Detection of Contamination and Analysis of Vertical Transmission of BmNPV in Eggs and Moths of Bombyx mori

This study reports the molecular detection of Bombyx mori nucleopolyhedrovirus (BmNPV) in silkworm strains of the Universidade Estadual de Maringá Brazilian Germplasm Bank (UBGB). DNA extraction was carried out by using six Bombyx mori female moths of each strain, followed by PCR amplification. A pair of primers was designed based on a specific sequence of the baculovirus genome related to the BmNPV ORF 14. Another pair of primers was used to amplify the silkworm Actin A3 gene segment, which was used as positive control. Twenty gene pools were analyzed, and fifteen revealed a fragment of 443 base pairs (bp), which indicated the presence of the BmNPV. The frequency of contaminated moths was as following: 100% for silkworm strains M18-2, M12-2 and J1;83% for C25, C75 and C24 strains;66% for KR01;50% for M11-A;33% for AS3, B106, M8 and M11 and 16% for C211, E8 and Hindu strains. These are promising results for the identification of contaminated B. mori moths by BmNPV, which may prevent virus proliferation in subsequent generations. We also analyzed DNA samples extracted from B. mori eggs, but the results were not conclusive regarding the detection of the fragments of the expected size (443 bp). The difficulty in detecting BmNPV contamination in B. mori eggs may be due to the low concentration of virus in samples.

CA-YOLOv5: Detection model for healthy and diseased silkworms in mixed conditions based on improved YOLOv5

The accurate identification and localization of diseased silkworms is an important task in the research of disease precision control technology and equipment development in the sericulture industry. However, the existing deep learning-based methods for this task are mainly based on image classification, which fails to provide the location information of diseased silkworms. To this end, this study proposed an object detection-based method for identifying and locating healthy and diseased silkworms. Images of mixed healthy and diseased silkworms were collected using a mobile phone, and the category and location of each silkworm were labeled using LabelImg as a labeling tool to construct an image dataset for object detection. Based on the one-step detection model YOLOv5s, the ConvNeXt-Attention-YOLOv5 (CA-YOLOv5) model was designed in which the large kernel with depth-wise separable convolution (7×7 dw-conv) of ConvNeXt was adopted to expand receptive fields and the channel attention mechanism ECANet was added to enhance the capability of feature extraction. Experiments showed that the mean average precision (mAP) values of CA-YOLOv5 for healthy and diseased silkworms reached 96.46%, which is 1.35% better than that achieved via YOLOv5s. At the same time, the overall performance of CA-YOLOv5 was significantly better than state-of-the-art one-step models, such as Single Shot MultiBox Detector (SSD), CenterNet, and EfficientDet, and even improved YOLOv5 using image attention mechanism and a lightweight backbone, like SENet-YOLOv5 and MobileNet-YOLOv5. The results of this study can provide an important basis for the accurate positioning of diseased silkworms in precision disease control technology and equipment development.