New Research Published on Real-Time Leaf Disease Detection for Smart Agriculture

We are pleased to announce another significant contribution to the field of smart agriculture and crop protection with the publication of a new research paper in the esteemed international journal, Crop Protection. The paper, titled “MobileH-Transformer: Enabling real-time leaf disease detection using hybrid deep learning approach for smart agriculture,” was published in March 2025.

This innovative research introduces MobileH-Transformer, a hybrid deep learning model engineered to enable real-time detection of leaf diseases, a critical component for advancing smart agriculture. The study focuses on providing an efficient and timely diagnostic tool that can be deployed to aid in the rapid identification of plant diseases directly in the field. The key contribution lies in the development of a novel hybrid approach that leverages the strengths of different deep learning architectures to achieve robust and accurate real-time detection capabilities, potentially on mobile or edge devices.

The potential impact of the MobileH-Transformer model is substantial for modern farming. By facilitating early and precise identification of crop diseases, this research offers a pathway to more effective and targeted crop protection strategies, thereby minimizing yield losses and reducing the reliance on broad-spectrum pesticides. This advancement supports sustainable agricultural practices and enhances the efficiency of farm management, marking a significant step forward in the application of AI in agriculture.

Our institution is proud to recognize the important work and dedication demonstrated in this research. We congratulate the authors, Huy-Tan Thai and Kim-Hung Le, on their significant academic contribution. This publication underscores the innovative research being conducted to address critical challenges in agriculture, and we look forward to the continued positive impact of their work.