AI enhanced Smart Crop Selection

This paper presents an innovative approach to smart crop selection by utilizing machine learning algorithms, specifically XGBoost, combined with AI-driven insights. The proposed system integrates soil characteristics, climatic conditions, and market trends to offer precise recommendations tailored to optimize agricultural productivity and sustainability. By leveraging predictive analytics and neural networks, this system provides real-time yield predictions, pest risk analysis, and economic feasibility reports. The integration of advanced technologies bridges the gap between traditional farming methods and modern agricultural advancements, ensuring a comprehensive and efficient decision-making process for farmers. Key Words: Smart agriculture, machine learning, XGBoost, AI, crop recommendation, sustainable farming, predictive analytics