The main aim of this study was to evaluate the feasibility of hyperspectral imagery for determining the correlation between fish skin changes and different diets. Rainbow trout (Oncorhynchus mykiss) were fed either a commercial based diet (N = 80) or a 100% plant-based diet (N = 80). Hyperspectral images were made using a push-broom hyperspectral imaging system in the spectral region of 394-1009 nm. All images were calibrated using dark and white reference, and the average spectral data from the region of interest were extracted. Seven spectral pre-treatment methods were used, including Savitzky-Golay (SG), First Derivative (FD), Second Derivative (SD), Standard Normal Variate (SNV), Multiplicative Scatter Correction(MSC) and Continuum removal (CR) then a support vector machine (SVM) with linear kernel was applied to establish the classification models. Overall classification models developed from full wavelengths with different preprocessing methods showed good performance (Correct Classification Rate (CCR) = 0.83, Kappa = 0.66) when coupled with SG and SD or SG and MSC. The overall results indicate that the integration of Vis/NIR hyperspectral imaging system and machine learning algorithms have promise for discriminating different diets based on the live fish skin. These procedures can be used to not only identify the diet used for fish feeding in the case where we are not sure but also monitor different diets impacts on live fish skin for more precise monitoring of fish status during cultivation and ultimately for better implementation of precision fish farming.