This chapter presents a case study on the pigmentation of captive marine species. It explains the possibility of implementing an algorithm using a Convolutional Neural Network (CNN) and pre-processing algorithms to detect pigmentation of the red snappers in captivity. The Lutjanidae family has a high commercial value due to the attractiveness of their skin color. Currently, during the culture of this species, a loss of this coloration is observed during its maintenance in captivity; aquaculturists must preserve these natural characteristics. The coloration of their skin is the most economically interesting characteristic in these fish species. Therefore, it is essential to determine whether captive species modify their coloration. In this study, we describe an experiment with different captive snappers with low coloration to determine if their pigmentation increases when shrimp head meal is fed. This study was divided into two stages: the first consisted of determining the impact, whether actual or not, of feeding red snapper shrimp head meal, as well as reviewing the state-of-the-art algorithms developed to determine skin color changes, which served as a reference for the proposal to be presented. The second stage consists of developing and evaluating the algorithm with the marine species in question. The use of artificial intelligence (AI) can help us automatically detect snapper pigmentation. Therefore, we propose applying a deep learning model to carry out this task. Pre-processing was performed beforehand.