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.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images. In some implementations, a method includes obtaining images of fish enclosed in a fish enclosure, identifying fish tracks shown in the images of the fish, determining a quality score for each of the fish tracks, selecting a subset of the fish tracks based on the quality scores, determining a representative weight of the fish in the fish enclosure based on weights of the fish shown in the subset of the fish tracks, and outputting the representative weight for display or storage at a device connected to the one or more processors.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that receives input image data and analyzes the input image data by one or more computing devices. The input image data includes one or more images, and the one or more computing devices analyzes the input image data that includes one or more images and analyzes the input image data using one or more computing devices to determine that the input image data is likely to induce motion sickness. In response to determining that the input image data is likely to induce motion sickness, the one or more computing devices identify one or more actions to modify presentation of the input image data on a display device.
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.
G06V 10/42 - Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
A01K 61/95 - Sorting, grading, counting or marking live aquatic animals, e.g. sex determination specially adapted for fish
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06F 18/2433 - Single-class perspective, e.g. one-against-all classificationNovelty detectionOutlier detection
In one aspect, there is provided a method that includes receiving, by a control system having (i) a first camera configured to obtain an image of a scene, (ii) a winch controller, and (iii) a feeding system configured to deliver a feed to aquaculture, instructions to initiate a calibration of the first camera, determining a calibration state of the first camera, determining a sequence of calibration steps based on the calibration state of the first camera, and executing the sequence of calibration steps to calibrate the first camera.
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 apparatus, including computer programs encoded on computer storage media, for environmental monitoring. One of the methods includes obtaining sensor data from sensors positioned within an environment; providing the sensor data to one or more machine learning models trained to predict a likelihood of a particular effect on the environment by machinery deployed within the environment; and adjusting, using output from the one or more machine learning models, one or more operations of the machinery in the environment.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life image. One of the methods includes receiving aquatic life data comprising a plurality of aquatic life images from a user through a user interface; receiving, within the user interface, a first user request to use the aquatic life data to train a machine learning model; determining a data curator score for each aquatic life image; identifying, based on the data curator scores, a proper subset of the plurality of aquatic life images; providing the proper subset of the plurality of aquatic life images to one or more data annotators; receiving annotation data generated by the one or more data annotators; and providing the annotation data to a training system configured to train the machine learning model by using the annotation data.
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life images. One of the methods includes obtaining aquatic life data; identifying a first subset of the aquatic life data that is to be annotated by one or more data annotators to generate annotated aquatic life data and a second subset of the aquatic life data that is not to be annotated; providing the first subset of the aquatic life data to the one or more data annotators, the one or more data annotators processing the first subset of the aquatic life data to generate the annotated aquatic life data; providing the second subset of the aquatic life data to a data storage curator; and determining whether and, if so, which storage device in one or more storage devices to store the second subset of the aquatic life data.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life images. One of the methods includes obtaining aquatic life data; identifying a first subset of the aquatic life data that is to be annotated by one or more data annotators to generate annotated aquatic life data and a second subset of the aquatic life data that is not to be annotated; providing the first subset of the aquatic life data to the one or more data annotators, the one or more data annotators processing the first subset of the aquatic life data to generate the annotated aquatic life data; providing the second subset of the aquatic life data to a data storage curator; and determining whether and, if so, which storage device in one or more storage devices to store the second subset of the aquatic life data.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A method for controlling a sensor subsystem, the method including receiving one or more metrics representing one or more characteristics of livestock, including one or more livestock objects, contained in an enclosure and monitored by one or more sensors coupled to a winch subsystem. The method further includes determining a position to move the one or more sensors based on the metrics and determining an instruction that includes information related to a movement of the one or more sensors. The method further includes sending the instruction to the winch subsystem to change the position of the one or more sensors.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for estimating fish biomass using a monocular camera including obtaining multiple frames captured by the monocular camera over a period of time, where at least one frame of the multiple frames includes a fish, identifying a first set of key points in a two-dimensional space for the fish in the multiple frames, receiving motion data for the monocular camera over the period of time, generating, from the first set of key points in the two-dimensional space and the motion data for the monocular camera, a fish model including a second set of key points in a three-dimensional space for the fish, generating a biomass estimate of the fish based on the second set of key points, and determining an action based on one or more biomass estimates including the biomass estimate of the fish.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that validate the synchronization of controllers in an aquaculture environment. An image processor receives images generated by a first image generating device that includes a light filter that is associated with light of a particular light frequency while an aquaculture environment was illuminated with light. The image processor determines whether the intensity value of the light frequency in the image satisfies a threshold value based on the generated image. The image processor determines whether the aquaculture environment was illuminated with light of a particular light frequency when the image was generated based on the whether the intensity value of the particular light frequency in the image exceeds the threshold value. The image processor provides for output an indication of whether the aquaculture was illuminated with light of the particular frequency when the image was generated.
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.
Methods, systems, and computer-readable media that implement an autonomous modular breakwater system. An example system includes a plurality of autonomous submersible structures, each configured to mechanically link to any other of the plurality of autonomous submersible structures to form a breakwater. The system includes a controller configured to perform operations including: determining a location for construction of a breakwater; determining an initial location of each of the plurality of autonomous submersible structures; selecting, based at least in part on the initial location of each of the plurality of autonomous submersible structures, a subset of the plurality of autonomous submersible structures for constructing the breakwater; and transmitting, to each of the selected autonomous submersible structures, instructions to transit from the respective initial location to the location for construction of the breakwater and to mechanically couple to at least one other autonomous submersible structure to form the breakwater.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for fishery net management. One of the methods includes transmitting, by an emitter (106), a signal; receiving, by each of multiple sensors (104) located at different, predetermined positions on a semi-rigid structure (102), the signal; determining, by each of the multiple sensors, timing information for the signal; determining, using the timing information for each sensor, a current shape of the semi-rigid structure; and controlling an item of equipment based on the current shape of the semi-rigid structure.
A net monitoring system, including: a plurality of net monitoring devices, each net monitoring device including: a housing; a plurality of tensioning arms, each tensioning arm reversibly extendable through the housing and configured to reversibly secure to a net, each tensioning arm including a force sensor configured to generate a tension signal indicative of a tension applied to the corresponding tensioning arm; a tensioning mechanism configured concurrently retract the plurality of tensioning arms into the housing; an impulse generating device, configured to generate an impulse responsive to a command; and a communications device configured to receive the tension signals from the plurality of force sensors, and transmit the tension signals through water; and a controller, configured to: command at least one of the plurality of net monitoring devices to generate the impulse; receive the tension signals responsive to the command to generate the impulse; and determine, based on the received tension signals, a presence of a defect in a net on which the plurality of net monitoring devices are installed.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-calibrating ultrasonic removal of sea lice. In some implementations, a method includes generating, by transducers distributed in a sea lice treatment station, a first set of ultrasonic signals, detecting a second set of ultrasonic signals in response to propagation of the first set of ultrasonic signals through water, determining propagation parameters of the sea lice treatment station based on the second set of ultrasonic signals that were detected, obtaining an image of a sea louse on a fish in the sea lice treatment station, determining, from the image, a location of the sea louse in the sea lice treatment station, and generating a third set of ultrasonic signals that focuses energy at the sea louse.
