Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.

Marine pollution is one of today\'s most relevant problems. Public awareness has been raised about the harmful potential of heavy metals (HMs) accumulating in edible fish and possibly ending up in human diet through the food chain. This study aimed to characterize and evaluate As, Cd, Cr, Cu, Ni and Pb contents in four edible fish species from the western Mediterranean Sea. Liver and muscle toxic elements were determined by GF-AAS in Mullus surmuletus, Merluccius merluccius, Auxis rochei and Scomber japonicus from Almería Bay (Spain). Muscular composition, biometrics and trophic levels were also determined. The mean PTE concentration levels (mg kg-1, DW) in fish muscle tissue were: As (2.90-53.74), Cd (0.01-0.18), Cr (0.53-2.01), Cu (0.78-6.93), Ni (0.06-0.24), Pb (0.0-0.32). These concentrations did not exceed the maximum limits set by European legislation (Commission Regulation (EC) No. 1881/2006) for the intake of these marine species. Accumulation of toxic elements tends to be seen in the liver (As (7.31-26.77), Cd (0.11-8.59), Cr (0.21-2.94), Cu (2.64-16.90), Ni (0.16-1.03), Pb (0.0-0.99)). As was the element at highest risk in this Mediterranean region, especially due to red mullet values in muscle. The high As contents with living habits as benthic species that feed near the coast. HMs, especially muscle Cd contents, were associated with higher contents of lipids and organic matter, and bigger specimen size (length and weight), while As was linked to higher fish protein content. However, these relationships between potentially toxic elements (PTE) and biometric indices and body composition parameters depend on species. Finally, the THQ indices indicated that eating fish from Almería Bay poses no human health risk despite pollution from the Almería coastline.

Total liver and muscle mercury, and muscular composition, biometrics and trophic levels, were determined in four species (Mullus surmuletus, Merluccius merluccius, Auxis rochei and Scomber japonicus) of the Mediterranean Sea (Almería Bay, Spain). Mercury levels did not exceed the maximum residue limit, and M. merluccius obtained the highest level in muscle. Considerable variations in Hg content among individuals were observed in non-gregarious species. A positive correlation between Hg and trophic level or length was found in muscle, but not in liver. Organs (liver or muscle) with major Hg accumulation depend on species; muscle in M. merluccius and liver in S. japonicus. The results indicate that Hg levels in fish depend on intra- and interspecies factors that should be taken into account in systems to monitor Hg levels.