A computerized system of performing fish census which otherwise requires high level of domain knowledge and expertise is described. Divers with minimal knowledge of fish can obtain high quality population and species distribution measurements using a stereo camera rig and fish video analyzer software that was developed. The system has two major components: a camera rig and software for fish size, density and biomass estimation. The camera rig consists of a simple stand on which one to four pairs of stereo cameras are mounted to take videos of the benthic floor for a few minutes. The collected videos are uploaded to a server which performs stereo analysis and image recognition. The software produces video clips containing estimates of fish size, density and species biodiversity and a log report containing information about the individual fishes for further end user analysis.

An automatic observing net is provided, and applied in croppers, creatures and cultivation via a feeding and observing equipment, and characterized in comprising a monitoring and identifying system; a feeding equipment control system, connected to the monitoring and identifying system, and including a monitoring data process center, a feeding operation platform, a feeding control center; and a feeding equipment execution system, connected to the feeding equipment control system. A utilizing method of an automatic observing net is provided, and characterized in: the automatic observing net being provided to allow a plurality of feeding and observing equipment to be corresponding to a plurality of cultivating farms for monitoring the cultivation at the same time so to as connect and communicate the data, wherein with a common platform interface, a remote control operation being achieved, the operator only requires to go to the farm when the platform interface sends a requirement.

Described herein are systems and methods of converting media dimensions. A device may identify a set of frames from a video in a first orientation as belonging to a scene. The device may receive a selected coordinate on a frame of the set of frames for the scene. The device may identify a first region within the frame including a first feature corresponding to the selected coordinate and a second region within the frame including a second feature. The device may generate a first score for the first feature and a second score for the second feature. The first score may be greater than the second score based on the first feature corresponding to the selected coordinate. The device may crop the frame to include the first region and the second region within a predetermined display area comprising a subset of regions of the frame in a second orientation.

A treatment method for a river system in a reservoir area, comprising: S1. determining whether a time from a current date to the rainy season is less than a preset duration; S2. moving a pressure sensor upward; S3. determining whether the pressure data meets corresponding conditions; S4. determining whether a duration of the pressure data is less than the preset duration; S5. determining whether an interval between the current time and the time for collecting pressure/nitrogen and phosphorus is greater than a preset number of days; S6. acquiring an image information of a river bottom, and sending it to neural network model for identification to obtain a depth of a sludge; S7. determining whether the depth of a sludge has reached a dredging depth, if so, starting a sludge pump to clean up; S8. collecting nitrogen and phosphorus concentration, and removing nitrogen and phosphorus when the concentration exceeds a standard.

A treatment method for a river system in a reservoir area, comprising: S1. determining whether a time from a current date to the rainy season is less than a preset duration; S2. moving a pressure sensor upward; S3. determining whether the pressure data meets corresponding conditions; S4. determining whether a duration of the pressure data is less than the preset duration; S5. determining whether an interval between the current time and the time for collecting pressure/nitrogen and phosphorus is greater than a preset number of days; S6. acquiring an image information of a river bottom, and sending it to neural network model for identification to obtain a depth of a sludge; S7. determining whether the depth of a sludge has reached a dredging depth, if so, starting a sludge pump to clean up; S8. collecting nitrogen and phosphorus concentration, and removing nitrogen and phosphorus when the concentration exceeds a standard.

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.

The size estimation device 5 is provided with an estimation unit 51 and a calculation unit 52. The estimation unit 51 estimates, on the basis of a learning model which learns the feature of a first underwater creature body that faces one direction in an image captured in the water, a first feature point that indicates one end in a one direction side of a second underwater creature body that faces a one direction included in an image captured outside and a second feature point that indicates the other end. The calculation unit 52 calculates information indicating the size of the second underwater creature body on the basis of information including the first feature point and the second feature point.

The size estimation device 5 is provided with an estimation unit 51 and a calculation unit 52. The estimation unit 51 estimates, on the basis of a learning model which learns the feature of a first underwater creature body that faces one direction in an image captured in the water, a first feature point that indicates one end in a one direction side of a second underwater creature body that faces a one direction included in an image captured outside and a second feature point that indicates the other end. The calculation unit 52 calculates information indicating the size of the second underwater creature body on the basis of information including the first feature point and the second feature point.

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 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.