Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{164583,
        author = {Yashas R and Sonika R and Harshit Kumar and Sanskar Sinha and Prof. Shobha Y},
        title = {Integrated ML-driven agricultural Technology Platform for Personalized Recommendations for Crop Selection, Resource Management, and Market Participation},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1866-1873},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164583},
        abstract = {India's agricultural sector, a significant contributor to the nation's GDP and workforce, faces challenges due to traditional farming practices and reliance on weather patterns. This research proposes a data-driven approach to empower farmers with optimal crop selection. By analyzing environmental factors such as soil nutrient levels, pH, humidity, and rainfall patterns, machine learning models including Decision Trees, Support Vector Machines, Logistic Regression, and Gaussian Naive Bayes will be applied to develop a website with a robust crop recommendation system. This system aims to bridge the gap between tradition and modernity, enhancing agricultural efficiency and productivity for Indian farmers.},
        keywords = {Agriculture, Machine Learning, Decision Trees, Support Vector Machines, Logistic Regression, Gaussian Naive Bayes, Website, Recommendation System, Agricultural Efficiency, Productivity.},
        month = {},
        }