International Journal on Science and Technology

E-ISSN: 2229-7677     Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.

Intelligent Crop Selection System using Ensemble Learning with Random Forest Approach for Sustainable Farming

Author(s) B Jeyashankari, M Dheetchanya, M Shree Soundarya, M S Bennet Praba
Country India
Abstract Crop analysis and prediction technology continue to expand rapidly because it serves as a critical tool for agricultural operation enhancement. In
agricultural practices farmers depend on crop recommendations since these assists them in determining which crops their area and climate support most productively. Specialist knowledge operated as the main requirement in this process which demanded significant time consumption in the past. A sustainable way of living becomes necessary for continued existence. Agricultural experts believe machine learning automations should serve as a foundation for crop suggestion automation and pest detection to help farmers maximize their farming output with nutritious soil maintenance [1]. This paper develops a precise machine learning model which functions effectively for crop recommendation. A number of features within the proposed system utilize climatic data together with soil composition to make precise crop recommendations for specific areas. Agricultural crop recommendations would experience a transformative change due to this technology which leads to higher yields and sustainable farming alongside improved profitability for farmers at different scales. Through evaluation of many machine learning algorithms, we successfully measured perfect accuracy by running detailed tests over a large historical dataset. Our highest achieved accuracy level amounted to 99.5%
accuracy.
Keywords Agriculture, crop, food, environmental factors, machine learning, prediction, data analysis, recommendation, big data, and agricultural productivity.
Field Computer > Data / Information
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-22
DOI https://doi.org/10.71097/IJSAT.v16.i2.3975
Short DOI https://doi.org/g9gdt3

Share this