WO2025062366A1

WO2025062366A1 - In channel analysis of aquatic animals based on non-invasive determination of animal characteristics

Info

Publication number: WO2025062366A1
Application number: PCT/IB2024/059176
Authority: WO
Inventors: Ayal Brenner; Stian Håland ROGNLID; Christoph PODES; Ana Catarina Marcelino Costa Da Silva; Magnus Dorian DEISS
Original assignee: Aquaticode Ltd.
Priority date: 2023-09-22
Filing date: 2024-09-20
Publication date: 2025-03-27
Also published as: NO20240951A1

Abstract

Improvements in aquaculture that allow for increasing the number and growth efficiency of fish or other aquatic animals 103 in an aquaculture setting by identifying or predicting characteristics of the animals based on visual or ultrasound images of the animals obtained through non-invasive means are described. The animals are sorted based on the characteristics. A modular removable imager 104 is configured to obtain one or more images of an aquatic animal located inside a channel 102. The channel is configured to carry the aquatic animal in water from a first location 110 to a second location 112. The imager 104 is configured to obtain the one or more images while the animal moves through the channel. Liquid flow through the channel may be controlled by a bypass 175. In some embodiments, a vaccinator 113 configured to vaccinate the aquatic animals in the channel or outside the channel may be included.

Description

IN CHANNEL ANALYSIS OF AQUATIC ANIMALS BASED ON NON-

INVASIVE DETERMINATION OF ANIMAL CHARACTERISTICS

FIELD OF THE INVENTION

[001] The invention relates to in channel analysis of aquatic animals based on non-invasive determination of animal characteristics.

BACKGROUND

[002] Aquaculture, the farming of fish, crustaceans, mollusks, aquatic plants, algae, and other organisms, is the fastest growing food sector, and it provides most fish for human consumption. However, there is a rising demand for seafood due to human population growth, increasing disposable income in the developing world (which coincides with an increase of a meat-based diet), increasing coastal populations, and general health awareness (which tends to motivate consumers to fish-based protein). Wild fish resources are already at their limits, and the world market is increasingly focusing on being more sustainable and environmentally responsible, meaning increased harvesting of wild fish is not feasible.

SUMMARY

[003] Given the infeasibility of meeting the world demand through harvesting wild fish and/or other aquatic animals, aquaculture represents a natural solution. However, increasing the use of aquaculture raises additional concerns such as increased disease, growth and feeding inefficiencies, and waste management. For example, increasing the number of fish in a fish farm, without mitigating measures, increases the prevalence of diseases as the proximity of fish to each other increases. The growth and feed inefficiencies are increased as more fish in proximity to each other make health and feed management more difficult, and more fish lead to more fish waste which increases the environmental impact of a fish farm on its immediate area.

[004] Methods and systems are disclosed herein for improvements in aquaculture that allow for increasing the number and growth efficiency of fish and/or other aquatic animals in an aquaculture setting while still mitigating the problems above. Specifically, using a unique sorting system, fish and/or other aquatic animals with superior traits are identified and sorted. In conventional systems, fish may be sorted or graded as they pass along a conveyor belt or another similar structure based on size, or other visually evident information, for example. [005] In contrast to the conventional approaches, methods and systems are described herein for non-invasive procedures for identifying characteristics in fish and/or other aquatic animals, and sorting the fish (and/or other aquatic animals) based on the characteristics. Example fish and/or other aquatic animals may include fin fish (including teleost fish and cartilaginous fish comprising salmon, tilapia, etc.), shellfish (including crustaceans, mollusks, etc.,), cephalopods, and/or other fish and/or aquatic animals. The size of fish and/or other aquatic animals that may be analyzed using the present systems and methods is significantly smaller than in prior systems. For example a smallest size in prior systems is about 0.5 kg (e.g., for trout) or about 1kg (for salmon). The present systems and methods may be utilized for fish and/or other aquatic animals that weigh as little as 20 grams (also being able to handle weights up to about 500 grams). One example of why this is important is that organs used to detect female versus male fish are much harder to detect in smaller fish, but the earlier one can separate the gender, the more beneficial for the producer (especially in an industrial scale production environment). This also applies to other characteristics as described below.

[006] Here, an imager is configured to obtain one or more images of an aquatic animal. The aquatic animal may be located in a channel. The channel is configured to carry the aquatic animal in water from a first location to a second location. The imager is configured to obtain the one or more images while the animal moves (e.g., inside a channel). The animal may be pumped in water, for example (though other non-pumping techniques may be used - gravity based movement as one possible example). Pumping facilitates a high throughput through the channel and/or has other advantages for aquaculture (e.g., relevant in an industrial scale production environment). The present systems and methods may be configured to identify and sort based on external and/or internal conditions in juvenile or other aquatic animals such as deformities (external and/or internal), gender (internal and/or external), growth potential (internal), diseases resistance (internal), etc. It should be noted that while the discussion herein focuses on fish, the described systems and methods can be similarly applied with other animals.

[007] For example, according to an embodiment, there is provided a system for analyzing aquatic animals. The system comprises an imager configured to obtain one or more images of an aquatic animal. The aquatic animal is carried in water from a first location to a second location. The imager is configured to obtain the one or more images while the animal moves. The system comprises a separator configured to separate the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal without interference from the other aquatic animals. The system comprises a positioner configured to position the aquatic animal in a target orientation for the imager to obtain the one or more images. The system comprises control circuitry configured to determine, based on the one or more images, a characteristic of the animal. In some embodiments, the aquatic animal is a fin fish (including teleost fish and cartilaginous fish comprising salmon, tilapia, etc.), shellfish (including crustaceans, mollusks, etc.,), cephalopods, and/or other fish and/or aquatic animals.

[008] In some embodiments, the system comprises a sorter configured to sort the aquatic animal into a group. The control circuitry may be configured to control the sorter to sort the aquatic animal into the group based on the characteristic, for example. In some embodiments, the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal.

[009] In some embodiments, the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X- ray machine, a computed tomography (CT) scanner, a magnetic resonance imager (MRI), and/or other imagers.

[010] In some embodiments, the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images. In some embodiments, the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images. In some embodiments, the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, a fat percentage of the aquatic animal, and/or other characteristics.

[Oi l] In some embodiments, the control circuitry is configured to: determine a fixed starting point for the one or more images of the aquatic animal, where the one or more images are obtained along a scan length that extends from the fixed starting point; trigger the imager to take the one or more images; and/or cause the imager to continuously scan as aquatic animals pass by, into and out of a field of view of the imager. For example, in some embodiments, the aquatic animal is a fish, or a crustacean, and the fixed starting point for the one or more images corresponds to a start of a tip of the nose or an end of a tail of the fish and/or the crustacean.

[012] In some embodiments, the imager is configured to move along the channel and/or around the channel to obtain the one or more images of the aquatic animal. The imager may be configured to move around the channel to enhance an angle at which the imaging of the aquatic animal takes place. In some embodiments, the imager moves along the channel to compensate for a speed of the aquatic animal.

[013] In some embodiments, the imager comprises an ultrasound transducer and a camera. The camera may be configured to obtain a red green blue (RGB) image set that includes a visual image; and the ultrasound transducer may be configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image. The control circuitry may be configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

[014] In some embodiments, the aquatic animal is pumped, in water, from the first location to the second location by a pump.

[015] In some embodiments, the aquatic animal is fully conscious and free of sedatives. However, in some embodiments, the aquatic animal may be partially sedated to facilitate a measure of calmness during pumping. With partial sedation, for example, an aquatic animal may be able to swim or maintain their buoyancy on its own, for example. Partial sedation may be accomplished using a tranquilizer, for example, and/or other sedatives (e.g., where relatively light doses induce the partial sedation).

[016] In some embodiments, the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal. While a fish is not touched with the imager, in the case of ultrasound, the water may be used for acoustic coupling. In some embodiments, the imager may touch the aquatic animal.

[017] In some embodiments, the system may include a vaccinator used to vaccinate the aquatic animals.

[018] In some embodiments, another system for analyzing aquatic animals is provided. The system comprises an imager configured to obtain one or more images of an aquatic animal located inside a channel. The channel is configured to carry the aquatic animal in water from a first location to a second location. The imager is configured to obtain the one or more images while the aquatic animal moves through the channel. The system comprises control circuitry configured to determine, based on the one or more images, a characteristic of the aquatic animal. The aquatic animal in water is pumped from the first location to the second location in the channel by a pump, and the aquatic animal is fully conscious and free of sedatives in the channel (though as described above, in some embodiments, partial sedation may be used).

[019] According to another embodiment, there is provided a method for sorting animals comprising one or more of the operations described above, performed by one or more components of the analysis system(s).

[020] According to another embodiment, there is provided a tangible, non-transitory, machine-readable medium storing instructions that, when executed by a data processing apparatus of the control circuitry, cause the control circuitry to perform one or more of the operations described above.

[021] The efficiencies and performance of animals such as fish and/or other aquatic animals are thus increased without the drawbacks discussed above.

[022] Various other aspects, features, and advantages of the invention will be apparent through the detailed description of the invention and the drawings attached hereto. It is also to be understood that both the foregoing general description and the following detailed description are examples and not restrictive of the scope of the invention. As used in the specification and in the claims, the singular forms of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. In addition, as used in the specification and the claims, the term “or” means “and/or” unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

[023] FIG. 1A illustrates a perspective view of a system for analyzing aquatic animals, in accordance with one or more embodiments.

[024] FIG. IB illustrates a view of another embodiment of the system, with a bypass, in accordance with one or more embodiments.

[025] FIG. 2 illustrates a separator of the system, in accordance with one or more embodiments.

