ML-CropAdvisor: A Data-Driven Approach to Crop Selection for Sustainable Farming

• Author(s): Amit Kumar; Dr. Anuj Chandila; Prof. (Dr.) Sanjay Pachauri
• Paper ID: 1712190
• Page: 1588-1589
• Published Date: 24-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

This paper presents a machine learning?based crop recommendation system designed to assist farmers in making data-driven agricultural decisions. The proposed system analyzes multiple environmental factors including soil nutrients (NPK), temperature, humidity, rainfall, and pH to recommend the most suitable crop for a given region. By applying machine learning algorithms such as Random Forest, Decision Trees, and Support Vector Machines (SVM), the system achieves high accuracy and reliability. The study demonstrates how data science and ML can contribute to smart farming, resource optimization, and improved agricultural productivity.

Keywords

Machine Learning, Crop Recommendation, Smart Farming, Random Forest, Agriculture Technology

Citations

IRE Journals:
Amit Kumar, Dr. Anuj Chandila, Prof. (Dr.) Sanjay Pachauri "ML-CropAdvisor: A Data-Driven Approach to Crop Selection for Sustainable Farming" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 1588-1589 https://doi.org/10.64388/IREV9I5-1712190

IEEE:
Amit Kumar, Dr. Anuj Chandila, Prof. (Dr.) Sanjay Pachauri "ML-CropAdvisor: A Data-Driven Approach to Crop Selection for Sustainable Farming" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712190