## 4 Conclusions  
The European Union's mussel production industry is a significant market, dependent on obtaining mussel larvae as seed for cultivation. Traditionally, the process of monitoring larvae presence in seawater involves manual sampling and labor-intensive microscopic counting, susceptible to human error and time-consuming procedures. To address these challenges, our research introduces a computer vision-based methodology aimed at accurately identifying, classifying, and quantifying mussel larvae individuals across various developmental stages from microscopic images of water samples.  
Our approach is the utilization of a neural network architecture derived from the YOLO method, incorporating convolutional, pooling, and fully connected layers to automate the detection, classification, and accounting tasks. Through the use of several manually labeled samples for model training and evaluation, along with data augmentation techniques to mitigate overfitting risks, we established a robust framework capable of effectively processing diverse larval specimens.  
The outcomes of our study highlight the effectiveness of the proposed algorithm, as evidenced by standard performance metrics including Recall-Confidence, Precision-Recall, and F1 scores. These metrics affirm the algorithm's aptitude in accurately identifying, classifying, and accounting for different mussel larval classes present in microscopic images, surpassing human capacities in both speed and reliability. Consequently, our research not only presents a viable solution to streamline mussel larvae monitoring processes but also showcases the potential of computer vision techniques in enhancing efficiency and accuracy within aquaculture industries.  
The next steps in our research include extending the spectrum of species to the rest of the bivalve molluss under industrial growth (not only mussels but also clams, oysters, cockles, razor claims, etc.), and developing an automated machine that will accept laboratory plates with seawater and will perform the capture of the image and return the accounting of the different species individuals.  
Conceptualization, P.O.-C. and C.G.-S.; data curation, G.R.-G.; formal analysis, F.A.-A.; funding acquisition, C.G.-S.; investigation, P.O.-C.; methodology, P.O.-C.; project administration, F.A.-A.; resources, C.G.-S., F.A.-A. and G.R.-G.; software, P.O.-C.; supervision, F.A.-A.; validation, C.G.-S. and G.R.-G.; writing--original draft, P.O.-C.; writing--review and editing, G.R.-G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Directorate of Fisheries, Aquaculture and Technological Innovation (Conselleria do Mar) and by the European Union--FEMP. Measure 2: Increase in the potential of aquaculture production areas (Art. 51). Co-financing percentage measured 75% EMFF and 25% Member State (EMFF: European Maritime and Fisheries Fund).  
Figure 12: F1-Confidence Curve.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author due to privacy reasons.  
[PERSON], [PERSON], [PERSON] and [PERSON], for the selection of the samples.  
The authors declare no conflicts of interest.