ML-CropAdvisor: A Data-Driven Approach to Crop Selection for Sustainable Farming

This paper presents a machine learning–based crop recommendation system designed to assist farmers in making data-driven agricultural decisions. The proposed system analyzes multiple environmental factors including soil nutrients (NPK), temperature, humidity, rainfall, and pH to recommend the most suitable crop for a given region. By applying machine learning algorithms such as Random Forest, Decision Trees, and Support Vector Machines (SVM), the system achieves high accuracy and reliability. The study demonstrates how data science and ML can contribute to smart farming, resource optimization, and improved agricultural productivity.