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ML-CropAdvisor: A Data-Driven Approach to Crop Selection for Sustainable Farming
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I5-1712190
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
How to cite this paper
@article{1712190,
author = {Amit Kumar, Dr. Anuj Chandila, Prof. (Dr.) Sanjay Pachauri},
title = {ML-CropAdvisor: A Data-Driven Approach to Crop Selection for Sustainable Farming},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {1588-1589},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712190.pdf},
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},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712190}
}