Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sea lice mitigation. In some implementations, a method includes obtaining multiple observations of a population of reference fish across a period of time, generating, from the multiple observations, a record for each reference fish that indicates an extent of sea lice infestation for the reference fish across the period of time, training, based on the records, a model that determines a predicted health indicator for a fish, obtaining an image of a sample fish that is not in the population of reference fish, determining, based on the image of the sample fish and with the model, a predicted health indicator for the sample fish, and selectively initiating sea lice mitigation based on the predicted health indicator.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sea lice mitigation. In some implementations, a method includes obtaining multiple observations of a population of reference fish across a period of time, generating, from the multiple observations, a record for each reference fish that indicates an extent of sea lice infestation for the reference fish across the period of time, training, based on the records, a model that determines a predicted health indicator for a fish, obtaining an image of a sample fish that is not in the population of reference fish, determining, based on the image of the sample fish and with the model, a predicted health indicator for the sample fish, and selectively initiating sea lice mitigation based on the predicted health indicator.

Embodiments of the present invention provide a novel deterrent barrier based on the phenomenon of fluid cavitation. A drive unit comprising a motor and a propeller are configured for inducing cavitation in water. The cavitation takes the form of a rotationally confined vertical column of cavitation bubbles extending from the propeller, and a one-dimensional series of drive units spanning the width of a waterway may provide an effective, environmentally friendly and non-lethal barrier against entry of target fish species.

Embodiments of the present invention provide a novel deterrent barrier based on the phenomenon of fluid cavitation. A drive unit comprising a motor and a propeller are configured for inducing cavitation in water. The cavitation takes the form of a rotationally confined vertical column of cavitation bubbles extending from the propeller, and a one-dimensional series of drive units spanning the width of a waterway may provide an effective, environmentally friendly and non-lethal barrier against entry of target fish species.

A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.

A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.

Described herein are polymorphisms, and in particular single nucleotide polymorphisms (SNP) associated with late onset of sexual maturation in rainbow trout (Oncorhynchus mykiss). In particular, provided are methods for predicting late onset of sexual maturation in rainbow trout, methods for selecting a rainbow trout having late onset of sexual maturation and kit suitable for carrying out said methods. Further provided are rainbow trout cells, sperm and unfertilized eggs carrying at least one allele conferring late onset of sexual maturation in their genome.

Described herein are polymorphisms, and in particular single nucleotide polymorphisms (SNP) associated with late onset of sexual maturation in rainbow trout (Oncorhynchus mykiss). In particular, provided are methods for predicting late onset of sexual maturation in rainbow trout, methods for selecting a rainbow trout having late onset of sexual maturation and kit suitable for carrying out said methods. Further provided are rainbow trout cells, sperm and unfertilized eggs carrying at least one allele conferring late onset of sexual maturation in their genome.