[026] FIG. 3 illustrates a positioner of the system, in accordance with one or more embodiments.

[027] FIG. 4 illustrates an imager of the system, in accordance with one or more embodiments.

[028] FIG. 5 illustrates a sorter of the system, in accordance with one or more embodiments. [029] FIG. 6 illustrates control circuitry of the system, in accordance with one or more embodiments.

[030] FIG. 7 illustrates a computing system that is part of the control circuitry of the system, featuring a machine learning model configured to determine characteristics of animals, in accordance with one or more embodiments.

[031] FIG. 8 shows graphical representations of artificial neural network models for characteristic determination based on information from the imager, in accordance with one or more embodiments.

[032] FIG. 9 illustrates a method for analyzing animals with the analyzing system, in accordance with one or more embodiments.

DETAILED DESCRIPTION OF THE DRAWINGS

[033] In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be appreciated, however, by those having skill in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other cases, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention. [034] In contrast to conventional approaches to identifying animal characteristics that use invasive approaches, methods and systems are described herein for non-invasive procedures that identify animal characteristics non-invasively and efficiently, and sort the animals based on the characteristics, for farming or other applications. A sufficiently high throughput (animals per hour) in a system with a limited size (to be able to transport and potentially share between farms, plus producers have limited space) is provided. Advantageously, even with the high throughput, the system produces ultrasound images which are readable by a machine learning model (such as a neural network) so that the machine learning model can produce predictions with a very high accuracy on features like gender determination, for example, in real time or near real time.

[035] Sufficiently high throughput is achieved by, among other things, configuring an imager to obtain one or more images of an aquatic animal located inside a channel. A channel may include a tube or pipe, a half open tube or pipe, a tube or pipe with various holes or slots, a passageway, a duct, a gutter, a groove, a furrow, a conduit, a trough, a trench, a culvert, a sluice, a spillway, ditch, a drain, a waterway, a canal, etc. Note that the following description refers to a tube extensively, and illustrates a tube in various figures. However, this is not intended to be limiting. The aquatic animals may be carried in water using any type of channel or other structure that allows the systems and methods to function as described. The principles described herein may be applied to other vessels (e.g., a moving compartment) and/or conduits, or in applications which do not require vessels or conduits at all (e.g., gravity based movement where a channel may not be necessary at all, if for example, an aquatic animal can be positioned under an imager as described while the aquatic animal moves in a flow of water).

[036] Notwithstanding the paragraph above, a channel such as a tube may be configured to carry the aquatic animal in water from a first location to a second location. The imager is configured to obtain the one or more images while the animal moves through the tube. The animal may be pumped through the tube in water, for example. Pumping facilitates a high throughput through the tube and/or has other advantages for aquaculture (e.g., relevant in an industrial scale production environment). However, other non-pumping techniques may be used - such as gravity based movement as one possible example.

[037] Also as described above, example fish and/or other aquatic animals may include salmon, tilapia, crustaceans, mollusks, etc. The size of fish and/or other aquatic animals that may be analyzed using the present systems and methods is significantly smaller than in prior systems. For example a smallest size in prior systems is about 0.5 kg (e.g., for trout) or about 1kg (for salmon). The present systems and methods may be utilized for fish and/or other aquatic animals that weigh as little as 0.2 grams (also being able to handle weights up to about 2.3kg or more - such as 500g for trout, 1kg for salmon, etc.). One example of why this is important is that organs used to detect female versus male fish are much harder to detect in smaller fish, but the earlier one can separate the gender, the more beneficial for the producer (especially in an industrial scale production environment). This also applies to other characteristics as described below.

[038] FIG. 1A illustrates a perspective view of an analysis system 100, in accordance with one or more embodiments. System 100 is configured for analyzing an aquatic animal 101 such as a fish (e.g., a salmon, tilapia, etc.) in this example, but may be configured similarly for sorting other aquatic animals. Other aquatic animals may comprise shellfish such as crustaceans, mollusks, etc., cephalopods, etc. (these examples and the other similar examples provided throughout this disclosure are not intended to be limiting). As shown in FIG. 1A, system 100 includes a tube 102 (which is one potential example of a channel), an imager 104, a separator 106, a positioner 107 (or orientor), a sorter 108, a vaccinator 113, control circuitry 109 (which may be operatively coupled to one or more of the other components of system 100 via a wired and/or wired network represented by the cloud in FIG. 1A - also see control circuitry 600 shown in Fig. 6), and/or other components. Note that the various figures shown and described herein illustrate one possible example embodiment of system 100. Other embodiments are contemplated which provide the same functionality. For example, two or more tubes (channels) running in parallel may be provided. Fish from one tube may be separated into two or more tubes, and each tube may have an imager and a sorter, etc.. This is just one of many possible example variations.

[039] As another possible variation, FIG. IB illustrates a view of another embodiment of system 100, comprising a bypass 175. Note that in this embodiment, imager 104 comprises a camera 104A and an ultrasound transducer 104B (as described below) - noting that one or the other of these may or may not be used in system 100 (e.g., system 100 may include only camera 104A and not ultrasound transducer 104B, or vice versa, in one embodiment).

Bypass 175 is configured to control the flow of water through a portion 177 of tube 102 corresponding to where imager 104 (i.e., 104A and 104B in this example) operates. Controlling the flow of water through portion 177 of tube 102 facilitates using a different diameter for portion 177 of tube 102 relative to one or more other portions 179 of tube 102, and/or has other advantages. In some embodiments, portion 177 of tube 102 is interchangeable such that different tube portions 177 with different diameters may be interchangeably included in system 100, depending on the flow of water and/or other factors. In some embodiments, as shown in FIG. IB, bypass 175 may comprise a grating, netting, or similar element 181, configured to prevent aquatic animals from entering bypass 175. Flow can be managed by changing the diameters of bypass 175, tube 102 (e.g., where imaging occurs), and/or using other techniques. In some embodiments, element 181 may also or instead comprise a diverter such as a moveable divider configured to divert at least some of the flow of water (see white colored arrows in FIG. IB) to a secondary bypass channel 183, return channels 185 and 187 (two are shown in this example, but other quantities are contemplated), and/or other components. In some embodiments, bypass 175 may comprise an active control system (e.g., controlled by the controller described herein). In some embodiments, bypass 175 comprises a passive overflow channel. Aquatic animals 101 are blocked from passing through bypass 175 by a grating, net, and/or other similar element 181 structure. Adjusting the size of tube 102 to better position an aquatic animal 101, or control the speed of the aquatic animal 101, system 100 accounts for water flow using bypass 175. The same applies to sorting the aquatic animals after imaging (e.g., as described herein), ensuring adequate water flow to supply sorting channels. [040] Returning to FIG. 1A, tube 102 is configured to carry aquatic animal 101 in water from a first location 110 to a second location 112. Tube 102, shown as a traditional tube with a round cross section in FIG. 1A and other figures, is representative of many possible channels or other conduits, with many possible shapes and sizes, that may be used as a channel or conduit to carry aquatic animals 101 as described herein. For example, tube 102 may have a rectangular cross section, tube 102 may be another channel (e.g., half a tube), etc. In some embodiments, aquatic animal 101 is pumped, in water, from first location 110 to second location in tube 102 by a pump 111. (Though in some embodiments, other nonpumping techniques may be used - gravity based movement as one possible example.) Pump 111 may be any pump capable of pumping the aquatic animals through system 100 as described herein. Aquatic animal 101 is fully conscious and free of sedatives in tube 102. However, in some embodiments, the aquatic animal 101 may be partially sedated to facilitate a measure of calmness during pumping. With partial sedation, for example, an aquatic animal

101 may be able to swim on its own, for example. Partial sedation may be accomplished using a tranquilizer, for example, and/or other sedatives (e.g., where relatively light doses induce the partial sedation). Tube 102 is configured to receive animals 101 (e.g., such as fish) and conduct the animals along a path within tube 102. The animals may freely swim (e.g., at lower pump speeds of pump 111) and/or be pumped into tube 102 in an unorganized larger group, in smaller groups, one by one, and/or in other ways. Animals 101 move through tube 102 and pass imager 104, where they are each imaged (e.g., ultrasonically, and/or in other ways). Once the images are processed, animals 101 are sorted into (e.g., three or more) groups by sorter 108. In some embodiments, tube 102 begins at a feeder input zone (e.g., first location 110), which may be a tank, pond, pool, etc.; where an operator places a corresponding end of tube 102 (e.g., so that animals 101 can be pumped out of the tank, pond, pool, etc.). In contrast to prior systems, tube 102 does not have a plurality of compartments configured to receive and hold individual animals while they are imaged.

[041] Separator 106 is configured to separate aquatic animal 101 from other aquatic animals 103 such that imager 104 can obtain the one or more images of aquatic animal 101 in tube

102 without interference from the other aquatic animals 103. FIG. 2 illustrates a more detailed view of one possible example embodiment of separator 106. As shown in FIG. 2, separator 106 one or more (e.g., a series of) open ended basket shaped funnel components 200 oriented in tube 102 and/or sized to separate aquatic animals 101 (fish in this example) from each other. For example, one end of the basket shaped funnel segments may be sized such that less and less (e.g., until only one) fish can progress through the basket shaped funnel segments at one time. Two or more basket shaped funnel segments may be used in case more than one fish passes through at a time. In some embodiments, an open end of each segment may become smaller and smaller for each segment along a flow direction in tube 102. Each segment may slotted sides, for example, to ensure water flows freely through each segment.

