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ML-CropAdvisor: An Enhanced Data-Driven Approach for Crop Selection in Sustainable Agriculture
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I10-1716864
Abstract
This paper presents an advanced machine learning–based crop recommendation system aimed at enhancing decision-making in sustainable agriculture. The proposed system utilizes critical environmental and soil parameters, including Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, rainfall, and pH, to predict the most suitable crop for cultivation. Unlike traditional approaches, this study incorporates multiple machine learning models, including Random Forest, Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM), to improve prediction performance. Experimental results demonstrate that the LGBM classifier outperforms other models, achieving an accuracy of 97.58%, indicating superior predictive capability and robustness. Feature importance analysis reveals that rainfall, soil nutrients, and pH play a crucial role in crop selection. The system provides a scalable and data-driven solution for precision agriculture, contributing to improved yield, efficient resource utilization, and sustainable farming practices.
Keywords
Machine Learning, Crop Recommendation, Smart Farming, Random Forest, Agriculture Technology
How to cite this paper
@article{1716864,
author = {Amit Kumar, Dr. Mohd Danish, Prof. (Dr.) Sanjay Pachauri},
title = {ML-CropAdvisor: An Enhanced Data-Driven Approach for Crop Selection in Sustainable Agriculture},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {2846-2848},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716864.pdf},
abstract = {This paper presents an advanced machine learning–based crop recommendation system aimed at enhancing decision-making in sustainable agriculture. The proposed system utilizes critical environmental and soil parameters, including Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, rainfall, and pH, to predict the most suitable crop for cultivation. Unlike traditional approaches, this study incorporates multiple machine learning models, including Random Forest, Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM), to improve prediction performance. Experimental results demonstrate that the LGBM classifier outperforms other models, achieving an accuracy of 97.58%, indicating superior predictive capability and robustness. Feature importance analysis reveals that rainfall, soil nutrients, and pH play a crucial role in crop selection. The system provides a scalable and data-driven solution for precision agriculture, contributing to improved yield, efficient resource utilization, and sustainable farming practices.},
keywords = {Machine Learning, Crop Recommendation, Smart Farming, Random Forest, Agriculture Technology},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716864}
}