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Título
Artificial Neural Network (ANN) as a tool to reduce human-animal interaction improves Senegalese sole production
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Biomolecules
Número de la revista
12
Cita Bibliográfica
Martínez-Vázquez JM, Valcarce DG, Riesco MF, Marco VS, Matsuoka M, Robles V. (2019). Artificial Neural Network (ANN) as a Tool to Reduce Human-Animal Interaction Improves Senegalese Sole Production. Biomolecules. 9(12):778. https://doi.org/10.3390/biom9120778
Editorial
MDPI
Fecha
2019
Resumen
[EN] Manipulation is usually required for biomass calculation and food estimation for optimal fish growth in production facilities. However, the advances in computer-based systems have opened a new range of applied possibilities. In this study we used image analysis and a neural network algorithm that allowed us to successfully provide highly accurate biomass data. This developed system allowed us to compare the effects of reduced levels of human-animal interaction on the culture of adult Senegalese sole (Solea senegalensis) in terms of body weight gain. For this purpose, 30 adult fish were split into two homogeneous groups formed by three replicates (n = 5) each: a control group (CTRL), which was standard manipulated and an experimental group (EXP), which was maintained under a lower human-animal interaction culture using our system for biomass calculation. Visible implant elastomer was, for the first time, applied as tagging technology for tracking soles during the experiment (four months). The experimental group achieved a statistically significant weight gain (p < 0.0100) while CTRL animals did not report a statistical before-after weight increase. Individual body weight increment was lower (p < 0.0100) in standard-handled animals. In conclusion, our experimental approach provides evidence that our developed system for biomass calculation, which implies lower human-animal interaction, improves biomass gain in Senegalese sole individuals in a short period of time.
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/MINECO/ Programa Estatal de I+D+I Orientada a los Retos de la Sociedad/ AGL2015-68330-C2-1-R/ES/ MECANISMOS MOLECULARES SUBYACENTES AL FALLO REPRODUCTIVO EN SOLEA SENEGALENSIS: DESARROLLO DE NUEVAS ESTRATEGIAS Y TRATAMIENTOS PARA SOLVENTAR LA DISFUNCION REPRODUTIVA EN F1
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