[042] FIG. 3 illustrates a more detailed view of one possible example embodiment of positioner 107 (which can also be thought of as an orientor). Positioner 107 is configured to position aquatic animal 101 in a target orientation in tube 102 for imager 104 to obtain the one or more images. In this example, the position or orientation of the fish is a tail to head orientation along tube 102, but could also be head to tail, or some other position. For example, the target orientation may include tail to head - ventral side (belly) up and/or ventral side down, and head to tail - ventral side (belly) up and/or ventral side down. Positioner 107 has a funnel shaped end 300 and then an inner tube portion 302. Funnel shaped end 300 is configured to guide into and/or maintain aquatic animal 101 in the target orientation so that it can pass into inner tube portion 302 and past imager 104.

[043] FIG. 4 illustrates a more detailed view of imager 104. Imager 104 may be any imaging device, sensor, and/or other device configured to capture a signal or group of signals (e.g., images, video, sound, etc.) that provide information about an aquatic animal and/or its behavior. For example, imager 104 may be configured to obtain one or more images of aquatic animal 101 while aquatic animal 101 is located inside tube 102. In some embodiments, imager 104 is contactless, configured to image aquatic animal 101 without contacting aquatic animal 101. In some embodiments, imager 104 may touch aquatic animal 101. Imager 104 may obtain the one or more images of aquatic animal 101 from any side and/or in any orientation, and/or in any other position that facilitates the functionality described herein. For example, imager 104 may obtain the one or more images of one side of aquatic animal 101, from an opposite side of aquatic animal 101, from the top or bottom of aquatic animal 101, and/or from any other orientation with respect to aquatic animal 101. In addition, note that imager 104 may comprise one or more of the same sensors, cameras, and/or other components. For example, imager 104 may include one or more ultrasound transducers, one or more still and/or video cameras (e.g., red green blue (RGB) cameras), one or more infrared sensors, one or more near infrared sensors, one or more ultra violet imagers, one or more hyperspectral cameras, one or more X-ray machines, one or more computed tomography (CT) scanners, one or more magnetic resonance imager (MRI)s, and/or other imagers (i.e., imager 104 may include one or more of each of these different devices). [044] In some embodiments, imager 104 is modular and removable from system 100 such that imager 104 is configured to be coupled to and used with multiple systems 100 for analyzing aquatic animals 101. In some embodiments, imager 104 and/or other parts of system 100 may be shared between different production or aquaculture sites, while the other components of system 100 remain permanently or semi-permanently on site. This modularity and removability of imager 104 may increase utilization of high value components of system 100 because they might be shared between systems, and/or have other advantages. In some embodiments, one or more adaptors and/or other components may be used to facilitate modularity. In some embodiments, imager 104 may be removably coupled with system 100 by screws, nuts, bolts, clamps, clips, etc.

[045] Imager 104 is configured to obtain the one or more images while aquatic animal 101 moves through tube 102. In some embodiments, imager 104 comprises an ultrasound transducer configured to obtain one more ultrasound images of aquatic animal 101, a still and/or video camera configured to obtain one or more red green blue (RGB) images of aquatic animal 101, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, a magnetic resonance imager (MRI), and/or other imagers (i.e., imager 104 may include one or more of these different devices). For example, while a fish is not touched with the imager, in the case of ultrasound, the water may be used for acoustic coupling. In some embodiments, imager 104 is configured to move along tube 102 and/or around tube 102 to obtain the one or more images of aquatic animal 101. Imager 104 may be configured to move around tube 102 to enhance an angle at which the imaging of aquatic animal 101 takes place, for example, and/or for other reasons. In some embodiments, imager 104 moves along tube 102 to compensate for a speed of aquatic animal 101 in the tube.

[046] As an example, in some embodiments, imager 104 is or includes one or more ultrasound transducers configured to obtain ultrasound images of the animals in tube 102. In some embodiments, the ultrasound transducers are configured to obtain an ultrasound image set of the animal that includes the ultrasound images. In some embodiments, an individual ultrasound transducer is configured to obtain one or more ultrasound images of a given animal in tube 102. The ultrasound image set may include one or more ultrasound images of the animal. If the ultrasound image set includes multiple ultrasound images, the multiple ultrasound images may be captured from different angles (e.g., a top view, side view, bottom view, etc.) and/or may be captured substantially simultaneously. The views may also include plan, elevation, and/or section views. The one or more views may create a standardized series of orthographic two-dimensional images that represent the form of the three- dimensional animal. For example, six views of the animal may be used, with each projection plane parallel to one of the coordinate axes of the animal. The views may be positioned relative to each other according to either a first-angle projection scheme or a third-angle projection scheme. The ultrasound images in the ultrasound image set may include separate images (e.g., images stored separately, but linked by a common identifier such as a serial number) or images stored together. An ultrasound image in an ultrasound image set may also be a composite image (e.g., an image created by cutting, cropping, rearranging, and/or overlapping two or more ultrasound images.

[047] Ultrasound transducers may be configured to obtain the ultrasound images while the ultrasound transducers move along a portion of tube 102 with the animals. Ultrasound transducers may be configured to move in at least two dimensions. The at least two dimensions comprise a first dimension along tube 102 around tube 102, for example. The second dimension is substantially perpendicular to the first dimension and tube 102, for example. In some embodiments, an ultrasound transducer is configured to move in the first dimension and the second dimension substantially simultaneously while obtaining ultrasonic images.

[048] Movement in two dimensions occurs at a controlled speed over defined distances that correspond to movement of an animal on in tube 102, and a length of a given animal. The controlled speed and distances facilitate acquisition of standardized images for each animal carried by tube 102. A mechanical system comprising independent electrical linear actuators may be configured to move each ultrasound transducer along and/or around tube 102, or in other directions, for example. Such actuators may also be used to move a given transducer toward or away from tube 102 and/or the body of an aquatic animal 101.

[049] As another example, in some embodiments, imager 104 is or includes a camera configured to obtain visual images (e.g., video or still images) of aquatic animals 101 in tube 102 as the animals move past the camera. In some embodiments, the camera is configured to obtain a red green blue (RGB) image set that includes the visual images. For example, in some embodiments, the visual images may include an image set of an animal. The image set may include one or more images of the animal. If the image set includes multiple images, the multiple images may be captured from different angles (e.g., atop view, side view, bottom view, etc.) and/or may be captured substantially simultaneously. The views may also include plan, elevation, and/or section views. The one or more views may create a standardized series of orthographic two-dimensional images that represent the form of the three- dimensional animal. For example, six views of the animal may be used, with each projection plane parallel to one of the coordinate axes of the animal. The views may be positioned relative to each other according to either a first-angle projection scheme or a third-angle projection scheme. The images in the image set may include separate images (e.g., images stored separately, but linked by a common identifier such as a serial number) or images stored together. An image in an image set may also be a composite image (e.g., an image created by cutting, cropping, rearranging, and/or overlapping two or more images).

[050] In some embodiments, information from imager 104 (e.g., various different images, video, etc., taken at different times, from different angles, etc.) may be used to capture the movement, swimming patterns, and/or other behavior of an aquatic animal in the tube (or other channel). For example, control circuitry 109 (described below) may be configured to determine, based on one or more images, a characteristic of aquatic animal 101 associated with the movement, swimming patterns, and/or other behavior. This characteristic may be associated with performance of the aquatic animal, health and/or welfare, phenotypes, and/or other characteristics of the aquatic animal.

[051] FIG. 5 illustrates a more detailed view of sorter 108. Sorter 108 is configured to sort aquatic animal 101 into a group. In some embodiments, sorter 108 comprises one or more flaps 120 coupled to tube 102 and/or other components. Note that this is just one possible example embodiment. Other configurations for sorter 108 are contemplated. For example, in some embodiments, sorter 108 may comprise a mechanical arm and/or other components controlled by control circuitry 109 to move between multiple positions such that sorting the animal 101 into a group (as described herein) comprises moving the mechanical arm to direct the animal from tube 102 to a same physical location as other animals in the group.

[052] Vaccinator 113 (shown in FIG. 5, along with FIG. 4 and FIG. 1A) may be used to vaccinate aquatic animal 101. In some embodiments, vaccination may be in tube 102. In some embodiments, vaccination may be outside of tube 102. In some embodiments, vaccination may occur when aquatic animal 101 is briefly removed from the water, for example. Vaccination may occur after sorting by sorter 108, for example, such that only aquatic animals 101 sorted into one or more specific groups (e.g., in tubes 130, 132, and/or 134) are vaccinated (e.g. to save vaccine and/or other costs from being spent on a group of aquatic animals that may eventually be rejected). FIG. 5 depicts vaccinator 113 on a track (see dotted lines) and/or another similar mechanism that facilitates movement (e.g., translation, rotation, etc.) of vaccinator 113 along tube 102 (e.g. to either side of sorter 108), along tubes 130-134, and/or other movement. Vaccinator 113 may be controlled to move along the track by control circuitry 109, for example. Vaccinator 113 may include a sharp and/or other components that facilitate vaccination of an aquatic animal 101 while still inside one of these tubes, for example. In some embodiments, vaccination may occur on its own, without imaging and/or sorting, for example.

[053] In some embodiments, vaccinator 113 includes multiple needles, or needle-less options. For example, one or more fine needles may be configured to deliver a vaccine to an aquatic animal 101. In some embodiments, vaccinator 113 may be configured to make one or more automated adjustments. For example, vaccinator 113 may comprise one or more sensors, one or more actuators, and/or other components configured to automatically adjust one or more injection parameters based on each aquatic animal’s size, species, condition, and/or other characteristics. In some embodiments, vaccinator 113 comprises a camera and/or other components configured to guide an injection so that the point of impact is correct regardless of the size and/or other characteristics of the aquatic animal 101.

