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.

A system for managing and monitoring a sensor network that senses pest activity. A plurality of sensor stations may be controlled by the system, such sensor station configured for detecting the presence of pests at a baiting station, or for alerting users to the presence of a pest in a trap, as well as a related system of data collection and data management of pest activity data.

A system for managing and monitoring a sensor network that senses pest activity. A plurality of sensor stations may be controlled by the system, such sensor station configured for detecting the presence of pests at a baiting station, or for alerting users to the presence of a pest in a trap, as well as a related system of data collection and data management of pest activity data.

An information processing method implemented by a computer. The information processing method includes the step of acquiring an internal parameter value regarding a biological characteristic of fish, an external parameter value regarding a surrounding environment characteristic of the fish, and a shoaling parameter value regarding a shoaling characteristic that is a behavior of one fish with respect to other fish, and the step of generating a simulation image including a behavior of each fish in a fish school based on the internal parameter value, the external parameter value, and the shoaling parameter value that are acquired in the step of acquiring.

An information processing method implemented by a computer. The information processing method includes the step of acquiring an internal parameter value regarding a biological characteristic of fish, an external parameter value regarding a surrounding environment characteristic of the fish, and a shoaling parameter value regarding a shoaling characteristic that is a behavior of one fish with respect to other fish, and the step of generating a simulation image including a behavior of each fish in a fish school based on the internal parameter value, the external parameter value, and the shoaling parameter value that are acquired in the step of acquiring.

Systems and methods for monitoring organisms within an aquatic environment are described. According to one aspect, an injectable acoustic transmission device includes a body configured to be injected inside of an organism, a transducer within the body and configured to convert a plurality of electrical signals into a plurality of data transmissions which are transmitted externally of the body and the organism, a plurality of circuit components within the body and configured to use electrical energy from a power source to generate the electrical signals which are provided to the transducer, and wherein the transducer defines an internal volume and at least one of the circuit components is provided within the internal volume of the transducer.

Systems and methods for monitoring organisms within an aquatic environment are described. According to one aspect, an injectable acoustic transmission device includes a body configured to be injected inside of an organism, a transducer within the body and configured to convert a plurality of electrical signals into a plurality of data transmissions which are transmitted externally of the body and the organism, a plurality of circuit components within the body and configured to use electrical energy from a power source to generate the electrical signals which are provided to the transducer, and wherein the transducer defines an internal volume and at least one of the circuit components is provided within the internal volume of the transducer.

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.

Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, mass, and health of fish are described. A pair of stereo cameras may be utilized to obtain off-axis images of fish in a defined area. The 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 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 fish model to determine characteristics of the fish.