Grape Leaf Disease Detection and Recognition Method Based on Improved Model RTDETR-DRA

In recent years, the rapid development of artificial intelligence has revolutionized agriculture, particularly in the detection and identification of plant diseases. Grape leaf diseases pose a significant threat to grape production, directly impacting yield and quality. To address the limitations of existing disease detection models, such as high computational cost and inefficient deployment on resource-limited devices, we propose an improved model based on RTDETR, named RTDETR-DRA. This model integrates the DRBNCSPELAN module, leveraging multi-path feature fusion and channel split design to optimize real-time detection accuracy while minimizing computational cost. Experimental results show that RTDETR-DRA outperforms the original RTDETR model, achieving higher accuracy in disease detection with significantly reduced parameters and GFLOPs, making it suitable for lightweight deployment on platforms like mobile devices. Our proposed method contributes to the advancement of efficient and precise plant disease detection technologies.