[054] In some embodiments, vaccinator 113 comprises a reservoir and/or other components for vaccine storage. The reservoir is configured to store the vaccine in a controlled environment to maintain its efficacy until it is administered. The reservoir size can vary based on the scale of the vaccination operation and the dosage required, and/or other factors. In some embodiments, vaccinator 113 comprises a pump and/or other components configured to deliver precision doses of the vaccine from the reservoir to an injection need (and/or other components of an injection system).

[055] Control circuitry 109 (FIG. 1A) is operatively coupled to (e.g., wirelessly via network as shown in FIG. 1 A, via wires, and/or by other methods) and configured to control imager 104, sorter 108, pump 111, vaccinator 113, bypass 175 (FIG. IB), and/or other components of system 100. Control circuitry 109 is configured to determine, based on the one or more images, a characteristic of aquatic animal 101. Control circuitry 109 may be configured to control sorter 108 to sort aquatic animal 101 into a group based on the characteristic, for example, and/or other information. Sorting may be performed based on one or more characteristics determined based on one or more ultrasonic images, RGB images, infrared sensor images, near infrared sensor images, ultra violet imager images, hyperspectral camera images, X-ray images, computed tomography (CT) scanner images, magnetic resonance imager (MRI) images, and/or other images, alone, and/or in any combination. For example, a characteristic may be determined and an aquatic animal may be sorted based on an ultrasound image alone, an RGB image alone, and/or some combination of the two. In some embodiments, control circuitry 109 is configured to control flaps 120 to move between multiple positions such that sorting aquatic animal 101 into a group comprises moving the one or more flaps 120 to direct aquatic animal 101 from the tube 102. In this example, aquatic animal 101 may be directed into one of tubes 130, 132, or 134, which may correspond to different characteristics, different levels and/or amounts of a characteristic, and/or other information. For example, tubes 130, 132, and/or 134 may be used to by control circuitry 109 to sort fish into male and female groups, groups with and without disease (and/or groups with different amounts of disease), different sizes, different levels of maturation, groups with and without kidney stones, groups with and without heart and/or skeletal muscle inflammation, groups with and without body and/or head deformities, groups by status of smoltification, groups according to fat percentage of the aquatic animal, and/or other characteristics.

[056] In some embodiments, control circuitry 109 is configured to determine the characteristic of aquatic animal 101 based on the one or more images by inputting the one or more images to an artificial neural network (see FIG. 8), which is trained to output the characteristic based on the one or more images. In some embodiments, the artificial neural network is trained to identify one or more external and/or internal characteristics of animal 101 based on the one or more images. In some embodiments, the characteristic is associated with performance of the aquatic animal, health and/or welfare of the aquatic animal, phenotypes, and/or other characteristics. In some embodiments, performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, body fat percentage of the aquatic animal, and/or other performance characteristics. In some embodiments, health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, disease resistance, and/or other health and/or welfare characteristics. Disease presence and/or disease resistance may be associated with diseases including bacterial kidney disease (BKD), kidney stones, heart and skeletal muscle inflammation (HSMI), hemorrhagic smolt syndrome (HSS), yersinosis, flavobacteriosis, saprolegniosis, cardiomyopathy syndrome (CMS), salmon gill poxvirus (SGPV), vibrio, nodavirus, pasteurellosis, and/or other diseases. As several additional examples of possible characteristics, the characteristic may be gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, a fat percentage of the aquatic animal, and/or other characteristics.

[057] In some embodiments, control circuitry 109 is configured to determine a fixed starting point for the one or more images of aquatic animal 101, where the one or more images are obtained along a scan length that extends from the fixed starting point. In some embodiments, control circuitry may be configured to trigger the imager to take the one or more images. In some embodiments, control circuitry 109 may be configured to cause imager 104 to continuously scan as aquatic animals pass through tube 102, into and out of a field of view of imager 104. For example, in some embodiments, aquatic animal 101 is a fish, or a crustacean, and the fixed starting point for the one or more images corresponds to a start of a tip of the nose or an end of a tail of the fish and/or the crustacean.

[058] As an example, in some embodiments, imager 104 comprises an ultrasound transducer and a camera. The camera may be configured to obtain a red green blue (RGB) image set that includes a visual image; and the ultrasound transducer may be configured to obtain an ultrasound image set of aquatic animal 101 that includes the ultrasound image. Control circuitry 600 may be configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

[059] In some embodiments, control circuitry 109 is configured to cause sorter 108 to handle, sort, and/or transfer animals (e.g., for vaccination, gender segregation, transfer to sea or breeding area, etc.). In such embodiments, the characteristics may be detected based on the (visual or) ultrasound images in real-time (e.g., as the animals pass through tube 102 or otherwise transferred). That is, following the output of a given characteristic for a given animal, sorter 108 may sort the animal based on the determined characteristic.

[060] FIG. 6 illustrates a more detailed view of control circuitry 109. For example, FIG. 6 illustrates various wired or wireless electronic communication paths 602 formed between different components of system 100. FIG. 6 illustrates actuators 610 configured to move the ultrasound transducers associated with imager 104 (as described above), vaccinator 113, sorter 108, element 181 (with secondary bypass channel 183 going into the page in this example), and/or other components.

[061] In some embodiments, control circuitry 109 may determine a starting point for ultrasound imaging based on one or more RGB images from a camera (e.g., with the camera placed inside tube 102). This may comprise generating a pixel array based on the visual images or image set of the animal. The pixel array may refer to computer data that describes an image (e.g., pixel by pixel). In some embodiments, this may include one or more vectors, arrays, and/or matrices that represent either a Red, Green, Blue or grayscale image. Furthermore, in some embodiments, control circuity 109 may additionally convert the image set from a set of one or more vectors, arrays, and/or matrices to another set of one or more vectors, arrays, and/or matrices. For example, the control circuitry 109 may convert an image set having a red color array, a green color array, and a blue color to a grayscale color array. In some embodiments, for example, the animal is a fish and the starting point, determined based on the pixel array, corresponds to a start of an operculum of the fish.

[062] Control circuitry 109 is configured to determine, based on the visual images, the ultrasound images, and/or other information, characteristics of the animals. In some embodiments, control circuitry 109 is configured to receive the visual images from imager 104 (FIG. 1A). In some embodiments, control circuitry 109 also includes memory (as described herein), which may be incorporated into and/or accessible by control circuitry 109. In some embodiments, control circuitry 109 may retrieve the (visual or ultrasound) image sets from memory.

[063] A characteristic may be or describe a condition, feature, or quality of an animal, that may be used to sort an animal into a group. The characteristics may include a current physiological condition (e.g., a condition occurring normal in the body of the animal) such as a gender of the animal (e.g., as determined by the development of sex organs) and/or a stage of development in the animal (e.g., the state of smoltification in a fish). The characteristics may include a predisposition to a future physiological condition such as a growth rate, maturity date, and/or behavioral traits. The characteristics may include a pathological condition (e.g., a condition centered on an abnormality in the body of the animal based on a response to a disease) such as whether or not the animal is suffering from a given disease and/or is currently infected with a given disease. The characteristics may include a genetic condition (e.g., a condition based on the formation of the genome of the animal) such as whether or not the animal includes a given genotype. The characteristics may include presence of a measurable substance in an aquatic animal whose presence is indicative of a disease, infection, current internal condition, future internal condition, and/or environmental exposure. The characteristics may include external characteristics (e.g., one or more observable characteristics of an animal resulting from the interaction of its genotype with the environment). These externally visible traits may include traits corresponding to physiological changes in the animal. For example, during smoltification in a fish (i.e., the series of physiological changes where juvenile salmonid fish adapt from living in fresh water to living in seawater), externally visible traits related to this physiological change may include altered body shape, increased skin reflectance (silvery coloration), and increased enzyme production (e.g., sodium-potassium adenosine triphosphatase) in the gills. By way of several specific examples, the characteristics (which again may be determined based on ultrasound images, RGB images, and/or other information) may include a deformation, gender of an animal, presence of disease in an animal, size of an animal, early maturation of an animal, presence of bacterial kidney disease in an animal, heart or skeletal muscle inflammation in an animal, a fat percentage of an animal, a size of an animal, a shape of an animal, a weight of an animal, and/or other characteristics.

[064] Control circuitry 109 is configured to determine the characteristics of an animal based on one or more (e.g., ultrasound, visual, and/or other images and/or data) of that animal by inputting the one or more ultrasound images and/or visual images, etc., to a machine learning mode such as an artificial neural network, which is trained to output the characteristics based on the one or more images. The artificial neural network is trained to identify one or more internal and/or external characteristics of the animal based on the ultrasound image, and determine presence of a marker in the animal indicative of the characteristic output by the artificial neural network based on the one or more internal and/or external characteristics.

[065] As shown in FIG. 2, control circuitry 109 may include various components configured to perform or control one or more of the operations described above, such as a programmable logic controller (PLC) input output (I/O) board 620, one or more actuators 610 coupled to imager 104 and/or sorter 108, and/or other components. Control circuitry 109 may include a PLC 630, a control board 632, a human machine interface (HMI) 634, and/or other components that are part of or configured to communicate with a computing system 700 (described below), or other components.

