Authors : Peeyush Gupta; Anuj Kumar Pal; Vishal Yadav; Lucknesh Kumar

Volume/Issue : RISEM–2025

Google Scholar : https://tinyurl.com/yvctu9jr

Scribd : https://tinyurl.com/4hwp9ap8

DOI : https://doi.org/10.38124/ijisrt/25jun153

Abstract : This research explores how machine learning (ML) can optimize agricultural productivity and sustainability. By analyzing key environmental factors such as soil composition, temperature, rainfall, and market trends, the system provides farmers with data-driven insights for optimal crop selection. Utilizing IoT-enabled sensors, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) algorithms, the model ensures precise recommendations. Additionally, a web-based platform and a feedback mechanism allow continuous improvement of recommendations. A comparative analysis with recent research from 2022-23 highlights the superior performance of our model over traditional methods, showing an increase in predictive accuracy by approximately 12%. This approach contributes to efficient resource utilization, promotes climate-resilient farming, and supports global food security efforts.

Keywords : Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Feedback Mechanism, Precision Agriculture, Smart Farming.