High-Performance Grape Disease Detection Method Using Multimodal Data and Parallel Activation Functions

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model’s computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection

A new pathogen pattern of acute respiratory tract infections in primary care after COVID-19 pandemic: a multi-center study in southern China

Abstract Background After the coronavirus disease 2019 (COVID-19) pandemic, no studies on bacterial and atypical pathogens were conducted in primary care. We aimed to describe the etiological composition of acute respiratory tract infections (ARTIs) presenting to primary care with limited resources after the pandemic. Methods 1958 adult patients with ARTIs from 17 primary care clinics were recruited prospectively from January 2024 to March 2024. 17 and 62 pathogens in throat swab samples were tested using polymerase chain reaction (PCR) and targeted next-generation sequencing (tNGS), respectively. We analyzed the pathogen spectrum and co-infectious pattern of viral, bacterial or atypical pathogens. Then, the associations between clinical characteristics and pathogens were investigated. Results In PCR test, the positive rate of any pathogens was 80.3%, consisting of 60.2% for viruses, 41.8% for bacteria and 21.7% for viral-bacterial co-infection. In tNGS test, the positive rate was 89.1%, consisting of 64.7% for viruses, 55.2% for bacteria and 30.9% for viral-bacterial co-infection. Influenza virus B (18.2%), influenza virus A (16.8%) and severe acute respiratory syndrome coronavirus 2 (14.1%) were the three leading viral pathogens, and H. influenzae (36.1%), S. anginosus (15.7%) and S. pneumoniae (8.4%) were the three leading bacterial pathogens. Few M. pneumoniae (1.6%) were detected. The mixed bacterial or mixed viral-bacterial co-infections were the most common co-infectious patterns. The mixed bacterial or mixed viral-bacterial co-infections were the most common co-infectious patterns. Overall, patients with viral infection or viral-bacterial co-infection had more clinical symptoms, and patients with bacterial infection had higher inflammatory indicators. Conclusions After the COVID-19 pandemic, the main viral pathogens of ARTIs were unevenly distributed, and less bacterial and atypical pathogens were detected in primary care. The microbiological evidences can optimize the precision diagnosis and treatment of ARTIs in primary care with limited resources