[066] By way of a non-limiting example, FIG. 7 illustrates a computing system 700 that is part of control circuitry 109 (FIG. 1A, 6), featuring a machine learning model 722 configured to determine characteristics of animals, in accordance with one or more embodiments. As shown in FIG. 7, system 700 may include client device 702, client device 704 or other components. Each of client devices 702 and 704 may include any type of mobile terminal, fixed terminal, or other device. Each of these devices may receive content and data via input/output (hereinafter “I/O”) paths and may also include processors and/or other components to send and receive commands, requests, and other suitable data using the I/O paths. Control circuitry 109 may comprise any suitable processing circuitry. Each of these devices may also include a user input interface and/or display for use in receiving and displaying data. By way of example, client devices 702 and 704 may include a desktop computer, a server, or other client device. Users may, for instance, utilize one or more client devices 702 and 704 to interact with one another, one or more servers, or other components of computing system 700. It should be noted that, while one or more operations are described herein as being performed by particular components of computing system 700, those operations may, in some embodiments, be performed by other components of computing system 700. As an example, while one or more operations are described herein as being performed by components of client device 702, those operations may, in some embodiments, be performed by components of client device 704. It should be noted that, although some embodiments are described herein with respect to machine learning models, other prediction models (e.g., statistical models or other analytics models) may be used in lieu of or in addition to machine learning models in other embodiments (e.g., a statistical model replacing a machine learning model and a non-statistical model replacing a non-machine-leaming model in one or more embodiments).

[067] Each of these devices may also include memory in the form of electronic storage. The electronic storage may include non-transitory storage media that electronically stores information. The electronic storage media of the electronic storages may include one or both of (i) system storage that is provided integrally (e.g., substantially non-removable) with servers or client devices or (ii) removable storage that is removably connectable to the servers or client devices via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storages may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storage may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). The electronic storage may store software algorithms, information determined by the processors, information obtained from servers, information obtained from client devices, or other information that enables the functionality as described herein.

[068] FIG. 7 also includes communication paths 708, 710, and 712. Communication paths 708, 710, and 712 may include a local network (e.g., a Wi-Fi or other wired or wireless local network), the Internet, a mobile phone network, a mobile voice or data network (e.g., a 5G or LTE network), a cable network, a public switched telephone network, wires, or other types of communications network or combinations of communications networks. Communication paths 708, 710, and 712 may separately or together include one or more communications paths, such as a satellite path, a fiber-optic path, a cable path, a path that supports Internet communications (e.g., IPTV), free-space connections (e.g., for broadcast or other wireless signals), or any other suitable wired or wireless communications path or combination of such paths. The computing devices may include additional communication paths linking a plurality of hardware, software, and/or firmware components operating together. For example, the computing devices may be implemented by a cloud of computing platforms operating together as the computing devices.

[069] In some embodiments, computing system 700 may use one or more prediction models to predict characteristics based on visual images, ultrasound images, or other information. For example, as shown in FIG. 7, computing system 700 may predict a characteristic of an animal (e.g., a fish identified by a specimen identification) using machine learning model 722. The determination may be output shown as output 718 on client device 704.

Computing system 700 may include one or more neural networks (e.g., as discussed in relation to FIG. 8) or other machine learning models. These neural networks or other machine learning models may be located locally (e.g., executed by one or more components of computing system 700 located at or near fish processing) or remotely (e.g., executed by a remote or cloud server that is part of computing system 700).

[070] As an example, with respect to FIG. 7, machine learning model 722 may take inputs 724 and provide outputs 726. The inputs may include multiple data sets such as a training data set and a test data set. The data sets may represent (e.g., ultrasound) images (or image sets) of animals such as fish or other animals. In one use case, outputs 726 may be fed back to machine learning model 722 as input to train machine learning model 722 (e.g., alone or in conjunction with user indications of the accuracy of outputs 726, labels associated with the inputs, or with other reference feedback information). In another use case, machine learning model 722 may update its configurations (e.g., weights, biases, or other parameters) based on its assessment of its prediction (e.g., outputs 726) and reference feedback information (e.g., user indication of accuracy, reference labels, or other information). In another use case, where machine learning model 722 is a neural network, connection weights may be adjusted to reconcile differences between the neural network’s prediction and the reference feedback. In a further use case, one or more neurons (or nodes) of the neural network may require that their respective errors are sent backward through the neural network to them to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model 722 may be trained to generate better predictions.

[071] Machine learning model 722 may be trained to detect the characteristics in animals based on a set of ultrasound images. For example, ultrasound transducers may generate the ultrasound image set of a first fish (as an example of an animal). Computing system 700 may determine an internal and/or external characteristic of the first fish. The presence of one internal and/or external characteristic may be correlated to one or more other internal and/or external characteristics. For example, machine learning model 722 may have classifications for characteristics. Machine learning model 722 is then trained based on a first data set (e.g., including data of the first fish and others) to classify a specimen as having a given characteristic when particular ultrasound image features are present.

[072] The system may then receive an ultrasound image set of a second fish. Computing system 700 may input one or more of the ultrasound images in the set into machine learning model 722. Computing system 700 may then receive an output from machine learning model 722 indicating that the second fish has the same characteristic (e.g., genotype biomarker) as the first. For example, computing system 700 may input a second data set (e.g., ultrasound image sets of fish for which characteristics are not known) into machine learning model 722. Machine learning model 722 may then classify the image sets of fish based on the images. [073] FIG. 8 shows a graphical representations of artificial neural network models for characteristic determination based on images, in accordance with one or more embodiments. Model 800 illustrates an artificial neural network. Model 800 includes input layer 802.

Image sets may be entered into model 800 at this level. Model 800 also includes one or more hidden layers (e.g., hidden layer 804 and hidden layer 806). Model 800 may be based on a large collection of neural units (or artificial neurons). Model 800 loosely mimics the manner in which a biological brain works (e.g., via large clusters of biological neurons connected by axons). Each neural unit of a model 800 may be connected with many other neural units of model 800. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some embodiments, each individual neural unit may have a summation function which combines the values of all of its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function such that the signal must surpass before it propagates to other neural units. Model 800 may be selflearning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. During training, output layer 808 may corresponds to a classification of model 800 (e.g., whether or not a given image set corresponds to a characteristic) and an input known to correspond to that classification may be input into input layer 802. In some embodiments, model 800 may include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some embodiments, back propagation techniques may be utilized by model 800 where forward stimulation is used to reset weights on the “front” neural units. In some embodiments, stimulation and inhibition for the model may be more free-flowing, with connections interacting in a more chaotic and complex fashion. Model 800 also includes output layer 808. During testing, output layer 808 may indicate whether or not a given input corresponds to a classification of model 800 (e.g., whether or not a given image set corresponds to a characteristic).

[074] FIG. 8 also illustrates model 850, which is a convolutional neural network. The convolutional neural network is an artificial neural network that features one or more convolutional layers. Convolution layers extract features from an input image. Convolution preserves the relationship between pixels by learning image features using small squares of input data. As shown in model 850, input layer 852 may proceed to convolution blocks 854 and 856 before being output to convolutional output or block 858. In some embodiments, model 850 may itself serve as an input to model 800.

[075] In some embodiments, model 850 may implement an inverted residual structure where the input and output of a residual block (e.g., block 854) are thin bottleneck layers. A residual layer may feed into the next layer and directly into layers that are one or more layers downstream. A bottleneck layer (e.g., block 858) is a layer that contains few neural units compared to the previous layers. Model 850 may use a bottleneck layer to obtain a representation of the input with reduced dimensionality. An example of this is the use of autoencoders with bottleneck layers for nonlinear dimensionality reduction. Additionally, model 850 may remove non-linearities in a narrow layer (e.g., block 858) in order to maintain representational power. In some embodiments, the design of model 850 may also be guided by the metric of computation complexity (e.g., the number of floating point operations). In some embodiments, model 850 may increase the feature map dimension at all units to involve as many locations as possible instead of sharply increasing the feature map dimensions at neural units that perform down sampling. In some embodiments, model 850 may decrease the depth and increase width of residual layers in the downstream direction.

[076] FIG. 9 illustrates a method 900 for analyzing animals with an analyzing system. Method 900 may be executed by a system such as system 100 (e.g., as shown in FIG. 1A-8) and/or other systems. The operations of method 900 presented below are intended to be illustrative. In some embodiments, method 900 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method 900 are illustrated in FIG. 9 and described below is not intended to be limiting.

[077] In some embodiments, method 900 may be implemented, at least in part, in one or more processing devices such as one or more processing devices described herein (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method 900 in response to instructions (e.g., machine readable instructions) stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method 900.

[078] At an operation 902, aquatic animals are received with a tube, and move along a path defined by the tube. The tube is configured to carry the aquatic animal in water from a first location to a second location. In some embodiments, operation 902 comprises actively pumping the aquatic animal, in water, from the first location to the second location in the tube by a pump. However, in some embodiments, non-pumping techniques may be used - gravity based movement as one possible example. The aquatic animal is fully conscious and free of sedatives in the tube (though as described above, partial sedation may be helpful). In some embodiments, operation 902 is performed by a tube the same as or similar to tube 102 (shown in FIG. 1A and described herein), a pump, and/or other components.

[079] In some embodiments, a bypass may be configured to control a flow of water through a portion of the tube corresponding to where the imager (as described herein) operates. Controlling the flow of water through the portion of the tube facilitates using a different diameter for the portion of the tube relative to one or more other portions of the tube, for example. The portion of the tube may be interchangeable such that different tube portions with different diameters may be interchangeably included in the system depending on the flow of water.

[080] Operation 904 comprises separating, with a separator, an aquatic animal from other aquatic animals such that an imager can obtain one or more images of the aquatic animal in the tube without interference from the other aquatic animals. In some embodiments, operation 904 is performed by a separator the same as or similar to separator 106 (shown in FIG. 1A and described herein).

[081] Operation 906 comprises positioning, with a positioner, the aquatic animal in a target orientation in the tube for the imager to obtain the one or more images. In some embodiments, operation 906 is performed by a positioner the same as or similar to positioner 107 (shown in FIG. 1A and described herein).

