Development of a TaqMan probe-based multiplex real-time PCR for the simultaneous detection of four clinically important filamentous fungi.

Unlabelled: Filamentous fungi present significant health hazards to immunocompromised individuals globally; however, the prompt and precise identification of them during infection remains challenging. In this study, a TaqMan probe-based multiplex real-time PCR (M-qPCR) assay was developed to detect simultaneously the target genes of four important pathogenic filamentous fungi: gene of gene of spp, mitochondrial gene of Mucorales, and gene of . In this M-qPCR assay, the limit of detection (LoD) to all four kinds of fungi was 100 copies and the correlation coefficients (R) were above 0.

Detecting Pests From Light-Trapping Images Based on Improved YOLOv3 Model and Instance Augmentation.

Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments.

Detection of Small-Sized Insects in Sticky Trapping Images Using Spectral Residual Model and Machine Learning.

One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly () and thrips () are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one.