An estimation program according to the application concerned causes a computer to execute an obtaining step and an estimating step. The obtaining step includes obtaining behavior information which indicates the behavior exhibited by a specific fish species having a predetermined physical abnormality. The estimating step includes estimating, based on the behavior information obtained at the obtaining step and based on state information indicating the state of the target fish for processing, behavioral features of the target fish for processing that are attributable to the predetermined physical abnormality seen in the target fish for processing.

An estimation program according to the application concerned causes a computer to execute an obtaining step and an estimating step. The obtaining step includes obtaining behavior information which indicates the behavior exhibited by a specific fish species having a predetermined physical abnormality. The estimating step includes estimating, based on the behavior information obtained at the obtaining step and based on state information indicating the state of the target fish for processing, behavioral features of the target fish for processing that are attributable to the predetermined physical abnormality seen in the target fish for processing.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

The present disclosure proposes an improved apparatus and method for counting of sea lice by providing a stable and controlled light environment which ensures counting of sea lice reliably and independent of weather conditions and an optimized spectral power distribution and intensity of the light for improved observation (detectability) of sea lice with respect to fish skin. An embodiment of the disclosed light system comprises multiple LEDs, at least two LEDs providing a light colour with peaks in the range 490-540 nm (Cyan/Green) respectively 620-660 nm (Red).

The present disclosure proposes an improved apparatus and method for counting of sea lice by providing a stable and controlled light environment which ensures counting of sea lice reliably and independent of weather conditions and an optimized spectral power distribution and intensity of the light for improved observation (detectability) of sea lice with respect to fish skin. An embodiment of the disclosed light system comprises multiple LEDs, at least two LEDs providing a light colour with peaks in the range 490-540 nm (Cyan/Green) respectively 620-660 nm (Red).

A precast dam structure includes at least two precast segments coupled together via linkages and a flow path structure. The flow path structure defines a flow path having an intake port and a draft port and is associated with at least one of the at least two precast segments. The flow path structure is configured to provide a change in flow direction, either internally or externally, from the at least one of the at least two precast segments.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for escape detection and mitigation for aquaculture. In some implementations, a method includes obtaining one or more images that depict one or more fish within a population of fish that are located within an enclosure; providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images; generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and detecting a condition based at least on the value.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for escape detection and mitigation for aquaculture. In some implementations, a method includes obtaining one or more images that depict one or more fish within a population of fish that are located within an enclosure; providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images; generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and detecting a condition based at least on the value.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images. In some implementations, a method includes obtaining images of fish enclosed in a fish enclosure, identifying fish tracks shown in the images of the fish, determining a quality score for each of the fish tracks, selecting a subset of the fish tracks based on the quality scores, determining a representative weight of the fish in the fish enclosure based on weights of the fish shown in the subset of the fish tracks, and outputting the representative weight for display or storage at a device connected to the one or more processors.