[082] Operation 908 comprises obtaining, with the imager, the one or more images of the aquatic animal while it is located inside the tube. The imager is configured to obtain the one or more images while the animal moves through the tube. The imager is contactless, configured to image the aquatic animal without contacting the aquatic animal (though the imager may contact the aquatic animal in some embodiments, as described above). In some embodiments, the imager is modular and removable from the system such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals. [083] In some embodiments, the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X- ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI), and/or other imagers. In some embodiments, operation 908 is performed by an imager the same as or similar to imager 104 (shown in FIG. 1A and described herein).

[084] For example at operation 908, an ultrasound image (or set of ultrasound images) of the animal may be obtained with an ultrasound transducer. The ultrasound transducer is configured to obtain the ultrasound image while the animal moves through the tube. In some embodiments, at operation 908, a visual image (or set of visual images) of an animal is obtained as the animal moves past a camera. In some embodiments, the camera is configured to obtain a red green blue (RGB) image set that includes the visual image.

[085] Operation 910 comprises determining, with control circuitry, based on the one or more images, a characteristic of the animal. In some embodiments, the characteristic is associated with performance of the aquatic animal, health and/or welfare of the aquatic animal, phenotypes, and/or other characteristics. In some embodiments, performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, body fat percentage of the aquatic animal, and/or other performance characteristics. In some embodiments, health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, disease resistance, and/or other health and/or welfare characteristics. Disease presence and/or disease resistance may be associated with diseases including BKD, kidney stones, HSMI, HSS, yersinosis, flavobacteriosis, saprolegniosis, CMS, SGPV, vibrio, nodavirus, and/or pasteurellosis. As several additional examples of possible characteristics, the characteristic may be gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, a fat percentage of the aquatic animal, and/or other characteristics. The control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images. The artificial neural network is trained to identify one or more internal and/or external characteristics of the animal based on the one or more images. In some embodiments, operation 910 is performed by control circuitry the same as or similar to control circuitry 109 (shown in FIG. 1A and described herein) and/or control circuitry 600 (shown in FIG. 1A and described herein).

[086] In some embodiments, operations 908 and/ 910 comprise determining, with the control circuitry, a fixed starting point for the one or more images of the aquatic animal. The one or more images are obtained along a scan length that extends from the fixed starting point. The control circuitry may trigger the imager to take the one or more images. For example, the aquatic animal may be a fish, or a crustacean, and the fixed starting point for the one or more images corresponds to a start of a tip of the nose or an end of a tail of the fish and/or the crustacean. As another example, the imager may comprise an ultrasound transducer and a camera, the camera is configured to obtain a red green blue (RGB) image set that includes a visual image, and the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image. The control circuitry may be configured to determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set. [087] In some embodiments, the control circuitry may cause the imager to continuously scan as aquatic animals pass through the tube, into and out of a field of view of the imager. [088] In some embodiments, the imager is configured to move along the tube and/or around the tube to obtain the one or more images of the aquatic animal. The imager may be configured to move around the tube to enhance an angle at which the imaging of the aquatic animal takes place. In some embodiments, the imager moves along the tube to compensate for a speed of the aquatic animal in the tube.

[089] Operation 912 comprises sorting, with a sorter, the aquatic animal into a group. The control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic. In some embodiments, the sorter comprises one or more flaps coupled to the tube and controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal from the tube. In some embodiments, operation 910 is performed by a sorter the same as or similar to sorter 108 (shown in FIG. 1A and described herein).

[090] In some embodiments, a vaccinator may be used to vaccinate the aquatic animal. In some embodiments, vaccination may be in the tube. In some embodiments, vaccination may occur outside of the tube (with the aquatic animal even potentially removed from the water for vaccination in some embodiments). Vaccination may occur after sorting, for example, such that only aquatic animals sorted into one or more specific groups are vaccinated (e.g. to save vaccine and/or other costs from being spent on a group of aquatic animals that may eventually be rejected). In some embodiments, vaccination may occur on its own, without imaging and/or sorting.

[091] In block diagrams such as FIG. 9, illustrated components are depicted as discrete functional blocks, but embodiments are not limited to systems in which the functionality described herein is organized as illustrated. The functionality provided by each of the components may be provided by software or hardware modules that are differently organized than is presently depicted, for example such software or hardware may be intermingled, conjoined, replicated, broken up, distributed (e.g. within a data center or geographically), or otherwise differently organized. The functionality described herein may be provided by one or more processors of one or more computers executing code stored on a tangible, non- transitory, machine readable medium. In some cases, notwithstanding use of the singular term “medium,” the instructions may be distributed on different storage devices associated with different computing devices, for instance, with each computing device having a different subset of the instructions, an implementation consistent with usage of the singular term “medium” herein. In some cases, third party content delivery networks may host some or all of the information conveyed over networks, in which case, to the extent information (e.g., content) is said to be supplied or otherwise provided, the information may be provided by sending instructions to retrieve that information from a content delivery network. [092] It is contemplated that the steps or descriptions of FIG. 9 may be used with any embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 9 may be performed in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in relation to FIGS. 1-8 could be used to perform one or more of the steps in FIG. 9.

[093] Although the present invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but on the contrary, is intended to cover modifications and equivalent arrangements that are within the scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

[094] The present techniques will be better understood with reference to the following enumerated embodiments, which may be combined in any combination:

1. A system for analyzing aquatic animals, comprising: an imager configured to obtain one or more images of an aquatic animal, the aquatic animal carried in water from a first location to a second location, the imager configured to obtain the one or more images while the aquatic animal moves; and control circuitry configured to determine, based on the one or more images, a characteristic of the aquatic animal.

2. The system of embodiment 1, further comprising: a separator configured to separate the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal without interference from the other aquatic animals; and/or a positioner configured to position the aquatic animal in a target orientation for the imager to obtain the one or more images.

3. The system of any of the previous embodiments, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod, the aquatic animal in water is pumped from the first location to the second location by a pump, and the aquatic animal is fully conscious and free of sedatives, or partially sedated to facilitate a measure of calmness during pumping.

4. The system of any of the previous embodiments, further comprising a sorter configured to sort the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic. 5. The system of any of the previous embodiments, wherein the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal.

6. The system of any of the previous embodiments, further comprising a vaccinator configured to vaccinate the aquatic animal.

7. The system of any of the previous embodiments, wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

8. The system of any of the previous embodiments, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

9. The system of any of the previous embodiments, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images.

10. The system of any of the previous embodiments, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes of the aquatic animal.

11. The system of any of the previous embodiments, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

12. The system of any of the previous embodiments, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

13. The system of any of the previous embodiments, wherein disease presence and/or disease resistance is associated with diseases including BKD, kidney stones, HSMI, HSS, yersinosis, flavobacteriosis, saprolegniosis, CMS, SGPV, vibrio, nodavirus, and/or pasteurellosis. 14. The system of any of the previous embodiments, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

15. The system of any of the previous embodiments, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

16. The system of any of the previous embodiments, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

17. The system of any of the previous embodiments, further comprising a bypass configured to control a flow of water through a portion of a channel corresponding to where the imager operates.

18. The system of any of the previous embodiments, wherein controlling the flow of water through the portion of the channel facilitates using a different diameter for the portion of the channel relative to one or more other portions of the channel.

19. The system of any of the previous embodiments, wherein the portion of the channel is interchangeable such that different channel portions with different diameters may be interchangeably included in the system depending on the flow of water.

20. The system of any of the previous embodiments, wherein the imager is modular and removable from the system such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

21. A method for analyzing aquatic animals, comprising: obtaining, with an imager, one or more images of an aquatic animal, the aquatic animal carried in water from a first location to a second location, the imager configured to obtain the one or more images while the aquatic animal moves; and determining, with control circuitry, based on the one or more images, a characteristic of the aquatic animal.

22. The method of embodiment 21, further comprising: separating, with a separator, the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal without interference from the other aquatic animals; and/or positioning, with a positioner, the aquatic animal in a target orientation for the imager to obtain the one or more images.

23. The method of any of the previous embodiments, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod, the aquatic animal in water is pumped from the first location to the second location by a pump, and the aquatic animal is fully conscious and free of sedatives in the tube, or partially sedated to facilitate a measure of calmness during pumping.

24. The method of any of the previous embodiments, further comprising sorting, with a sorter, the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic.

25. The method of any of the previous embodiments, wherein the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal.

26. The method of any of the previous embodiments, further comprising vaccinating the aquatic animal.

27. The method of any of the previous embodiments, wherein the vaccinating is after sorting.

28. The method of any of the previous embodiments, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

29. The method of any of the previous embodiments, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images.

30. The method of any of the previous embodiments, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes, of the aquatic animal. 31. The method of any of the previous embodiments, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

32. The method of any of the previous embodiments, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

33. The method of any of the previous embodiments, wherein disease presence and/or disease resistance is associated with diseases including BKD, kidney stones, HSMI, HSS, yersinosis, flavobacteriosis, saprolegniosis, CMS, SGPV, vibrio, nodavirus, and/or pasteurellosis.

34. The method of any of the previous embodiments, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

35. The method of any of the previous embodiments, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

36. The method of any of the previous embodiments, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

37. The method of any of the previous embodiments, further comprising controlling, with a bypass, a flow of water through a portion of a channel corresponding to where the imager operates.

38. The method of any of the previous embodiments, wherein controlling the flow of water through the portion of the channel facilitates using a different diameter for the portion of the channel relative to one or more other portions of the channel.