A01K 61/13 - Prevention or treatment of fish diseases
A01M 29/18 - Scaring or repelling devices, e.g. bird-scaring apparatus using sound waves using ultrasonic signals
G01N 29/00 - Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic wavesVisualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
G01N 29/34 - Generating the ultrasonic, sonic or infrasonic waves
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that provides an enhanced synchronization framework. One of the methods includes a primary and a second device that receive configuration information which identifies one or more actions to be performed by the secondary device when it receives specified pulses of a sequence of pulses from the primary device. The primary device transmits a sequence of pulses. The primary and the secondary device receive a particular pulse from the sequence of pulses. The secondary device determines whether the particular pulse satisfies one or more predetermined criteria and generates an instruction based on the determination.
A method for monitoring an aquaculture net, including: transmitting, from a sensor coupled to a net, a signal including information indicative of one or more net state parameters through water; receiving the signal from a sensor coupled to a net, the signal including information indicative of one or more net state parameters; extracting the information from the signal; applying the extracted information as input to a machine learning model trained to determine a net state from the extracted information; determining the presence of a defect in the net.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for real-time monitoring of an aquaculture feeding system using Time Domain Reflectometry (TDR). In one aspect, an aquaculture feeding system comprises: a feed pipe arranged between a dosing system and a fish pen; a feed pipe monitoring subsystem comprising one or more controlled impedance cables arranged along the feed pipe, and a pulse generator configured to generate a series of pulses at a predetermined pulse rate to be transmitted through the controlled impedance cables; and a controller subsystem connected to the feed pipe monitoring subsystem, the controller subsystem configured to: monitor pulse responses along the controlled impedance cables; and detect, from the pulse responses, a condition of the feed pipe.
A01K 63/00 - Receptacles for live fish, e.g. aquariaTerraria
G01M 3/16 - Investigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means
G01M 3/18 - Investigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for pipes, cables, or tubesInvestigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for pipe joints or sealsInvestigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for valves
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that generate from a first pair and a second pair of images of livestock that are within an enclosure and that are taken at different times using a stereoscopic camera, at least two distance distributions of the aquatic livestock within the enclosure. The distance distributions can be used to determine a measure associated with an optical property of the water within the enclosure. A signal associated with the measure can be provided.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling a remotely operated vehicle (ROV) for performing an underwater task. One apparatus includes a watertight housing; a mounting hardware that attaches the watertight housing to the ROV; one or more sensors in the watertight housing, the one or more sensors configured to generate sensor data that is associated with an underwater task; and one or more processors in the watertight housing, the one or more processors configured to: receive the sensor data from the one or more sensors; generate a navigation plan for the ROV using the sensor data; determine, using the navigation plan, control instructions configured to control the ROV to perform the underwater task; and provide the control instructions to an interface of the ROV configured to communicate with the apparatus.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for automated underwater camera system control for aquaculture systems. An underwater camera system includes (i) a line on which the underwater camera system is mounted, the line detachably affixed to a feeder that provides feed for aquatic livestock, (ii) a sensor manager, (iii) one or more sensors that are managed by the sensor manager, (iv) a line navigation controller, and (v) a first actuator for controlling a distance between the feeder and the underwater camera system. The one or more sensors obtain sensor data and the line navigation controller of the underwater camera system determines a distance to position the underwater camera system beneath the feeder to obtain additional sensor data. The line navigation controller transmits a first message to the first actuator to position the underwater camera system at the determined distance beneath the feeder.
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining drone-based water measurements. One of the methods includes receiving, by a drone management system that controls a drone that is configured to collect water samples, data from a simulator that generates simulation data associated with a body of water; receiving, by the drone management system, an indication that the drone is available to collect one or more water samples from the body of water; determining, by the drone management system and based on the data from the simulator, one or more locations associated with the body of water at which the drone is to collect one or more respective water samples; and; transmitting, by the drone management system, the one or more locations associated with the body of water to the drone.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for microplastic removal. In some implementations, a method can include controlling a camera to capture one or more images of plastic in water; providing the one or more images to a machine learning model trained to detect plastic; obtaining output from the machine learning model indicating one or more items of plastic; and controlling one or more acoustic transducers to move the one or more items of plastic.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a calibration target for ultrasonic removal of ectoparasites from fish. In some implementations, the calibration target includes a fish-shaped structure, sensors positioned at different locations of the fish-shaped structure, a processor that receives sensor values from the sensors, and a transmitter that outputs sensor data from the calibration target based on the sensor values.
Methods, systems, and apparatus, including medium-encoded computer program products, for adjusting an aquaculture camera mounting system. A current combined field of view of two or more cameras that are mounted on an adjustable camera mounting structure in an environment can be determined based upon a current configuration of the adjustable camera mounting structure. A target field of view for the two or more cameras that are mounted on the adjustable camera mounting structure can be determined. Based at least on the field of view target and the current combined field of view, an adjustment parameter for the adjustable camera mounting structure can be determined. The adjustable camera mounting structure can be adjusted according to the adjustment parameter to provide a field of view in accordance with the field of view target.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for combining a simulator and neural network for predictions, such as predictions of Harmful Algal Bloom (HAB). One of the methods includes obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region; providing the estimate to a prediction system that comprises (i) an ocean simulator and (ii) a trained network model, wherein the trained network model is trained to provide an indication of marine-life change using output from the ocean simulator; obtaining output from the prediction system, wherein the output indicates a second number of cells representative of growth of the marine-life within a second region; comparing the output to an output threshold; and in response to the output satisfying the output threshold, performing an action.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for receiving sensor data and refining a training model for microplastics detector. In some implementations, an exemplary method includes receiving microplastics detection data from a microplastics detection sensor and additional sensor data from one or more other sensors; providing the microplastics detection data and additional sensor data to a model trained to detect microplastics; receiving one or more values representing the amount of microplastics from the microplastics detection data and additional sensor data; and providing a representation of the one or more values for output of the model describing the amount of microplastics for use by one or more user devices.
G06V 10/12 - Details of acquisition arrangementsConstructional details thereof
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
In an aspect, there is provided a method that includes: obtaining a physics-based ocean simulation model that specifies multiple simulation parameters, obtaining data characterizing an ocean in a geographical region, determining initial values of the simulation parameters based on data characterizing the ocean in the geographical region, and determining adjusted values of the simulation parameters. Determining adjusted values can include performing a simulation using the initial values of the simulation parameters to determine an initial simulation output, evaluating a task-specific objective function that is based on a measure of disagreement between: (i) the initial simulation output, and (ii) a target value for the ocean in the geographical region, and determining adjusted values of the simulation parameters of the physics-based ocean simulation model.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
An underwater camera system includes a camera assembly configured to scan a seabed while submerged under water and moving in a direction of travel. A buoyant support is coupled to the camera assembly and configured to position the camera assembly under water during the moving in the direction of travel. A stabilization assembly is coupled to the camera assembly and configured for adjusting an orientation of the camera assembly relative to the direction of travel.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting features of an aquatic ecosystem. One of the methods includes generating, using ground truth data, first training input, wherein the first training input includes training labels; generating an augmented dataset from multiple data sources as second training input, wherein the augmented dataset is generated using (i) bathymetric data and (ii) simulated data based on satellite data indicating one or more coastal ecosystems; and training the machine learning model using (i) the first training input and (ii) second training input, such that the machine learning model is trained to predict biomass growth.
