Integrated ML-driven agricultural Technology Platform for Personalized Recommendations for Crop Selection, Resource Management, and Market Participation

Integrated ML-driven agricultural Technology Platform for Personalized Recommendations for Crop Selection, Resource Management, and Market Participation
Author(s):
Yashas R, Sonika R, Harshit Kumar, Sanskar Sinha, Prof. Shobha Y
Keywords:
Agriculture, Machine Learning, Decision Trees, Support Vector Machines, Logistic Regression, Gaussian Naive Bayes, Website, Recommendation System, Agricultural Efficiency, Productivity.
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.
Article Details
Unique Paper ID: 164583 Publication Volume & Issue: Volume 10, Issue 12 Page(s): 1866 - 1873
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