Institute Researcher Contributes to Publication in Prestigious "Internet of Things" Journal

A significant contribution to the field of agricultural technology and artificial intelligence has been made with the recent publication of a new research paper in the esteemed international journal, “Internet of Things.” We extend our warmest congratulations to all authors involved in this important work. The paper, titled “TinyResViT: A lightweight hybrid deep learning model for on-device corn leaf disease detection,” is scheduled for publication in March 2025.

The research introduces TinyResViT, an innovative lightweight hybrid deep learning model specifically designed for the efficient and accurate detection of corn leaf diseases directly on-device. This model represents a notable advancement in making sophisticated diagnostic tools more accessible and deployable in real-world agricultural settings. The study’s key contributions lie in the development of a computationally efficient model that does not compromise on detection accuracy, addressing a critical need for timely and effective crop disease management.

The potential impact of this research is substantial, offering a practical solution for early disease identification in corn crops, which can lead to more targeted interventions, reduced crop losses, and enhanced food security. By enabling on-device detection, TinyResViT can empower farmers with immediate diagnostic capabilities, particularly in regions where access to laboratory facilities or consistent internet connectivity may be limited. This work underscores the transformative potential of IoT and AI in modernizing agricultural practices.

Our institution is proud to acknowledge the dedication and innovative spirit demonstrated in this research. We congratulate the authors, Van-Linh Truong-Dang, Huy-Tan Thai, and Kim-Hung Le, on this significant academic achievement and their contribution to advancing scientific knowledge. We look forward to seeing the continued impact of their work in the field.