The
localized surface-plasmon resonance of the AuNP in aqueous
media is extremely sensitive to environmental changes. By measuring
the signal of plasmon scattering light, the dark-field microscopic
(DFM) imaging technique has been used to monitor the aggregation of
AuNPs, which has attracted great attention because of its simplicity,
low cost, high sensitivity, and universal applicability. However,
it is still challenging to interpret DFM images of AuNP aggregation
due to the heterogeneous characteristics of the isolated and discontinuous
color distribution. Herein, we introduce machine vision algorithms
for the training of DFM images of AuNPs in different saline aqueous
media. A visual deep learning framework based on AlexNet is constructed
for studying the aggregation patterns of AuNPs in aqueous suspensions,
which allows for rapid and accurate identification of the aggregation
extent of AuNPs, with a prediction accuracy higher than 0.96. With
the aid of machine learning analysis, we further demonstrate the prediction
ability of various aggregation phenomena induced by both cation species
and the concentration of the external saline solution. Our results
suggest the great potential of machine vision frameworks in the accurate
recognition of subtle pattern changes in DFM images, which can help
researchers build predictive analytics based on DFM imaging data.