ANALYSIS AND SORTING IN AQUACULTURE
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sorting fish in aquaculture. In some implementations, one or more images are obtained of a particular fish within a population of fish. Based on the one or more images of the fish, a data element is determined. The data element can include a first value that reflects a physical characteristic of the particular fish, and a second value that reflects a runt factor of the particular fish. Based on the data element, the fish is classified as a member of a particular subpopulation of the population of fish. An actuator of an automated fish sorter is controlled based on classifying the particular fish as a member of the particular subpopulation of the population of fish.
1 . A computer-implemented method comprising:
obtaining one or more images of a particular fish within a population of fish;
determining, based on the one or more images of the fish, a data element including (i) a first value that reflects a physical characteristic of the particular fish, and (ii) a second value that reflects a runt factor of the particular fish;
classifying, based on the data element, the fish as a member of a particular subpopulation of the population of fish; and
controlling an actuator of an automated fish sorter based on classifying the particular fish as a member of the particular subpopulation of the population of fish.
2 . The computer-implemented method of claim 1 , wherein the one or more images of the fish include at least a first image representing a first view of the fish and a second image representing a different, second view of the fish.
3 . The computer-implemented method of claim 2 , wherein at least the first image and the second image are used to determine a three dimensional (3D) pose of the fish.
4 . The computer-implemented method of claim 1 , wherein the runt factor is calculated as a function of one or more weight values associated with the fish and one or more truss lengths corresponding to distances between locations on a body of the fish.
5 . The computer-implemented method of claim 1 , wherein the data element is determined, in part, by estimating a set of truss lengths corresponding to distances between locations on a body of the fish and determining a weight based on the set of truss lengths.
6 . The computer-implemented method of claim 1 , comprising generating one or more other data elements corresponding to other fish within the population of fish.
7 . The computer-implemented method of claim 6 , wherein classifying the fish as a member of the particular subpopulation includes comparing the data element to a threshold.
8 . The computer-implemented method of claim 1 , wherein the actuator controls a flap that selectively opens a particular passage among multiple passages that are associated with the automated fish sorter.
9 . The computer-implemented method of claim 8 , wherein the flap includes holes to reduce force required to actuate the actuator.
10 . The computer-implemented method of claim 1 , further comprising generating a visual representation using the first value of the data element and the second value of the data element as a coordinate pair to represent a data point within a graph associated with the particular fish.
11 . A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
obtaining one or more images of a particular fish within a population of fish;
determining, based on the one or more images of the fish, a data element including (i) a first value that reflects a physical characteristic of the particular fish, and (ii) a second value that reflects a runt factor of the particular fish;
classifying, based on the data element, the fish as a member of a particular subpopulation of the population of fish; and
controlling an actuator of an automated fish sorter based on classifying the particular fish as a member of the particular subpopulation of the population of fish.
12 . The non-transitory, computer-readable medium of claim 11 , wherein the one or more images of the fish include at least a first image representing a first view of the fish and a second image representing a different, second view of the fish.
13 . The non-transitory, computer-readable medium of claim 12 , wherein at least the first image and the second image are used to determine a three dimensional (3D) pose of the fish.
14 . The non-transitory, computer-readable medium of claim 11 , wherein the runt factor is calculated as a function of one or more weight values associated with the fish and one or more truss lengths corresponding to distances between locations on a body of the fish.
15 . The non-transitory, computer-readable medium of claim 11 , wherein the data element is determined, in part, by estimating a set of truss lengths corresponding to distances between locations on a body of the fish and determining a weight based on the set of truss lengths.
16 . The non-transitory, computer-readable medium of claim 11 , comprising generating one or more other data elements corresponding to other fish within the population of fish.
17 . The non-transitory, computer-readable medium of claim 16 , wherein classifying the fish as a member of the particular subpopulation includes comparing the data element to a threshold.
18 . The non-transitory, computer-readable medium of claim 11 , wherein the actuator controls a flap that selectively opens a particular passage among multiple passages that are associated with the automated fish sorter.
19 . The non-transitory, computer-readable medium of claim 11 , further comprising generating a visual representation using the first value of the data element and the second value of the data element as a coordinate pair to represent a data point within a graph associated with the particular fish.
20 . A computer-implemented system, comprising:
one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
obtaining one or more images of a particular fish within a population of fish;
determining, based on the one or more images of the fish, a data element including (i) a first value that reflects a physical characteristic of the particular fish, and (ii) a second value that reflects a runt factor of the particular fish;
classifying, based on the data element, the fish as a member of a particular subpopulation of the population of fish; and
controlling an actuator of an automated fish sorter based on classifying the particular fish as a member of the particular subpopulation of the population of fish.
21 . A computer-implemented method comprising:
obtaining one or more images of a particular fish within a population of fish;
determining, based on the one or more images of the fish, a data element including (i) a first value that reflects a physical characteristic of the particular fish, and (ii) a second value that reflects a runt factor of the particular fish;
classifying, based on the data element, the fish as a member of a particular subpopulation of the population of fish; and
generating a prediction of a healthy yield for the population of fish based at least on classifying the fish as the member of the particular subpopulation.