Abstract: 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 an enclosure; generating, as a result of providing the one or more images to multiple detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of each different type of fish; detecting an error condition relating to a possible opening of the enclosure based at least on the value; and in response to detecting the error condition relating to the possible opening of the enclosure, initiating one or more mitigation actions relating to the possible opening.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for visually detecting a halocline. In some implementations, a method includes moving a camera through different depths of water within a fish enclosure, capturing, at the different depths, images of fish, determining that changes in focus in the images correspond to changes in depth that the images were captured, and based on determining that the changes in focus in the images correspond to the changes in depths that the images were captured, detecting a halocline at a particular depth.

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 from one or more cameras and a spatial model that corresponds to a subfeeder. The system determines, based on data from the spatial model and the image of the scene, a current position of the one or more cameras relative to the subfeeder. The system selects an updated position of the one or more cameras relative to the subfeeder. The system moves the one or more cameras to the updated position. In some implementations, the system dynamically selects the updated position of the one or more cameras relative to the subfeeder.

Abstract: A digital assistant executing at, at least one processor, is described that is configured to determine a set of candidate third party agents. The digital assistant is further configured to receive, from a computing device that is associated with a user, information indicative of one or more interests of the user and determine based on the information, a set of relevance scores. The digital assistant is further configured to select one or more candidate third party agents from the set of candidate third party agents that have a respective relevance score that satisfies a threshold. Responsive to receiving an indication of user input that accepts a recommendation to configure the user account with the one or more candidate third party agents, the digital assistant is further configured to configure the user account for operation with the one or more candidate third party agents.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sorting fish in aquaculture. In some implementations, one or more images are obtained of a particular fish within a population of fish. Based on the one or more images of the fish, a data element is determined. The data element can include a first value that reflects a physical characteristic of the particular fish, and a second value that reflects a condition factor of the particular fish. Based on the data element, the fish is classified as a member of a particular subpopulation of the population of fish. An actuator of an automated fish sorter is controlled based on classifying the particular fish as a member of the particular subpopulation of the population of fish.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for controlling a camera to observe aquaculture feeding behavior. In some implementations, a method includes moving a camera to a first position, obtaining an image captured by the camera at the first position, determining a feeding observation mode, and based on the feeding observation mode and analysis of the image, determining a second position to move the camera.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computerized travel services. One of the methods includes identifying photographs using an index of photographs, the photographs being identified from the index as photographs geographically related to a point of interest or destination and having a creation timestamp corresponding to a time of the year; determining for each of the photographs, a relevancy score based at least in part on: selection success data of the photograph for image queries referring to the point of interest or destination, and references to the point of interest or destination in documents associated with the photograph; and selecting a selected photograph from the photographs based at least in part on a respective visual quality score and the respective relevancy scores, the visual quality score representing a degree of visual quality of the respective photographs.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.

Abstract: A digital assistant executing at, at least one processor, is described that is configured to determine a set of candidate third party agents. The digital assistant is further configured to receive, from a computing device that is associated with a user, information indicative of one or more interests of the user and determine based on the information, a set of relevance scores. The digital assistant is further configured to select one or more candidate third party agents from the set of candidate third party agents that have a respective relevance score that satisfies a threshold. Responsive to receiving an indication of user input that accepts a recommendation to configure the user account with the one or more candidate third party agents, the digital assistant is further configured to configure the user account for operation with the one or more candidate third party agents.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.

Abstract: 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.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sorting fish in aquaculture. In some implementations, one or more images are obtained of a particular fish within a population of fish. Based on the one or more images of the fish, a data element is determined. The data element can include a first value that reflects a physical characteristic of the particular fish, and a second value that reflects a runt factor of the particular fish. Based on the data element, the fish is classified as a member of a particular subpopulation of the population of fish. An actuator of an automated fish sorter is controlled based on classifying the particular fish as a member of the particular subpopulation of the population of fish.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sorting fish in aquaculture. In some implementations, one or more images are obtained of a particular fish within a population of fish. Based on the one or more images of the fish, a data element is determined. The data element can include a first value that reflects a physical characteristic of the particular fish, and a second value that reflects a condition factor of the particular fish. Based on the data element, the fish is classified as a member of a particular subpopulation of the population of fish. An actuator of an automated fish sorter is controlled based on classifying the particular fish as a member of the particular subpopulation of the population of fish.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for controlling a camera to observe aquaculture feeding behavior. In some implementations, a method includes moving a camera to a first position, obtaining an image captured by the camera at the first position, determining a feeding observation mode, and based on the feeding observation mode and analysis of the image, determining a second position to move the camera.

Abstract: 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.