Techniques and systems for robotic aquaculture are described. In one embodiment, for example, a mariculture system may include an aquatic animal containment system operative to hold a population of aquatic animals, the aquatic animal containment system comprising an enclosed hull having a receptacle configured to receive a mechanical core, the mechanical core configured to store at least one sub-system to implement at least one function of mariculture system, and a position management system operative to maintain the enclosed hull at a depth below a surface of a body of water. Other embodiments are described.

Techniques and systems for robotic aquaculture are described. In one embodiment, for example, a mariculture system may include an aquatic animal containment system operative to hold a population of aquatic animals, the aquatic animal containment system comprising an enclosed hull having a receptacle configured to receive a mechanical core, the mechanical core configured to store at least one sub-system to implement at least one function of mariculture system, and a position management system operative to maintain the enclosed hull at a depth below a surface of a body of water. Other embodiments are described.

In the present invention, an estimator for estimating the number of fish present in an underwater space is constructed by means of machine learning using, as teaching data, a plurality of data sets for learning that each include an echo image for learning, the echo image being based on received sound waves reflected by fish when sound waves are transmitted in an underwater space where fish are present, and the number of fish present in the underwater space in the echo image. The plurality of data sets for learning each include: an echo image for learning that is based on received sound waves reflected by fish when sound waves are transmitted in the underwater space where fish are present; and the number of fish present in the underwater space in the echo image. The estimator is used on an echo image generated on the basis of received sound waves reflected by an unknown number of fish present in the underwater space after transmitting sound waves in the underwater space, so as to calculate the number of said unknown number of fish present in the underwater space.

In the present invention, an estimator for estimating the number of fish present in an underwater space is constructed by means of machine learning using, as teaching data, a plurality of data sets for learning that each include an echo image for learning, the echo image being based on received sound waves reflected by fish when sound waves are transmitted in an underwater space where fish are present, and the number of fish present in the underwater space in the echo image. The plurality of data sets for learning each include: an echo image for learning that is based on received sound waves reflected by fish when sound waves are transmitted in the underwater space where fish are present; and the number of fish present in the underwater space in the echo image. The estimator is used on an echo image generated on the basis of received sound waves reflected by an unknown number of fish present in the underwater space after transmitting sound waves in the underwater space, so as to calculate the number of said unknown number of fish present in the underwater space.

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.

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 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.

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.

In some embodiments, an underwater camera system for monitoring fish in a fish pen is provided. In some embodiments, a stereoscopic camera may be used to determine dimensions of fish, and the underwater camera system may use the fish dimensions to estimate biomass of fish within the fish pen. The biomass value and rates of change of the biomass value may be used to adjust feeding of the fish in the fish pen. In some embodiments, images captured by the stereoscopic camera may be used to focus a variable focal lens camera on a fish to obtain high-resolution images that can be used for diagnosing fish conditions. In some embodiments, images captured by a camera may be used to predict motion of the fish, and the predicted motion may be used to generate various fish monitoring analytic values. The fish monitoring analytic values may be used to automatically control operation of the fish pen.

The present disclosure proposes an improved apparatus and method for counting of sea lice by providing a stable and controlled light environment which ensures counting of sea lice reliably and independent of weather conditions and an optimized spectral power distribution and intensity of the light for improved observation (detectability) of sea lice with respect to fish skin. An embodiment of the disclosed light system comprises multiple LEDs, at least two LEDs providing a light colour with peaks in the range 490-540 nm (Cyan/Green) respectively 620-660 nm (Red).