*3.5. Discussion*

Airborne fungal diseases mostly float in the air in the form of spores and spread with the wind before their widespread outbreaks [4,8]. At the time of disease occurrence, the concentration of disease spores in the air is higher than 100 spores /m3; if timely detection and prevention and control measures are not taken, the disease spores will drift to other areas with the wind and continue to infect other areas. Y. Zhang et al. proposed a deep-learningbased fungal spore detector FSNet for recognition and automatic counting of *Aspergillus glaucus, Penicillium solitum*, and *Aspergillus candidus*, and the experiments demonstrated that FSNet achieved an average precision of 0.9, 0.944, and 0.904 on *Aspergillus glaucus, Penicillium solitum,* and *Aspergillus candidus*, respectively, demonstrating the ability to automate detection of spores in the laboratory [7]. However, the automatic detection of spores based on images cannot accurately identify spores with similar appearance. Aswathi S. et al. were able to differentiate between dead and live *C. sporogenes* spores on media (SBA and TSA) plates using hyperspectral imaging [34]. The use of hyperspectral images and spectral information can accurately identify spores, but the effective reflectance of the hyperspectral spectrum for spores is limited to the spectral band range, and the spores cannot be captured and detected. The method for detecting and identifying disease spores of microfluidic chip combined with Raman microscopy developed in this study can capture spores in the air and then accurately identify the spores by identifying their Raman fingerprints, and this method does not require cumbersome biochemical experiments with low cost.