Methods, systems, and apparatus, including medium-encoded computer program products, for obtaining a plurality of images from at least one imaging device in an aquaculture environment and determining a statistical distribution of the livestock in the aquaculture environment from the plurality of images. Based on the statistical distribution, a location of a thermocline in the aquaculture environment can be determined. A signal indicative of the location of the thermocline can be provided to an aquaculture management device in the aquaculture environment.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for Measuring and Quantifying Biodiversity in an Environment. One of the methods includes receiving a set of images representing the marine environment; identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment; classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/58 - Extraction of image or video features relating to hyperspectral data
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for aquatic biomass estimation. One of the methods includes obtaining an image of an aquatic environment including aquatic grass; providing the image to a network model trained to construct a point cloud indicating a portion of the image that represents the aquatic grass; generating a floor model indicating a floor of the aquatic environment where the aquatic grass grows; identifying, using (i) the floor model and (ii) the point cloud indicating the aquatic grass, (i) a first subset of points in the point cloud as indicating aquatic grass and (ii) a second subset of points in the point cloud as indicating the floor of the aquatic environment; and generating, using the first subset of points in the point cloud, an indication of biomass within the aquatic environment.
Methods, systems, and apparatus, including medium-encoded computer program products, for receiving outputs from a plurality of models that are each informed by real-time data provided by one or more sensors that are present in an aquaculture environment. An input is generated for an algorithmic music composer for algorithmically composing music that reflects multiple current conditions within the aquaculture environment, based at least on the received outputs from the plurality of models. The input is provided to the algorithmic music composer to algorithmically compose the music that reflects the multiple current conditions within the aquaculture environment.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for setting modes for an underwater camera. In some implementations, a scheduler repeatedly: obtains data of a current context of an underwater camera; determines whether the current context satisfies first criteria associated with continued activation of one or more modes that are currently activated and satisfies second criteria associated with activation of one or more modes that are not currently activated; selects modes to be active based on (i) determining whether the current context satisfies the first criteria and (ii) determining whether the current context satisfies the second criteria; and activating any of the modes that are to be active and that are not currently activated, or deactivating any of the modes that are currently activated that are not included in the one or more modes that are to be activated.
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.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction. In some implementations, an exemplary method includes obtaining one or more images of a population of fish captured by an underwater camera; providing data corresponding to the one or more images to a model trained to predict biomass values; obtaining output of the trained model including a predicted biomass value indicating a future biomass of a fish within the population of fish; and determining an action based on the predicted biomass value.
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
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 a plurality of images of fish captured by a monocular underwater camera; providing the plurality of images that were captured by the monocular underwater camera to a first model trained to detect one or more fish within the plurality of images; generating one or more values for each detected fish as a set of values; generating a biomass distribution of the fish based on the set of values; and determining an action based on the biomass distribution.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for distribution-based machine learning. In some implementations, a method for distribution-based machine learning includes obtaining fish images from a camera device; generating predicted values using a machine learning model and one or more of the fish images; comparing the predicted values to distribution data representing features of multiple fish; and updating one or more parameters of the machine learning model based on the comparison.
In one aspect, there is provided a moisture detection system that includes: a moisture detection unit including: a moisture sensor configured to obtain a measurement that indicates an amount of moisture, and a Radio Frequency Identification (RFID) module coupled to the moisture sensor through multiple wires, where the RFID module includes an antenna and is configured to wirelessly transmit a telemetry message based on the measurement from the moisture sensor through the antenna and is further configured to wirelessly receive energy for powering the moisture detection unit through the antenna; and a control unit communicatively coupled to the moisture detection unit, where the control unit is configured to wirelessly receive the telemetry message from the RFID module and process the telemetry message to determine the amount of moisture measured by the moisture sensor.
G01M 3/18 - Investigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for pipes, cables, or tubesInvestigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for pipe joints or sealsInvestigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for valves
G01N 27/12 - Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body in dependence upon absorption of a fluidInvestigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body in dependence upon reaction with a fluid
H04Q 9/00 - Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a lighting controller for sea lice detection. In some implementations, fish are contained within an elliptical tank filled with water. An imaging station located on the elliptical tank is used to capture an image of a fish from which image analysis can be performed to detect sea lice or other skin features, including lesions, on the fish. Pairs of imaging assemblies coordinate pulsing light of at least a first and a second color and capturing images of the fish while the fish is illuminated by at least the first and the second color. By using the captured images with different color light, computers can detect features on the body of a fish including sea lice, skin lesions, shortened operculum or other physical deformities and skin features. Detection results can aid in mitigation techniques or be stored for analytics. For example, sea lice detection results can inform targeted treatments comprised of lasers, fluids, or mechanical devices such as a brush or suction.
Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
A01K 61/95 - Sorting, grading, counting or marking live aquatic animals, e.g. sex determination specially adapted for fish
A01K 63/02 - Receptacles specially adapted for transporting live fish
F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
G06F 18/213 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/22 - Matching criteria, e.g. proximity measures
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/40 - Extraction of image or video features
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
G06V 20/80 - Recognising image objects characterised by unique random patterns
51.
SEA LICE MITIGATION BASED ON HISTORICAL OBSERVATIONS
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sea lice mitigation. In some implementations, a method includes generating (i) a first record for a first fish indicating an extent of sea lice infestation for the first fish at a first time and (ii) a second record, different than the first record, for a second fish, different than the first fish, indicating an extent of sea lice infestation for the second fish at the first time; and training, based at least in part on the first record and the second record, a model that determines, given one or more input records for a third fish, whether the third fish is likely to be healthy at a second time subsequent to the first time.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that validate the synchronization of controllers in an aquaculture environment. One of the methods includes an image processor that receives images generated by a first image generating device that includes a light filter that is associated with light of a particular light frequency while an aquaculture environment was illuminated with light. Based on the image that was generated by the first image generating device, the image processor determines whether the intensity value of the light frequency in the image satisfies a threshold value. Based on determining whether the intensity value of the light frequency in the image satisfies the threshold value, the image processor determines whether the aquaculture environment was illuminated with light of the particular light frequency when the image was generated. The image processor provides for output an indication of whether the aquaculture was illuminated with light of the particular frequency when the image was generated.
