DEVELOPMENT OF INTELLIGENT AUTOMATED SYSTEMS FOR AQUACULTURE FEEDING (Q3145308): Difference between revisions

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(‎Created claim: summary (P836): The project proposal is completely innovative. The aim is to develop an automatic larval feeding system for microdietas (75-500µ) that allows a comprehensive management of feed supply to larvae. The system shall be fully programmable and linked to the monitoring of crop tank parameters. It is also sought for the system to be able to learn, so that it can generate a continuous improvement of the feeding and weaning protocols, and also ensure that...)
(‎Changed label, description and/or aliases in en: translated_label)
label / enlabel / en
 
DEVELOPMENT OF INTELLIGENT AUTOMATED SYSTEMS FOR AQUACULTURE FEEDING

Revision as of 13:37, 12 October 2021

Project Q3145308 in Spain
Language Label Description Also known as
English
DEVELOPMENT OF INTELLIGENT AUTOMATED SYSTEMS FOR AQUACULTURE FEEDING
Project Q3145308 in Spain

    Statements

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    355,104.0 Euro
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    443,880.0 Euro
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    80.0 percent
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    3 July 2015
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    31 December 2017
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    ENGRANOR SL
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    42°9'42.77"N, 8°37'12.97"W
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    36039
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    La propuesta de proyecto es totalmente innovadora. Se pretende desarrollar un Sistema Automático de Alimentación Larvaria para Microdietas (75-500µ) que permita una gestión integral del suministro de pienso a las larvas. El sistema será totalmente programable, y estará vinculado a la monitorización de parámetros del tanque de cultivo. Se busca además que el sistema sea capaz de aprender, de forma que pueda generar una mejora continua de los protocolos de alimentación y destete, y garantice además que sean estables y replicables. Se trata de dotar al sistema de capacidad para tomar decisiones en base a la interpretación de los parámetros de cultivo y la experiencia acumulada._x000D_ _x000D_ En el ámbito de la acuicultura, se ha demostrado hasta la fecha que el ruido de fondo así como el ruido durante el proceso de alimentación es totalmente diferenciable debido a la suma del comportamiento de agitación de los peces con la propia deglución del alimento. Esto genera una huella sonora que permite identificar a cada especie concreta, y por tratarse de un comportamiento estereotipado, permite generar un patrón, un modelo interpretativo. Mediante el desarrollo de algoritmos matemáticos se puede analizar el sonido captado mediante hidrófonos, para filtrar el ruido de fondo y aislar e identificar una huella sonora específica. Una vez generado este algoritmo, se puede definir con exactitud en qué momento el pez se está alimentando, que grado de actividad o voracidad manifiesta y cuándo se ha alcanzado la saciedad (Spanish)
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    The project proposal is completely innovative. The aim is to develop an automatic larval feeding system for microdietas (75-500µ) that allows a comprehensive management of feed supply to larvae. The system shall be fully programmable and linked to the monitoring of crop tank parameters. It is also sought for the system to be able to learn, so that it can generate a continuous improvement of the feeding and weaning protocols, and also ensure that they are stable and replicable. The aim is to provide the system with the ability to make decisions based on the interpretation of the crop parameters and the accumulated experience._x000D_ _x000D_ In the field of aquaculture, it has been demonstrated to date that background noise as well as noise during the feeding process is totally different from the sum of the agitation behavior of the fish with the food’s own swallowing. This generates a sound footprint that allows to identify each specific species, and because it is a stereotyped behavior, allows to generate a pattern, an interpretative model. By developing mathematical algorithms, the sound captured by hydrophones can be analysed to filter background noise and isolate and identify a specific sound footprint. Once this algorithm is generated, it can be defined exactly when the fish is feeding, what degree of activity or manifest voracity and when satiety has been reached (English)
    12 October 2021
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    Porriño, O
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    Identifiers

    ITC-20151187-11
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