StrawberryTalk-v2: Edged-IoT System for Detection of Strawberry Anthracnose

Anthracnose diseases can severely affect strawberry plant stands and yields, making early detection essential. Manual identification of diseased plants is labor-intensive, prompting the development of IoT-based AI (AIoT) solutions for more efficient detection. While many AIoT methods rely on high-performance servers typically hosted in the cloud, edge solutions are preferable for commercial farms to reduce dependency on network connections. To implement an effective edge-based system, the computation hardware must feature a capable CPU for running the IoT engine and a GPU sufficient for YOLO-based detection, while maintaining code security. This article explores the StrawberryTalk-v2 solution using the NVIDIA Jetson Nano, secured with Winbond’s W77Q TRUSTME® Secure Serial Flash Memory, fulfilling these requirements. The deployment of the StrawberryTalk-v2 IoT solution on this hardware facilitates strawberry anthracnose detection. Choosing a YOLO version that balances edge device efficiency and high detection accuracy presents a significant challenge. While YOLO11 Nano’s computational complexity is well-suited for edge deployment, its baseline accuracy falls short of YOLO11 Extra Large. By incorporating WIoU and DySample into YOLO11 Nano, the detection accuracy exceeds that of the Extra Large version while retaining the Nano version’s low execution cost. These enhancements allow the system to achieve superior performance, with StrawberryTalk-v2 reaching a four-level detection accuracy of 95.5%.