Discrimination of dead and viable biological spore based on the convolutional neural network

Hao Cao; Youlin Gu; Guolong Chen; Yihua Hu; Wanying Ding; Haihao He

doi:10.1117/12.2651922

20 January 2023 Discrimination of dead and viable biological spore based on the convolutional neural network

Hao Cao, Youlin Gu, Guolong Chen, Yihua Hu, Wanying Ding, Haihao He

Proceedings Volume 12558, AOPC 2022: Optical Spectroscopy and Imaging; 125580K (2023) https://doi.org/10.1117/12.2651922
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

The detection of biological spore activity is the basis for effective prevention and control of plant and animal diseases. However, the reduction of its activity level during storage is one of the major problems affecting the application. A rapid and accurate method to detect the activity of biological spores is of great value for exploration and research. In this paper, UV-Vis spectroscopy combined with a one-dimensional convolutional neural network (1D-CNN) is used for the discrimination of dead and viable biological spore. The spectrum of three biological spores were collected and preprocessed by the standard normal variate transformation (SNV). Unsupervised clustering of the sample set was performed using principal component analysis (PCA). The activity discrimination model of biological spores is constructed based on 1D-CNN. The experimental results show that the model has a discriminative accuracy of 100%, which has the potential to replace the traditional methods of determining the dead and viable biological spore.

Citation Download Citation

Hao Cao, Youlin Gu, Guolong Chen, Yihua Hu, Wanying Ding, and Haihao He "Discrimination of dead and viable biological spore based on the convolutional neural network", Proc. SPIE 12558, AOPC 2022: Optical Spectroscopy and Imaging, 125580K (20 January 2023); https://doi.org/10.1117/12.2651922

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