Optimized Crop Recommendation System Using Machine Learning for Soil Analysis

Apurv Verma; Chaitali Biswas Datta; Kalyani Pandey; Arpita Sinha; Khushi Raj Saxena

doi:10.2991/978-94-6463-738-0_76

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Optimized Crop Recommendation System Using Machine Learning for Soil Analysis

Authors

Apurv Verma¹^{, *}, Chaitali Biswas Datta¹, Kalyani Pandey¹, Arpita Sinha¹, Khushi Raj Saxena¹

¹Shri Shankaracharya Institute of Professional Management and Technology, Raipur, India

^*Corresponding author.

Corresponding Author

Apurv Verma

Available Online 22 June 2025.

DOI: 10.2991/978-94-6463-738-0_76 How to use a DOI?
Keywords: Random-Forest; Exploratory Data Analysis; Sustainable crop selection; localized farming solution
Abstract: The agricultural sector serves as a fundamental pillar of India’s economy; however, it is currently confronted with significant challenges, including climate change impacts and soil quality deterioration. This paper presents an innovative crop recommendation system that leverages machine learning techniques, specifically employing a Random Forest algorithm to conduct comprehensive soil analyses. The system demonstrated impressive performance metrics by evaluating critical parameters such as pH, moisture content, and nutrient availability, achieving 93% precision, 90% recall, and an F1 score of 91%. These analytical insights empower farmers so that they can make informed decisions regarding crop selection specifically aligned with the unique conditions of their local environments, thereby fostering sustainable agricultural practices. The system’s inherent flexibility and reliance on data-driven methodologies underscore its potential to revolutionize conventional approaches and effectively address the agricultural sector’s urgent challenges.
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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Volume Title: Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 22 June 2025
ISBN: 978-94-6463-738-0
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-738-0_76 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Apurv Verma
AU  - Chaitali Biswas Datta
AU  - Kalyani Pandey
AU  - Arpita Sinha
AU  - Khushi Raj Saxena
PY  - 2025
DA  - 2025/06/22
TI  - Optimized Crop Recommendation System Using Machine Learning for Soil Analysis
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 972
EP  - 987
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_76
DO  - 10.2991/978-94-6463-738-0_76
ID  - Verma2025
ER  -

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