## 1 Introduction
Finally, DNA-based systems use a combination of cytogenetics and cytology after dissolving the shell; this type of method is really accurate and bases its strategy on comparing a DNA sequence that is sampled from well-known identified cases (especially grown-up mussels), with the one extracted from the larvae to be identified. Again, this is a mechanism that not only takes time and money but also does not provide size classification according to age or automatic accounting [23]. Focusing on bivalve larvae, most of the literature is based on techniques based on image analysis (manual or automatic) [24]. The fact that both immunological and DNA-based mechanisms need specialized equipment not available in many laboratories, those methods based on images obtained through the microscope are coping with every case [24]. This reality brings the need for automatization of the inspection, detection, classification, and accounting of the species in the sample, and this is why different research for the development of intelligent systems is born [24]. To the best of our investigations, the automatized methods for mollusk larvae in the literature are scarce. In the Mussel Classifier System Based on Morphological Characteristics [25], an automatic classifier of five species of mollusks based on their morphological characteristics is developed. This mimics the same procedure that skilled operators carry out to visually identify the different kinds of mollusks. The proposed system goes further by including statistical information on crop developments. With this system, a recognition rate of around 95% has been achieved, obtaining the results in a matter of seconds as long as the visual interface that demonstrates the results is not active. Other studies focus on proposing solutions to prevent the settlement of invasive aquatic species. The following paper describes an autoencoder-based automated system for extracting features from image sets. An autoencoder is a type of neural network used to reduce and extract relevant features about a dataset. In other words, this technique is not designed to identify and classify different types of mollusks but to detect the differences between them in order to discriminate against invasive species. With this technique, a harmonic average between sensitivity and reliability of 97% has been achieved [26]. Another novel technique is the ShellBi method, which consists of the supervised classification of images by means of birefringence stops in the shells of bivalve larvae. The research seeks to improve the accuracy of ShellBi. The improvements have been obtained in larvae reared in conditions similar to those of the natural environment, so it concludes that it has application with field samples with a potential future to the extension of different marine systems [27]. In [28], an image processing algorithm is introduced to identify, classify, and count the mussels in seawater using computer vision, but the research is focused on grown mussels, not microscopic larvae. In [29], a CNN is proposed to identify and classify mussels from other types of species (barnacles, worms), but, again, it uses grown species instead of larvae under the microscope. The same approach is given in [30], where a CNN method is applied to perform image segmentation, with a genetic programming mechanism (but applied to buoys). Finally, in [31], Sentinel-2 images are used to automatically identify and count mussel platforms using MLP and a neural network. Thus, different perspectives on the use of artificial vision and machine learning to address the identification of mollusks are highlighted but lack focus on larvae, especially mussel larvae. This proves that there is already evidence in the field and opens the way for us to carry out research in this field under a new premise. In addition, we will add a new approach by proposing an automatic counting of samples to speed up the work process of experts.