Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.