09 - Scientific and electric apparatus and instruments
12 - Land, air and water vehicles; parts of land vehicles
42 - Scientific, technological and industrial services, research and design
Goods & Services
Winches; travel winch; power winch; winch motor. Cameras; underwater cameras; video cameras; digital cameras;
apparatus for recording, transmission, processing and
reproduction of sound, images or data; sensors for measuring
concentrations of chemicals in water; temperature sensors;
pressure sensors; pollutant sensors; digital weather station
instruments; sensors to measure water quality; a mesh
network of sensors, specifically, temperature, water
quality, and pollution sensors, that records, measures,
surveys, processes, tracks, and reproduces data, video,
images, sounds, and measurements and wirelessly transmits
the data, video, images, sounds, and measurements to a
computer; sensor and measurement apparatus, namely, computer
hardware, downloadable software, sensors, and measurement
apparatus all for monitoring, detecting, data collection,
storage, reporting, auditing, tracking and predicting
farming conditions, environmental factors, conditions
relevant to animal growth and reproduction, biodiversity
quantification, carbon emissions, carbon sequestration,
decarbonization, carbon offsetting, and the detection and
filtration of microplastics; wireless adapters used to link
computers to a telecommunications network; electronic
navigational and positioning apparatus and instruments;
downloadable software in the nature of a mobile application
for use in recording, measuring, surveying, processing,
tracking, organizing, optimizing, sorting, playing,
receiving and transmitting data, videos, images, sounds, and
measurements taken from hardware and electronic devices,
namely, software for monitoring, analyzing, and tracking
water quality for aquaculture and research purposes;
downloadable software using artificial intelligence (AI),
machine learning, deep learning, and remote sensing in the
fields of marine research, oceanographic research, carbon
emissions research, carbon sequestration research,
decarbonization research, carbon offsetting, biodiversity
quantification, microplastics research, oceanic health,
ocean sustainability, aquaculture, fish farming, and fish
handling; downloadable computer software for detecting,
analyzing, and providing reports relating to marine
research, oceanographic research, carbon emissions, carbon
sequestration, decarbonization research, carbon offsetting,
biodiversity quantification, microplastics, oceanic health,
ocean sustainability, aquaculture, fish farming, and fish
handling; downloadable computer simulation software for
modeling bodies of water and marine environments;
downloadable software for optimizing maritime ship routes;
downloadable software for use in data analytics, data
virtualization, predictive business analytics, business
intelligence, and machine learning to process and analyze
data in the fields of marine research, oceanographic
research, carbon emissions research, carbon sequestration
research, decarbonization research, carbon offsetting,
biodiversity quantification, microplastics research, oceanic
health, ocean sustainability, aquaculture, fish farming, and
fish handling. Underwater drones; remotely operated vehicle (ROV) for use
underwater for surveying, inspection, marine research,
carbon emissions research, carbon sequestration research,
decarbonization research, carbon offsetting, biodiversity
quantification, microplastics research, oceanic health,
ocean sustainability, aquaculture, fish farming, and fish
handling. Design of computer software for use in the fields of marine
research, oceanographic research, carbon emissions research,
carbon sequestration research, decarbonization research,
carbon offsetting, biodiversity quantification,
microplastics research, oceanic health, ocean
sustainability, aquaculture, fish farming, and fish
handling; scientific and technological services, namely,
research, analysis, and testing in the fields of marine
research, oceanographic research, carbon emissions research,
carbon sequestration research, decarbonization research,
carbon offsetting, biodiversity quantification,
microplastics research, oceanic health, ocean
sustainability, aquaculture, fish farming, fish handling;
design, installation, updating and maintenance of computer
software; consulting services in the field of providing
on-line, non-downloadable software and applications;
providing on-line non-downloadable cloud computing software
using artificial intelligence (AI), machine learning, deep
learning for remote sensing in the fields of marine
research, oceanographic research, carbon emissions research,
carbon sequestration research, decarbonization research,
carbon offsetting, biodiversity quantification,
microplastics research, oceanic health, ocean
sustainability, aquaculture, fish farming, and fish
handling; software as a service (SaaS) services featuring
software using artificial intelligence (AI), machine
learning, deep learning, and remote sensing in the fields of
marine research, oceanographic research, carbon emissions
research, carbon sequestration research, decarbonization
research, carbon offsetting, biodiversity quantification,
microplastics research, oceanic health, ocean
sustainability, aquaculture, fish farming, and fish
handling; platform as a service (PaaS) services featuring
software using artificial intelligence (AI), machine
learning, deep learning, and remote sensing in the fields of
marine research, oceanographic research, carbon emissions
research, carbon sequestration research, decarbonization
research, carbon offsetting, biodiversity quantification,
microplastics research, oceanic health, ocean
sustainability, aquaculture, fish farming, and fish
handling; providing on-line non-downloadable software for
optimizing maritime ship routes; software as a service
(SaaS) services featuring software for optimizing maritime
ship routes; platform as a service (PaaS) services featuring
software for optimizing maritime ship routes; providing
temporary use of online non-downloadable simulation software
for modeling bodies of water and marine environments;
software as a service (SaaS) services featuring software for
modeling bodies of water and marine environments; platform
as a service (PaaS) services featuring software for modeling
bodies of water and marine environments; providing on-line
non-downloadable software for detecting, analyzing, and
providing reports on marine research, oceanographic
research, carbon emissions, carbon sequestration,
decarbonization research, carbon offsetting, biodiversity
quantification, microplastics, oceanic health, ocean
sustainability, aquaculture, fish farming, and fish
handling; software as a service (SaaS) services featuring
software for detecting, analyzing, and providing reports on
marine research, oceanographic research, carbon emissions,
carbon sequestration, decarbonization research, carbon
offsetting, biodiversity quantification, microplastics,
oceanic health, ocean sustainability, aquaculture, fish
farming, and fish handling; platform as a service (PaaS)
services featuring software for detecting, analyzing, and
providing reports on marine research, oceanographic
research, carbon emissions, carbon sequestration,
decarbonization research, carbon offsetting, biodiversity
quantification, microplastics, oceanic health, ocean
sustainability, aquaculture, fish farming, and fish
handling; technology research in the field of marine
research, oceanographic research, carbon emissions and
sequestration research, decarbonization research, carbon
offsetting, biodiversity research, microplastics research,
oceanic health, ocean sustainability, aquaculture, fish
farming, and fish handling; technological planning and
consulting services in the fields of marine research,
oceanographic research, carbon emissions and sequestration
research, decarbonization research, carbon offsetting,
biodiversity research, microplastics research, oceanic
health, ocean sustainability, aquaculture, fish farming, and
fish handling; underwater survey services in the fields of
marine research, oceanographic research, carbon emissions
and sequestration research, decarbonization research, carbon
offsetting, biodiversity research, microplastics research,
oceanic health, sustainability, aquaculture, fish farming,
and fish handling; providing advice relating to reducing
carbon emissions, carbon sequestration, decarbonization,
carbon offsetting, biodiversity quantification, and
microplastic detection, filtration, and elimination; design
of computer-simulated models; computer modeling services;
research, development, and consultation services in the
fields of science, engineering, and technology, namely,
modeling and simulating bodies of water and marine
environments; technical support services, namely,
troubleshooting of computer software problems; computer
technical support services, namely, 24/7 service desk or
help desk services for IT infrastructure, operating systems,
database systems, and web applications; providing online
non-downloadable software for use in data analytics, data
virtualization, predictive business analytics, business
intelligence, and machine learning to process and analyze
data in the fields of marine research, oceanographic
research, carbon emissions research, carbon sequestration
research, decarbonization research, carbon offsetting,
biodiversity quantification, microplastics research, oceanic
health, ocean sustainability, aquaculture, fish farming, and
fish handling; surveying services and data analysis in
connection therewith; computer systems analysis.
