A Distributed Deep Learning Network Based on Data Enhancement for Few-Shot Raman Spectral Classification of Litopenaeus vannamei Pathogens

Abstract

Litopenaeus vannamei is a common species in aquaculture and has a high economic value. However, Litopenaeus vannamei are often invaded by pathogenic bacteria and die during the breeding process, so it is of great significance to study the identification of shrimp pathogenic bacteria. The wide application of Raman spectroscopy in identifying directions of inquiry provides a new means for this. However, the traditional Raman spectroscopy classification task requires a large amount of data to ensure the accuracy of its classification. Therefore, the question of how to obtain higher classification accuracy through the means of a small amount of Raman spectrum data is a difficult point in the research. This paper proposes a distributed deep learning network based on data enhancement for few-shot Raman spectral classification of Litopenaeus vannamei pathogens. The network consists of RSEM, RSDM, and DLCM modules. The RSEM module uses an improved generative adversarial network combined with transfer learning to generate a large amount of spectral data. The RSDM module uses improved U-NET to denoise the generated data. In addition, we designed a distributed learning classification model (DLCM) which significantly speeds up model training, improves the efficiency of the algorithm, and solves the network degradation problem that often occurs during deep learning model training. The average classification accuracy of our proposed network on four shrimp pathogenic bacteria reaches 98.9%, which is higher than several models commonly used in Raman spectroscopy classification tasks. The method proposed in this article only needs the Raman spectra of a small number of microorganisms to complete the efficient and rapid identification of shrimp pathogenic bacteria, and this method certainly has the potential to solve the problem of the spectral classification of other microorganisms.