39. The method of any of the previous embodiments, wherein the portion of the channel is interchangeable such that different channel portions with different diameters may be interchangeably included in the system depending on the flow of water. 40. The method of any of the previous embodiments, wherein the imager is modular and removable such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

41. A system for analyzing aquatic animals, comprising: an imager configured to obtain one or more images of an aquatic animal located inside a tube, the tube configured to carry the aquatic animal in water from a first location to a second location, the imager configured to obtain the one or more images while the animal moves through the tube; a separator configured to separate the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal in the tube without interference from the other aquatic animals; a positioner configured to position the aquatic animal in a target orientation in the tube for the imager to obtain the one or more images; and control circuitry configured to determine, based on the one or more images, a characteristic of the animal.

42. The system of embodiment 41, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.

43. The system of any of the previous embodiments, further comprising a sorter configured to sort the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic.

44. The system of any of the previous embodiments, wherein the sorter comprises one or more flaps coupled to the tube and controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal from the tube.

45. The system of any of the previous embodiments, further comprising a vaccinator configured to vaccinate the aquatic animal.

46. The system of any of the previous embodiments, wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

47. The system of any of the previous embodiments, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

48. The system of any of the previous embodiments, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputing the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images.

49. The system of any of the previous embodiments, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images.

50. The system of any of the previous embodiments, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes, of the aquatic animal.

51. The system of any of the previous embodiments, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

52. The system of any of the previous embodiments, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

53. The system of any of the previous embodiments, wherein disease presence and/or disease resistance is associated with diseases including BKD, kidney stones, HSMI, HSS, yersinosis, flavobacteriosis, saprolegniosis, CMS, SGPV, vibrio, nodavirus, and/or pasteurellosis.

54. The system of any of the previous embodiments, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

55. The system of any of the previous embodiments, wherein the control circuitry is configured to: determine a fixed starting point for the one or more images of the aquatic animal, wherein the one or more images are obtained along a scan length that extends from the fixed starting point; trigger the imager to take the one or more images; and/or cause the imager to continuously scan as aquatic animals pass through the tube, into and out of a field of view of the imager.

56. The system of any of the previous embodiments, wherein the aquatic animal is a fish, or a crustacean, and the fixed starting point for the one or more images corresponds to a start of a tip of the nose or an end of a tail of the fish and/or the crustacean. 57. The system of any of the previous embodiments, wherein the imager is configured to move along the tube and/or around the tube to obtain the one or more images of the aquatic animal, wherein the imager is configured to move around the tube to enhance an angle at which the imaging of the aquatic animal takes place; and/or wherein the imager moves along the tube to compensate for a speed of the aquatic animal in the tube.

58. The system of any of the previous embodiments, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

59. The system of any of the previous embodiments, wherein the aquatic animal in water is pumped from the first location to the second location in the tube by a pump.

60. The system of any of the previous embodiments, wherein the aquatic animal is fully conscious and free of sedatives in the tube; or partially sedated to facilitate a measure of calmness during pumping.

61. The system of any of the previous embodiments, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

62. The system of any of the previous embodiments, further comprising a bypass configured to control a flow of water through a portion of the tube corresponding to where the imager operates.

63. The system of any of the previous embodiments, wherein controlling the flow of water through the portion of the tube facilitates using a different diameter for the portion of the tube relative to one or more other portions of the tube.

64. The system of any of the previous embodiments, wherein the portion of the tube is interchangeable such that different tube portions with different diameters may be interchangeably included in the system depending on the flow of water.

65. The system of any of the previous embodiments, wherein the imager is modular and removable from the system such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

66. A method for analyzing aquatic animals, comprising: obtaining, with an imager, one or more images of an aquatic animal located inside a tube, the tube configured to carry the aquatic animal in water from a first location to a second location, the imager configured to obtain the one or more images while the animal moves through the tube; separating, with a separator, the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal in the tube without interference from the other aquatic animals; positioning, with a positioner, configured to position the aquatic animal in a target orientation in the tube for the imager to obtain the one or more images; and determining, with control circuitry, based on the one or more images, a characteristic of the animal.

67. The method of embodiment 66, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.

68. The method of any of the previous embodiments, further comprising sorting, with a sorter, the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic.

69. The method of any of the previous embodiments, wherein the sorter comprises one or more flaps coupled to the tube and controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal from the tube.

70. The method of any of the previous embodiments, further comprising vaccinating the aquatic animal in the tube.

71. The method of any of the previous embodiments, wherein the vaccinating is after the sorting.

72. The method of any of the previous embodiments, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

73. The method of any of the previous embodiments, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images.

74. The method of any of the previous embodiments, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images. 75. The method of any of the previous embodiments, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes, of the aquatic animal.

76. The method of any of the previous embodiments, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

77. The method of any of the previous embodiments, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

78. The method of any of the previous embodiments, wherein disease presence and/or disease resistance is associated with diseases including BKD, kidney stones, HSMI, HSS, yersinosis, flavobacteriosis, saprolegniosis, CMS, SGPV, vibrio, nodavirus, and/or pasteurellosis.

79. The method of any of the previous embodiments, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

80. The method of any of the previous embodiments, further comprising: determining, with the control circuitry, a fixed starting point for the one or more images of the aquatic animal, wherein the one or more images are obtained along a scan length that extends from the fixed starting point; triggering, with the control circuitry, the imager to take the one or more images; and/or causing, with the control circuitry, the imager to continuously scan as aquatic animals pass through the tube, into and out of a field of view of the imager.

81. The method of any of the previous embodiments, wherein the aquatic animal is a fish, or a crustacean, and the fixed starting point for the one or more images corresponds to a start of a tip of the nose or an end of a tail of the fish and/or the crustacean.

82. The method of any of the previous embodiments, wherein the imager is configured to move along the tube and/or around the tube to obtain the one or more images of the aquatic animal, wherein the imager is configured to move around the tube to enhance an angle at which the imaging of the aquatic animal takes place; and/or wherein the imager moves along the tube to compensate for a speed of the aquatic animal in the tube. 83. The method of any of the previous embodiments, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

84. The method of any of the previous embodiments, further comprising pumping the aquatic animal, in water, from the first location to the second location in the tube by a pump.

85. The method of any of the previous embodiments, wherein the aquatic animal is fully conscious and free of sedatives in the tube.

86. The method of any of the previous embodiments, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

87. The method of any of the previous embodiments, further comprising controlling, with a bypass, a flow of water through a portion of the tube corresponding to where the imager operates.

88. The method of any of the previous embodiments, wherein controlling the flow of water through the portion of the tube facilitates using a different diameter for the portion of the tube relative to one or more other portions of the tube.

89. The method of any of the previous embodiments, wherein the portion of the tube is interchangeable such that different tube portions with different diameters may be interchangeably included in the system depending on the flow of water.

90. The method of any of the previous embodiments, wherein the imager is modular and removable such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

91. A system for vaccinating aquatic animals, comprising: a vaccinator configured to vaccinate an aquatic animal moving from a first location to a second location, the vaccinator configured to vaccinate the aquatic animal while the aquatic animal moves; wherein the aquatic animal is fully conscious and free of sedatives, or partially sedated to facilitate a measure of calmness during movement.

92. The system of embodiment 91, further comprising an imager configured to obtain one or more images of the aquatic animal, the imager configured to obtain the one or more images while the aquatic animal moves; and control circuitry configured to determine, based on the one or more images, a characteristic of the aquatic animal. 93. The system of any of the previous embodiments, further comprising: a pump configured to pump the aquatic animal from the first location to the second location; a separator configured to separate the aquatic animal from other aquatic animals such that the vaccinator can vaccinate the aquatic animal without interference from the other aquatic animals; and/or a positioner configured to position the aquatic animal in a target orientation for the vaccinator.

94. The system of any of the previous embodiments, further comprising a sorter configured to sort the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic, wherein the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal, and wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

95. The system of any of the previous embodiments, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.

96. A method for vaccinating aquatic animals, comprising: vaccinating, with a vaccinator, an aquatic animal moving from a first location to a second location, the vaccinator configured to vaccinate the aquatic animal while the aquatic animal moves; wherein the aquatic animal is fully conscious and free of sedatives, or partially sedated to facilitate a measure of calmness during movement.

97. The method of embodiment 96, further comprising obtaining, with an imager, one or more images of the aquatic animal, the imager configured to obtain the one or more images while the aquatic animal moves; and determining, with control circuitry, based on the one or more images, a characteristic of the aquatic animal.

98. The method of any of the previous embodiments, further comprising: pumping, with a pump, the aquatic animal from the first location to the second location; separating, with a separator, the aquatic animal from other aquatic animals such that the vaccinator can vaccinate the aquatic animal without interference from the other aquatic animals; and/or positioning, with a positioner, the aquatic animal in a target orientation for the vaccinator.

99. The method of any of the previous embodiments, further comprising sorting, with a sorter, the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic, wherein the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal, and wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

100. The method of any of the previous embodiments, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.

101. A tangible, non-transitory, machine -readable medium storing instructions that, when executed by a data processing apparatus of the control circuitry, cause the control circuitry to perform one or more operations of any of the previous embodiments.

Claims

WHAT IS CLAIMED IS:

2. The system of claim 1, further comprising: a separator configured to separate the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal without interference from the other aquatic animals; and/or a positioner configured to position the aquatic animal in a target orientation for the imager to obtain the one or more images.

3. The system of claim 1, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod, the aquatic animal in water is pumped from the first location to the second location by a pump, and the aquatic animal is fully conscious and free of sedatives during pumping, or partially sedated to facilitate a measure of calmness during pumping.

4. The system of claim 1, further comprising a sorter configured to sort the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic.

