Agriculture plays a crucial role in ensuring food security and economic stability, yet farmers often face challenges related to crop health, yield optimization, and disease management. This project focuses on developing a mobile application that leverages satellite imagery and artificial intelligence (AI) for crop optimization and disease detection. By integrating remote sensing data, machine learning models, and real time environmental analytics, the application provides farmers with actionable insights to enhance productivity and mitigate risks. The AI-driven disease detection system analyzes images of crops to identify potential infections, nutrient deficiencies, and pest infestations, enabling early intervention. Additionally, satellite-based vegetation indices and weather data assist in optimizing irrigation, fertilization, and planting strategies. This solution aims to empower farmers with advanced, accessible, and cost-effective technology to improve agricultural sustainability and yield quality.
