Generating consensus biomass estimates include providing a first biomass parameter data set associated with a first biomass attribute parameter to a first biomass estimation model and providing a second biomass parameter data set associated with a second biomass attribute parameter to a second biomass estimation model different from the first biomass estimation model. The first biomass estimation model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second biomass estimation model. An aggregated biomass estimate is determined based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.
A method of monocular depth estimation includes receiving a plurality of monocular images corresponding to images of fish within a marine enclosure and further receiving acoustic data synchronized in time relative to the plurality of images. The plurality of images and the acoustic data are provided to a convolutional neural network (CNN) for training a monocular depth model. The monocular depth model is trained to generate, based on the received plurality of monocular images and the acoustic data, a distance-from-feeder estimate of a vertical biomass center of fish within the marine enclosure.
A method of surface splash scoring includes receiving, at an electronic device, a set of camera frames corresponding to images of a water surface. The electronic device processes the set of camera frames with a trained machine learning model to generate one or more quantifications associated with fish activity proximate the water surface. In some embodiments, a surface splash score is computed that represents an appetite level anticipated to be exhibited for a first time period. Subsequently, the electronic device generates an output indicative of the surface splash score.
A method of surface splash scoring includes receiving, at an electronic device, a set of camera frames corresponding to images of a water surface. The electronic device processes the set of camera frames with a trained machine learning model to generate one or more quantifications associated with fish activity proximate the water surface. In some embodiments, a surface splash score is computed that represents an appetite level anticipated to be exhibited for a first time period. Subsequently, the electronic device generates an output indicative of the surface splash score.
A method of dynamically reconfiguring sensor system operating parameter by receiving, at an electronic device, data indicative of one or more underwater object parameters corresponding to one or more underwater objects within a marine enclosure. A set of intrinsic operating parameters for a sensor system at a position within the marine enclosure is determined based at least in part on the data indicative of one or more underwater object parameters. The sensor system is configured according to the determined set of intrinsic operating parameters by changing at least one intrinsic operating parameter of the sensor system in response to the data indicative of one or more underwater object parameters.
A method of dynamically reconfiguring laser system operating parameter by receiving, at an electronic device, data indicative of one or more underwater object parameters corresponding to one or more underwater objects within a marine enclosure. A set of intrinsic operating parameters for a laser system at a position within the marine enclosure is determined based at least in part on the data indicative of one or more underwater object parameters. The laser system is configured according to the determined set of intrinsic operating parameters by changing at least one intrinsic operating parameter of the laser system in response to the data indicative of one or more underwater object parameters.
Generating consensus biomass estimates include providing a first biomass parameter data set associated with a first biomass attribute parameter to a first biomass estimation model and providing a second biomass parameter data set associated with a second biomass attribute parameter to a second biomass estimation model different from the first biomass estimation model. The first biomass estimation model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second biomass estimation model. An aggregated biomass estimate is determined based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.
Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system.
Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for fish farm material handling are described. Fish farm material includes samples collected from broodstock individuals and transported for suspension in cold storage. During the time window of cold storage, a metric of interest corresponding to the fish farm material is identified. Based on the metric of interest identification, material handling may be modified to, for example, segregate fish farm material into quarantined incubation units to inhibit infection of fish farm material within other incubation units.
Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system.
Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system.
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