Sustainable crop disease monitoring relies on data-driven practices to enhance early detection and management strategies. Our research focuses on integrating advanced technologies into agriculture to improve crop disease detection and surveillance. This study is part of a broader research initiative aimed at leveraging machine learning techniques to enhance early crop disease and pest diagnostics. By combining multispectral sensing with AI-driven analytics, we seek to empower farmers with real-time, actionable insights to improve crop health and productivity. 

We have built a protocol that outlines the process for annotating a skin lesion database using images obtained with a camera. The aim is to collect and organize detailed information for each patient and their respective lesions while maintaining patient anonymity and confidentiality. The annotations will follow a strict naming convention and structure to ensure data consistency, accuracy, and integrity. All personnel involved in data collection, annotation, and storage must comply with these guidelines.

Collaboration and partnerships