Portable Edge Computing System Analysis for Real-time Detection and Classification of Wheat Yellow Rust Infection Types Using Embedded AI

Wheat rust diseases, notably yellow rust, pose a substantial threat to global food security by adversely impacting crop yields. The timely and precise detection of these diseases is imperative for implementing effective management strategies. This research paper delves into the challenges associated with categorizing wheat yellow-rust infection types and critically reviews existing approaches within the domain. The primary focus is on embedded AI solutions, digital image processing techniques, and deep learning methodologies aimed at achieving accurate and early detection of wheat rust diseases. In addition, the paper aligns with Sustainability Development Goals (SDGs) by contributing to global efforts in ensuring food security, as the proposed integrated AI approach addresses identified issues, fostering a sustainable and resilient agricultural ecosystem.