54.
Image processing-based weight estimation for aquaculture
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images. In some implementations, a method includes obtaining images of fish enclosed in a fish enclosure, identifying fish tracks shown in the images of the fish, determining a quality score for each of the fish tracks, selecting a subset of the fish tracks based on the quality scores, determining a representative weight of the fish in the fish enclosure based on weights of the fish shown in the subset of the fish tracks, and outputting the representative weight for display or storage at a device connected to the one or more processors.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for obtaining initial parameters for ultrasonic transducers around a calibration target. The calibration target can include a fish-shaped structure, sensors placed at different locations of the fish-shaped structure, a processor that receives sensor values from the sensors, and a transmitter that outputs sensor data from the calibration target based on the sensor values. The calibration target can be fixed at a particular position relative to the ultrasonic transducers by a filament coupled to both the calibration target and a support structure. Sensor data can be obtained from the calibration target at the particular position relative to the ultrasonic transducers, and relative locations of the sensors can be determined. Parameters for the ultrasonic transducers around the calibration target can be adjusted based on the sensor data and the respective locations of the sensors.
09 - Scientific and electric apparatus and instruments
12 - Land, air and water vehicles; parts of land vehicles
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Winches; travel winch; power winch; winch motor.
(2) Cameras; underwater cameras; video cameras; digital cameras; apparatus for recording, transmission, processing and reproduction of sound, images or data; sensors for measuring concentrations of chemicals in water; temperature sensors; pressure sensors; pollutant sensors; digital weather station instruments; sensors to measure water quality; a mesh network of sensors, specifically, temperature, water quality, and pollution sensors, that records, measures, surveys, processes, tracks, and reproduces data, video, images, sounds, and measurements and wirelessly transmits the data, video, images, sounds, and measurements to a computer; sensor and measurement apparatus, namely, computer hardware, downloadable software, sensors, and measurement apparatus all for monitoring, detecting, data collection, storage, reporting, auditing, tracking and predicting farming conditions, environmental factors, conditions relevant to animal growth and reproduction, biodiversity quantification, carbon emissions, carbon sequestration, decarbonization, carbon offsetting, and the detection and filtration of microplastics; wireless adapters used to link computers to a telecommunications network; electronic navigational and positioning apparatus and instruments; downloadable software in the nature of a mobile application for use in recording, measuring, surveying, processing, tracking, organizing, optimizing, sorting, playing, receiving and transmitting data, videos, images, sounds, and measurements taken from hardware and electronic devices, namely, software for monitoring, analyzing, and tracking water quality for aquaculture and research purposes; downloadable software using artificial intelligence (AI), machine learning, deep learning, and remote sensing in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; downloadable computer software for detecting, analyzing, and providing reports relating to marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; downloadable computer simulation software for modeling bodies of water and marine environments; downloadable software for optimizing maritime ship routes; downloadable software for use in data analytics, data virtualization, predictive business analytics, business intelligence, and machine learning to process and analyze data in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling.
(3) Underwater drones; remotely operated vehicle (ROV) for use underwater for surveying, inspection, marine research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling. (1) Design of computer software for use in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; scientific and technological services, namely, research, analysis, and testing in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, fish handling; design, installation, updating and maintenance of computer software; consulting services in the field of providing on-line, non-downloadable software and applications; providing on-line non-downloadable cloud computing software using artificial intelligence (AI), machine learning, deep learning for remote sensing in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; software as a service (SaaS) services featuring software using artificial intelligence (AI), machine learning, deep learning, and remote sensing in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; platform as a service (PaaS) services featuring software using artificial intelligence (AI), machine learning, deep learning, and remote sensing in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; providing on-line non-downloadable software for optimizing maritime ship routes; software as a service (SaaS) services featuring software for optimizing maritime ship routes; platform as a service (PaaS) services featuring software for optimizing maritime ship routes; providing temporary use of online non-downloadable simulation software for modeling bodies of water and marine environments; software as a service (SaaS) services featuring software for modeling bodies of water and marine environments; platform as a service (PaaS) services featuring software for modeling bodies of water and marine environments; providing on-line non-downloadable software for detecting, analyzing, and providing reports on marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; software as a service (SaaS) services featuring software for detecting, analyzing, and providing reports on marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; platform as a service (PaaS) services featuring software for detecting, analyzing, and providing reports on marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; technology research in the field of marine research, oceanographic research, carbon emissions and sequestration research, decarbonization research, carbon offsetting, biodiversity research, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; technological planning and consulting services in the fields of marine research, oceanographic research, carbon emissions and sequestration research, decarbonization research, carbon offsetting, biodiversity research, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; underwater survey services in the fields of marine research, oceanographic research, carbon emissions and sequestration research, decarbonization research, carbon offsetting, biodiversity research, microplastics research, oceanic health, sustainability, aquaculture, fish farming, and fish handling; providing advice relating to reducing carbon emissions, carbon sequestration, decarbonization, carbon offsetting, biodiversity quantification, and microplastic detection, filtration, and elimination; design of computer-simulated models; computer modeling services; research, development, and consultation services in the fields of science, engineering, and technology, namely, modeling and simulating bodies of water and marine environments; technical support services, namely, troubleshooting of computer software problems; computer technical support services, namely, 24/7 service desk or help desk services for IT infrastructure, operating systems, database systems, and web applications; providing online non-downloadable software for use in data analytics, data virtualization, predictive business analytics, business intelligence, and machine learning to process and analyze data in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; surveying services and data analysis in connection therewith; computer systems analysis.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater feed movement detection. In one aspect, the method may include the actions of obtaining images captured at different time points, where the images are captured by a camera and indicate feed that has been dispersed by a feeder for aquatic livestock inside an enclosure; determining, for each image, respective locations of the feed indicated by the image; determining, from the respective locations of the feed, a respective movement of the feed over the different time points; determining, based on the respective feed movement of the feed over the different time points, water current movement within the enclosure for the aquatic livestock; and outputting an indication of the water current movement.
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for automatic object detection for underwater cameras. In some implementations, an underwater camera captures many images which are obtained by a control unit. The control unit can detect one or more contours within a captured image based on values representing pixels of the image, generate a representation of the image based on the detected contours, provide the representation to a model that is trained to classify an input image as including a net or as not including a net, and perform an action based on classifying the image as including a net.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
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.
