摘要Salt stress is considered one of the primary threats to cotton production.Although cotton is found to have reasonable salt tolerance,it is sensitive to salt stress during the seedling stage.This research aimed to propose an effective method for rapidly detecting salt stress of cotton seedlings using multicolor fluorescence-multispectral reflectance imaging coupled with deep learning.A prototyping platform that can obtain multicolor fluorescence and multispectral reflectance images synchronously was developed to get different characteristics of each cotton seedling.The experiments revealed that salt stress harmed cotton seedlings with an increase in malondialdehyde and a decrease in chlorophyll content,superoxide dismutase,and catalase after 17 days of salt stress.The Relief algorithm and principal component analysis were introduced to reduce data dimension with the first 9 principal component images(PC1 to PC9)accounting for 95.2％of the original variations.An optimized EfficientNet-B2(EfficientNet-OB2),purposely used for a fixed resource budget,was established to detect salt stress by optimizing a proportional number of convolution kernels assigned to the first convolution according to the corresponding contributions of PC1 to PC9 images.EfficientNet-OB2 achieved an accuracy of 84.80％,91.18％,and 95.10％for 5,10,and 17 days of salt stress,respectively,which outperformed EfficientNet-B2 and EfficientNet-OB4 with higher training speed and fewer parameters.The results demonstrate the potential of combining multicolor fluorescence-multispectral reflectance imaging with the deep learning model EfficientNet-OB2 for salt stress detection of cotton at the seedling stage,which can be further deployed in mobile platforms for high-throughput screening in the field.