AI-Based Multi-Parameter Decision System For Crop Recommendation And Disease Prediction

Abstract

Agriculture is one of the most important sectors for food security and economic stability worldwide. Current methods in crop selection and disease diagnosis tend to rely on expertise that may not always deliver optimal results without real-time data analytics. The following review will cover the state of the art related to crop recommendation systems and crop disease prediction models enabled through advancements in AI and ML. It examines a range of approaches regarding how soil, weather, and disease data are integrated and identifies the dominant gaps in current research. Building on these findings, this work will introduce an integrated multi-parameter AI-enabled decision support system that can synthesize information on soil properties, weather forecasts, disease susceptibility, and economics toward intelligent crop recommendations.