A system for servicing watercraft includes one or more waterborne platforms. Each waterborne platform includes an electric power supply, a driving system for moving the waterborne platform in a body of water, a watercraft interfacing system configured to at least supply electric power to an electrically-powered watercraft, and a control interface configured to exchange data with a controller. The controller is configured to: receive input data, determine respective destination locations for the waterborne platforms to supply electric power to the electrically-powered watercraft, and send control data that includes data indicating the destination locations to the waterborne platforms.
Methods and systems, including computer programs encoded on computer-storage media, for controlling a system for growing seaweed are described. Some implementations of a method include forming a substrate loop inoculated with seaweed spores; arranging the substrate loop about a pulley; submerging the substrate loop to grow the seaweed; determining, using a seaweed farm controller, that the seaweed has grown to a pre-determined size; and based on the determination that the seaweed has grown to a pre-determined size: providing instructions to the pulley to feed a section of the substrate loop to a harvesting unit; providing instructions to the harvesting unit to separate the seaweed attached to the section of the substrate loop; providing instructions to a cleaning unit to clean the section of the substrate loop that is freed from seaweed; and providing instructions to a seeding unit to inoculate the cleaned section of substrate loop with seaweed spores.
A sensor positioning system, includes an actuation server for communicating with components of the sensor positioning system. The sensor positioning system additionally includes a first actuation system and a second actuation system, wherein each actuation system includes a pulley system for maneuvering an underwater sensor system. The sensor positioning system includes a dual point attachment bracket that connects through a first line to the first actuation system and connecting through a second line to the second actuation system. The underwater sensor system is affixed to the first pulley system, the second pulley system, and the dual attachment bracket through the first line and the second line.
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.
In one aspect, there is provided a marine sensor platform that includes: a line having a plurality of slots that are spaced apart on the line, a plurality of sensor nodes, each sensor node configured to couple to each of the slots on the line, and each sensor node including at least one sensor, a power source coupled to the plurality of sensor nodes and configured to supply power to the plurality of sensor nodes on the line, and a controller coupled to the power source and the plurality of sensor nodes on the line, wherein the controller is configured to identify a type of the at least one sensor included in each of the sensor nodes when each of the sensor nodes is coupled to the respective slot on the line.
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.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for environmentally sustainable aquaculture through computer vision for ectoparasite detection and medication dosing. In some implementations, actions include obtaining an image captured by an underwater camera; determining one or more fish detections and one or more ectoparasite detections based on the image; generating a filtered set of fish detections and ectoparasite detections; providing the filtered set of fish detections and ectoparasite detections to a trained model; and obtaining output of the trained model indicating an intensity of ectoparasite infection.
A01K 61/13 - Prevention or treatment of fish diseases
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
67.
AUTONOMOUS SEAGOING POWER REPLENISHMENT WATERCRAFT
Methods, systems, and computer-readable media that implement autonomous seagoing power replenishment watercraft. An example system includes a plurality of marine vessels; a plurality of watercraft, each watercraft of the plurality of watercraft including a rechargeable electrical power supply and being configured to operate in: a first mode in which the watercraft awaits an assignment to provide electrical energy to a marine vessel of the plurality of marine vessels; a second mode in which the watercraft performs operations including keeping station with an assigned marine vessel and providing electrical energy to the assigned marine vessel from the power supply; and a third mode in which the watercraft recharges the power supply from a charging station. The system includes a controller configured to perform operations comprising: transmitting, to a first watercraft, an instruction indicating an assignment of the first watercraft to provide electrical energy to a first marine vessel.
G05D 1/02 - Control of position or course in two dimensions
B63B 79/40 - Monitoring properties or operating parameters of vessels in operation for controlling the operation of vessels, e.g. monitoring their speed, routing or maintenance schedules
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that automate selection of configuration data. One of the methods includes obtaining meal configuration data relating to feeding livestock in an aquaculture environment that includes one or more feeding parameters relating to feeding farmed livestock and one or more criteria. Each of the one or more criteria can be associated with the one or more feeding parameters. Sensor data can be obtained that describes at least one property of the aquaculture environment. A selected set of feeding parameters can be selected using the obtained sensor data and the obtained feeding parameters. The selected set of feeding parameters can be provided to a feeding control subsystem.
Methods, systems, and computer-readable media that implement autonomous seagoing power replenishment watercraft. An example system includes a plurality of marine vessels; a plurality of watercraft, each watercraft of the plurality of watercraft including a rechargeable electrical power supply and being configured to operate in: a first mode in which the watercraft awaits an assignment to provide electrical energy to a marine vessel of the plurality of marine vessels; a second mode in which the watercraft performs operations including keeping station with an assigned marine vessel and providing electrical energy to the assigned marine vessel from the power supply; and a third mode in which the watercraft recharges the power supply from a charging station. The system includes a controller configured to perform operations comprising: transmitting, to a first watercraft, an instruction indicating an assignment of the first watercraft to provide electrical energy to a first marine vessel.
B60L 53/57 - Charging stations without connection to power networks
B63B 35/44 - Floating buildings, stores, drilling platforms, or workshops, e.g. carrying water-oil separating devices
H02J 7/00 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
B60L 53/16 - Connectors, e.g. plugs or sockets, specially adapted for charging electric vehicles
B63B 79/40 - Monitoring properties or operating parameters of vessels in operation for controlling the operation of vessels, e.g. monitoring their speed, routing or maintenance schedules
G05D 1/02 - Control of position or course in two dimensions
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
H02J 7/35 - Parallel operation in networks using both storage and other DC sources, e.g. providing buffering with light sensitive cells
70.
Calibration target for ultrasonic removal of ectoparasites from fish
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a calibration target for ultrasonic removal of ectoparasites from fish. In some implementations, the calibration target includes a fish-shaped structure, sensors positioned at different locations of the fish-shaped structure, a processor that receives sensor values from the sensors, and a transmitter that outputs sensor data from the calibration target based on the sensor values.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-calibrating ultrasonic removal of sea lice. In some implementations, a method includes generating, by transducers distributed in a sea lice treatment station, a first set of ultrasonic signals, detecting a second set of ultrasonic signals in response to propagation of the first set of ultrasonic signals through water, determining propagation parameters of the sea lice treatment station based on the second set of ultrasonic signals that were detected, obtaining an image of a sea louse on a fish in the sea lice treatment station, determining, from the image, a location of the sea louse in the sea lice treatment station, and generating a third set of ultrasonic signals that focuses energy at the sea louse.
G01N 29/00 - Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic wavesVisualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
Methods, systems, and computer-readable media that implement a mobile filtration system that provides sustainable, on-demand water filtration while supporting the growth and maintenance of organisms. The method includes determining an environmental parameter associated with a volume of water, determining, based on the determined environmental parameter, a control parameter for an autonomous submersible structure that includes a platform on which marine life grows, and generating, based on determining the control parameter, an instruction for the autonomous submersible structure.
