Deep learning enhanced terahertz imaging of silkworm eggs development

Terahertz (THz) technology lays the foundation for next-generation high-speed wireless communication, nondestructive testing, food safety inspecting, and medical applications. When THz technology is integrated by artificial intelligence (AI), it is confidently expected that THz technology could be accelerated from the laboratory research stage to practical industrial applications. Employing THz video imaging, we can gain more insights into the internal morphology of silkworm egg. Deep learning algorithm combined with THz silkworm egg images, rapid recognition of the silkworm egg development stages is successfully demonstrated, with a recognition accuracy of ∼98.5%. Through the fusion of optical imaging and THz imaging, we further improve the AI recognition accuracy of silkworm egg development stages to ∼99.2%. The proposed THz imaging technology not only features the intrinsic THz imaging advantages, but also possesses AI merits of low time consuming and high recognition accuracy, which can be extended to other application scenarios. [Display omitted] •THz QCL video system recorded the changing of the silkworm egg internal morphology•Established a silkworm egg dataset corresponding to THz image and optical image•Combining deep learning and data fusion to detect silkworm egg development stages Terahertz imaging; Applied physics; Machine learning