Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining an image of a fish captured by a monocular underwater camera; providing the image of the fish to a depth perception model; obtaining output of the depth perception model indicating a depth-enhanced image of the fish data in the image; determining a biomass value estimate of the fish based on the output; and determining an action based on one or more biomass values estimates including the biomass value estimate of the fish.

Abstract: 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.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for estimating wave properties of a body of water. A computer-implemented system obtains measurement data for a duration of time from an inertial measurement unit (IMU) onboard an underwater device, generates model input data based on at least the measurement data obtained at the plurality of time points, and processes the model input data to generate model output data indicating one or more wave properties using a machine-learning model. The system further determines, based on at least the one or more wave properties, whether the device is safe to be deployed.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for automated camera positioning for feeding behavior monitoring. In some implementations, a system obtains an image of a scene, a spatial model that corresponds to a subfeeder, and calibration parameters of a camera, the system determines a size of the subfeeder in the image of the scene, the system selects an updated position of the camera relative to the subfeeder, the system provides the updated position of the camera relative to the subfeeder to a winch controller, and the system moves the camera to the updated position.

Abstract: Methods, systems, and computer-readable media that implement an autonomous or semi-autonomous growth platform used to control live cargo exposures to environmental parameters by changing depth in an offshore environment. For example, the growth platform can be lowered at night so that farmed seaweed can perform luxury uptake of nutrients and raised during the daytime so that the farmed seaweed can capture sunlight.