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.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that generate from a first pair and a second pair of images of livestock that are within an enclosure and that are taken at different times using a stereoscopic camera, at least two distance distributions of the aquatic livestock within the enclosure. The distance distributions can be used to determine a measure associated with an optical property of the water within the enclosure. A signal associated with the measure can be provided.
In one aspect, there is provided a method that includes receiving, by a control system having (i) a first camera configured to obtain an image of a scene, (ii) a winch controller, and (iii) a feeding system configured to deliver a feed to aquaculture, instructions to initiate a calibration of the first camera, determining a calibration state of the first camera, determining a sequence of calibration steps based on the calibration state of the first camera, and executing the sequence of calibration steps to calibrate the first camera.
Methods, systems, and computer-readable media that implement an autonomous modular breakwater system. An example system includes a plurality of autonomous submersible structures, each configured to mechanically link to any other of the plurality of autonomous submersible structures to form a breakwater. The system includes a controller configured to perform operations including: determining a location for construction of a breakwater; determining an initial location of each of the plurality of autonomous submersible structures; selecting, based at least in part on the initial location of each of the plurality of autonomous submersible structures, a subset of the plurality of autonomous submersible structures for constructing the breakwater; and transmitting, to each of the selected autonomous submersible structures, instructions to transit from the respective initial location to the location for construction of the breakwater and to mechanically couple to at least one other autonomous submersible structure to form the breakwater.
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 a population of fish that are located within an enclosure; providing, to one or more detection models configured to classify fish that are depicted within the images as likely being member or as likely not being member of a type of fish, the one or images; generating, as a result of providing the one or more images to the one or more detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of the type of fish; and detecting a condition based at least on the value.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for the automated feeding of fish. In some implementations, a corresponding method may include obtaining meal configuration data including one or more parameters indicating a meal plan for feeding farmed fish; executing the meal plan based on the meal configuration data; receiving sensor data from one or more sensors during execution of the meal plan; and adjusting the execution of the meal plan based on the sensor data from the one or more sensors.
09 - Scientific and electric apparatus and instruments
12 - Land, air and water vehicles; parts of land vehicles
42 - Scientific, technological and industrial services, research and design
Goods & Services
Winches; travel winch; power winch; winch motor Cameras; underwater cameras; video cameras; digital cameras; Apparatus for recording, transmission, processing and reproduction of sound, images or data; Sensors for measuring concentrations of chemicals in water; temperature sensors; pressure sensors; pollutant sensors; digital weather station instruments; sensors to measure water quality; A mesh network of sensors, specifically, temperature, water quality, and pollution sensors, that records, measures, surveys, processes, tracks, and reproduces data, video, images, sounds, and measurements and wirelessly transmits the data, video, images, sounds, and measurements to a computer; Sensor and measurement apparatus, namely, computer hardware, downloadable software, sensors, and measurement apparatus all for monitoring, detecting, data collection, storage, reporting, auditing, tracking and predicting farming conditions, environmental factors, conditions relevant to animal growth and reproduction, biodiversity quantification, carbon emissions, carbon sequestration, decarbonization, carbon offsetting, and the detection and filtration of microplastics; Wireless adapters used to link computers to a telecommunications network; Electronic navigational and positioning apparatus and instruments; Downloadable software in the nature of a mobile application for use in recording, measuring, surveying, processing, tracking, organizing, optimizing, sorting, playing, receiving and transmitting data, videos, images, sounds, and measurements taken from hardware and electronic devices, namely, software for monitoring, analyzing, and tracking water quality for aquaculture and research purposes; downloadable software using artificial intelligence (AI), machine learning, deep learning, and remote sensing for monitoring, analyzing, and tracking water quality, environmental data, and fish health and behavior in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Downloadable computer software for detecting, analyzing, and providing reports relating to marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Downloadable computer simulation software for modeling bodies of water and marine environments; Downloadable software for optimizing maritime ship routes; Downloadable software for use in data analytics, data virtualization, predictive business analytics, business intelligence, and machine learning to process and analyze data in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling. Underwater drones; remotely operated vehicle (ROV) for use underwater for surveying, inspection, marine research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling Design of computer software for use in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Scientific and technological services, namely, research, analysis, and testing in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, fish handling; Design, installation, updating and maintenance of computer software; Consulting services in the field of providing on-line, non-downloadable software and applications; Providing on-line non-downloadable cloud computing software using artificial intelligence (AI), machine learning, deep learning for remote sensing in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Software as a service (SaaS) services featuring software using artificial intelligence (AI), machine learning, deep learning, and remote sensing for monitoring, analyzing, and tracking water quality, environmental data, and fish health and behavior in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Platform as a service (PaaS) services featuring software using artificial intelligence (AI), machine learning, deep learning, and remote sensing for monitoring, analyzing, and tracking water quality, environmental data, and fish health and behavior in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Providing on-line non-downloadable software for optimizing maritime ship routes; Software as a service (SaaS) services featuring software for optimizing maritime ship routes; Platform as a service (PaaS) services featuring software for optimizing maritime ship routes; Providing temporary use of online non-downloadable simulation software for modeling bodies of water and marine environments; Software as a service (SaaS) services featuring software for modeling bodies of water and marine environments; Platform as a service (PaaS) services featuring software for modeling bodies of water and marine environments; Providing on-line non-downloadable software for detecting, analyzing, and providing reports on marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Software as a service (SaaS) services featuring software for detecting, analyzing, and providing reports on marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Platform as a service (PaaS) services featuring software for detecting, analyzing, and providing reports on marine research, oceanographic research, carbon emissions, carbon sequestration, decarbonization research, carbon offsetting, biodiversity quantification, microplastics, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Technology research in the field of marine research, oceanographic research, carbon emissions and sequestration research, decarbonization research, carbon offsetting, biodiversity research, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Technological planning and consulting services in the fields of marine research, oceanographic research, carbon emissions and sequestration research, decarbonization research, carbon offsetting, biodiversity research, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; Underwater survey services in the fields of marine research, oceanographic research, carbon emissions and sequestration research, decarbonization research, carbon offsetting, biodiversity research, microplastics research, oceanic health, sustainability, aquaculture, fish farming, and fish handling; Providing advice relating to reducing carbon emissions, carbon sequestration, decarbonization, carbon offsetting, biodiversity quantification, and microplastic detection, filtration, and elimination; Design of computer-simulated models; Computer modeling services; Research, development, and consultation services in the fields of science, engineering, and technology, namely, modeling and simulating bodies of water and marine environments; Technical support services, namely, troubleshooting of computer software problems; Computer technical support services, namely, 24/7 service desk or help desk services for IT infrastructure, operating systems, database systems, and web applications; providing online non-downloadable software for use in data analytics, data virtualization, predictive business analytics, business intelligence, and machine learning to process and analyze data in the fields of marine research, oceanographic research, carbon emissions research, carbon sequestration research, decarbonization research, carbon offsetting, biodiversity quantification, microplastics research, oceanic health, ocean sustainability, aquaculture, fish farming, and fish handling; surveying services and data collection and analysis in connection therewith; computer systems analysis.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sensor data processing. The method may include the actions of obtaining sensor data regarding aquatic livestock over periods of time, where the sensor data is captured by at least one sensor at different depths, determining, for each of the periods of time, whether the sensor data captured at different depths during the period of time satisfy one or more evaluation criteria, generating an input data set that concatenates representations of the periods of time, providing the input data set to a machine-learning trained model, receiving, as an output from the machine-learning trained model, an indication of an action to be performed for the aquatic livestock, and initiating performance of the action for the aquatic livestock.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that control devices in an aquaculture environment. One of the methods includes determining a particular objective for a robot that is operating in an aquaculture environment and determining one or more sensed conditions that are associated with the aquaculture environment. The particular objective is provided to an anti-fish-startling model evaluation engine that is configured to output actions, for a given objective, that accomplish the given objective while reducing a startling effect on nearby fish. Based on providing the particular objective to the anti-fish-startling model evaluation engine, one or more particular actions for accomplishing the particular objective are determined. The one or more particular actions are transmitted to another device.
