PY  - 2025
DA  - 2025/11/04
TI  - Machine Learning-Based Agricultural Analysis for Accurate Crop Recommendation and Yield Prediction
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 625
EP  - 633
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_54
DO  - 10.2991/978-94-6463-858-5_54
ID  - Reddy2025
ER  -