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. 
More information - [Owomugisha et al.]

Resources 

This study has several accomplished including: 

1. Datasets using spectroscopy and imagery

2. Light spectrometer device for crop disease monitoring

Contact

Godliver Owomugisha
ogodliver@eng.busitema.ac.ug