B63B 45/00 - Arrangements or adaptations of signalling or lighting devices
A01K 61/95 - Sorting, grading, counting or marking live aquatic animals, e.g. sex determination specially adapted for fish
B63B 79/10 - Monitoring properties or operating parameters of vessels in operation using sensors, e.g. pressure sensors, strain gauges or accelerometers
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that automatically performs actions in an aquaculture environment based on light sensed by underwater cameras. One of the methods includes obtaining images of a surface of water captured by a camera that faces upwards from a depth towards the surface of the water within an enclosure that encloses aquatic livestock. An ambient light metric is determined at the depth from the images of the surface of the water. A determination is made as to whether the camera satisfies one or more depth criteria. Based on determining that the depth of the camera satisfies the one or more depth criteria, it is determined that, based on the ambient light metric at the depth, one or more action criteria are satisfied, then initiating performance of an action to be performed for the aquatic livestock.
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G01B 11/22 - Measuring arrangements characterised by the use of optical techniques for measuring depth
G01J 1/18 - Photometry, e.g. photographic exposure meter by comparison with reference light or electric value using electric radiation detectors using comparison with a reference electric value
G01J 1/42 - Photometry, e.g. photographic exposure meter using electric radiation detectors
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that validate the synchronization of controllers in an aquaculture environment. One of the methods includes an image processor that receives images generated by a first image generating device that includes a light filter that is associated with light of a particular light frequency while an aquaculture environment was illuminated with light. Based on the image, the image processor determines whether the intensity value of the light frequency in the image satisfies a threshold value. Based on determining whether the intensity value of the light frequency in the image satisfies the threshold value, the image processor determines whether the aquaculture environment was illuminated with light of the particular light frequency when the image was generated. The image processor provides an indication of whether the aquaculture was illuminated with light of the particular frequency when the image was generated.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for controlling an aquaculture feeding system are described. In some implementations, a method includes determining, using a feeding system controller, that a feeding system has transitioned from a first state to a second state, and based on the transition from the first to the second state providing, using the feeding system controller, instructions to a dosing system to set a feed rate of the dosing system, and providing, using the feeding system controller, instructions to a blower operatively coupled to the dosing system to set a flow rate of the blower.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that provides an enhanced synchronization framework. One of the methods includes a primary and a second device that receive configuration information which identifies one or more actions to be performed by the secondary device when it receives specified pulses of a sequence of pulses from the primary device. The primary device transmits a sequence of pulses. The primary and the secondary device receive a particular pulse from the sequence of pulses. The secondary device determines whether the particular pulse satisfies one or more predetermined criteria and generates an instruction based on the determination.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that provides an enhanced synchronization framework. One of the methods includes a primary and a second device that receive configuration information which identifies one or more actions to be performed by the secondary device when it receives specified pulses of a sequence of pulses from the primary device. The primary device transmits a sequence of pulses. The primary and the secondary device receive a particular pulse from the sequence of pulses. The secondary device determines whether the particular pulse satisfies one or more predetermined criteria and generates an instruction based on the determination.
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.
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.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05D 1/02 - Control of position or course in two dimensions
A01D 44/00 - Harvesting of underwater plants, e.g. harvesting of seaweed
90.
COOLING METHOD, A METHOD OF MANUFACTURING A SEMICONDUCTOR DEVICE AND A NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM
A cooling method is a method of cooling a processed substrate in a state of being held by a substrate holder, the method including: a first cooling step of cooling the substrate by supplying a gas toward the substrate holder disposed at a reference position; a stopping step of stopping supply of the gas; and a second cooling step of cooling the processed substrate held in a lower portion of the substrate holder.
H01L 21/67 - Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereofApparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components
H01L 21/673 - Apparatus specially adapted for handling semiconductor or electric solid state devices during manufacture or treatment thereofApparatus specially adapted for handling wafers during manufacture or treatment of semiconductor or electric solid state devices or components using specially adapted carriers
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.
A sensor positioning system, includes an actuation server for communicating with components of the sensor positioning system. The sensor positioning system additionally includes a first actuation system and a second actuation system, wherein each actuation system includes a pulley system for maneuvering an underwater sensor system. The sensor positioning system includes a dual point attachment bracket that connects through a first line to the first actuation system and connecting through a second line to the second actuation system. The underwater sensor system is affixed to the first pulley system, the second pulley system, and the dual attachment bracket through the first line and the second line.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a lighting controller for sea lice detection. In some implementations, a pulse of red light and a pulse of blue light can be timed with the exposure of a camera to capture multiple images of a fish or group of fishes in both red and blue light. By using the captured images with different color light, computers can detect features on the body of a fish including sea lice, skin lesions, shortened operculum or other physical deformities and skin features. Detection results can aid in mitigation techniques or be stored for analytics. For example, sea lice detection results can inform targeted treatments comprised of lasers, fluids, or mechanical devices such as a brush or suction.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-calibrating ultrasonic removal of sea lice. In some implementations, a method includes generating, by transducers distributed in a sea lice treatment station, a first set of ultrasonic signals, detecting a second set of ultrasonic signals in response to propagation of the first set of ultrasonic signals through water, determining propagation parameters of the sea lice treatment station based on the second set of ultrasonic signals that were detected, obtaining an image of a sea louse on a fish in the sea lice treatment station, determining, from the image, a location of the sea louse in the sea lice treatment station, and generating a third set of ultrasonic signals that focuses energy at the sea louse.
G01N 29/00 - Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic wavesVisualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
G01N 29/34 - Generating the ultrasonic, sonic or infrasonic waves
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.
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.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images. In some implementations, a method includes obtaining images of fish enclosed in a fish enclosure, identifying fish tracks shown in the images of the fish, determining a quality score for each of the fish tracks, selecting a subset of the fish tracks based on the quality scores, determining a representative weight of the fish in the fish enclosure based on weights of the fish shown in the subset of the fish tracks, and outputting the representative weight for display or storage at a device connected to the one or more processors.
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.