5. The system of claim 4, wherein the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal.

6. The system of claim 4, further comprising a vaccinator configured to vaccinate the aquatic animal.

7. The system of claim 6, wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

8. The system of claim 1, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

9. The system of claim 1, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images.

10. The system of claim 1, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes, of the aquatic animal.

11. The system of claim 10, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

12. The system of claim 10, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

13. The system of claim 12, wherein disease presence and/or disease resistance is associated with diseases including bacterial kidney disease (BKD), kidney stones, heart and skeletal muscle inflammation (HSMI), hemorrhagic smolt syndrome (HSS), yersinosis, flavobacteriosis, saprolegniosis, cardiomyopathy syndrome (CMS), salmon gill poxvirus (SGPV), vibrio, nodavirus, and/or pasteurellosis.

14. The system of claim 1, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

15. The system of claim 1, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

16. The system of claim 1, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

17. The system of claim 1, wherein the aquatic animal is located inside a channel, the channel configured to carry the aquatic animal from the first location to the second location, the system further comprising a bypass configured to control a flow of water through a portion of the channel corresponding to where the imager operates.

18. The system of claim 17, wherein controlling the flow of water through the portion of the channel facilitates using a different size for the portion of the channel relative to one or more other portions of the channel.

19. The system of claim 17, wherein the portion of the channel is interchangeable such that different channel portions with different diameters may be interchangeably included in the system depending on the flow of water.

20. The system of claim 1, wherein the imager is modular and removable from the system such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

22. The method of claim 21, further comprising: separating, with a separator, the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal without interference from the other aquatic animals; and/or positioning, with a positioner, the aquatic animal in a target orientation for the imager to obtain the one or more images.

23. The method of claim 21, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod, the aquatic animal in water is pumped from the first location to the second location by a pump, and the aquatic animal is fully conscious and free of sedatives during pumping, or partially sedated to facilitate a measure of calmness during pumping.

24. The method of claim 21, further comprising sorting, with a sorter, the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic.

25. The method of claim 24, wherein the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal.

26. The method of claim 24, further comprising vaccinating the aquatic animal.

27. The method of claim 26, wherein the vaccinating is after sorting.

28. The method of claim 21, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

29. The method of claim 21, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images.

30. The method of claim 21, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes, of the aquatic animal.

31. The method of claim 30, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

32. The method of claim 30, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

33. The method of claim 32, wherein disease presence and/or disease resistance is associated with diseases including bacterial kidney disease (BKD), kidney stones, heart and skeletal muscle inflammation (HSMI), hemorrhagic smolt syndrome (HSS), yersinosis, flavobacteriosis, saprolegniosis, cardiomyopathy syndrome (CMS), salmon gill poxvirus (SGPV), vibrio, nodavirus, and/or pasteurellosis.

34. The method of claim 21, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

35. The method of claim 21, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

36. The method of claim 21, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

37. The method of claim 21, wherein the aquatic animal is located inside a channel, the channel configured to carry the aquatic animal from the first location to the second location, the method further comprising controlling, with a bypass, a flow of water through a portion of the channel corresponding to where the imager operates.

38. The method of claim 37, wherein controlling the flow of water through the portion of the channel facilitates using a different diameter for the portion of the channel relative to one or more other portions of the channel.

39. The method of claim 37, wherein the portion of the channel is interchangeable such that different channel portions with different diameters may be interchangeably included in a system depending on the flow of water.

40. The method of claim 21, wherein the imager is modular and removable such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

41. A system for analyzing aquatic animals, comprising: an imager configured to obtain one or more images of an aquatic animal located inside a channel, the channel configured to carry the aquatic animal in water from a first location to a second location, the imager configured to obtain the one or more images while the aquatic animal moves through the channel; a separator configured to separate the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal in the channel without interference from the other aquatic animals; a positioner configured to position the aquatic animal in a target orientation in the channel for the imager to obtain the one or more images; and control circuitry configured to determine, based on the one or more images, a characteristic of the aquatic animal.

42. The system of claim 41, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.

43. The system of claim 41, further comprising a sorter configured to sort the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic.

44. The system of claim 43, wherein the sorter comprises one or more flaps coupled to the channel and controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal from the channel.

45. The system of claim 44, further comprising a vaccinator configured to vaccinate the aquatic animal.

46. The system of claim 45, wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

47. The system of claim 41, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

48. The system of claim 41, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images.

49. The system of claim 48, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images.

50. The system of claim 41, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes, of the aquatic animal.

51. The system of claim 50, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

52. The system of claim 50, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

53. The system of claim 52, wherein disease presence and/or disease resistance is associated with diseases including bacterial kidney disease (BKD), kidney stones, heart and skeletal muscle inflammation (HSMI), hemorrhagic smolt syndrome (HSS), yersinosis, flavobacteriosis, saprolegniosis, cardiomyopathy syndrome (CMS), salmon gill poxvirus (SGPV), vibrio, nodavirus, and/or pasteurellosis.

54. The system of claim 41, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

55. The system of claim 41, wherein the control circuitry is configured to: determine a fixed starting point for the one or more images of the aquatic animal, wherein the one or more images are obtained along a scan length that extends from the fixed starting point; trigger the imager to take the one or more images; and/or cause the imager to continuously scan as aquatic animals pass through the channel, into and out of a field of view of the imager.

56. The system of claim 55, wherein the aquatic animal is a fish, or a crustacean, and the fixed starting point for the one or more images corresponds to a start of a tip of a nose or an end of a tail of the fish and/or the crustacean.

57. The system of claim 41, wherein the imager is configured to move along the channel and/or around the channel to obtain the one or more images of the aquatic animal, wherein the imager is configured to move around the channel to enhance an angle at which the imaging of the aquatic animal takes place; and/or wherein the imager moves along the channel to compensate for a speed of the aquatic animal in the channel.

58. The system of claim 41, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

59. The system of claim 41, wherein the aquatic animal in water is pumped from the first location to the second location in the channel by a pump.

60. The system of claim 41, wherein the aquatic animal is fully conscious and free of sedatives in the channel; or partially sedated to facilitate a measure of calmness during pumping.

61. The system of claim 41, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

62. The system of claim 41, further comprising a bypass configured to control a flow of water through a portion of the channel corresponding to where the imager operates.

63. The system of claim 62, wherein controlling the flow of water through the portion of the channel facilitates using a different diameter for the portion of the channel relative to one or more other portions of the channel.

64. The system of claim 62, wherein the portion of the channel is interchangeable such that different channel portions with different diameters may be interchangeably included in the system depending on the flow of water.

65. The system of claim 41, wherein the imager is modular and removable from the system such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

66. A method for analyzing aquatic animals, comprising: obtaining, with an imager, one or more images of an aquatic animal located inside a channel, the channel configured to carry the aquatic animal in water from a first location to a second location, the imager configured to obtain the one or more images while the animal moves through the channel; separating, with a separator, the aquatic animal from other aquatic animals such that the imager can obtain the one or more images of the aquatic animal in the channel without interference from the other aquatic animals; positioning, with a positioner, configured to position the aquatic animal in a target orientation in the channel for the imager to obtain the one or more images; and determining, with control circuitry, based on the one or more images, a characteristic of the animal.

67. The method of claim 66, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.

68. The method of claim 66, further comprising sorting, with a sorter, the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic.

69. The method of claim 68, wherein the sorter comprises one or more flaps coupled to the channel and controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal from the channel.

70. The method of claim 69, further comprising vaccinating the aquatic animal in the channel.

71. The method of claim 70, wherein the vaccinating is after the sorting.

72. The method of claim 66, wherein the imager comprises an ultrasound transducer configured to obtain one more ultrasound images of the aquatic animal, a still and/or video camera configured to obtain one or more red green blue (RGB) images of the aquatic animal, an infrared sensor, a near infrared sensor, and ultra violet imager, a hyperspectral camera, an X-ray machine, a computed tomography (CT) scanner, and/or a magnetic resonance imager (MRI).

73. The method of claim 66, wherein the control circuitry is configured to determine the characteristic of the aquatic animal based on the one or more images by inputting the one or more images to an artificial neural network, which is trained to output the characteristic based on the one or more images.

74. The method of claim 73, wherein the artificial neural network is trained to identify one or more external and/or internal characteristics of the animal based on the one or more images.

75. The method of claim 66, wherein the characteristic is associated with performance of the aquatic animal, health and/or welfare, and/or phenotypes, of the aquatic animal.

76. The method of claim 75, wherein performance of the aquatic animal comprises growth, length, weight, depth, height, gender, sexual maturation, condition factor, and/or body fat percentage of the aquatic animal.

77. The method of claim 75, wherein health and/or welfare of the aquatic animal comprises body and/or head internal and/or external deformities, skin lesions, body and/or head wounds, fin erosion, scale loss, cataracts, smoltification status, emaciation, disease presence, and/or disease resistance.

78. The method of claim 77, wherein disease presence and/or disease resistance is associated with diseases including bacterial kidney disease (BKD), kidney stones, heart and skeletal muscle inflammation (HSMI), hemorrhagic smolt syndrome (HSS), yersinosis, flavobacteriosis, saprolegniosis, cardiomyopathy syndrome (CMS), salmon gill poxvirus (SGPV), vibrio, nodavirus, and/or pasteurellosis.

79. The method of claim 66, wherein the characteristic is gender of the aquatic animal, presence of disease in the aquatic animal, size of the aquatic animal, level of maturation of the aquatic animal, presence of bacterial kidney disease in the aquatic animal, presence of kidney stones in the aquatic animal, heart and/or skeletal muscle inflammation in the aquatic animal, presence of body and/or head deformities, status of smoltification, and/or a fat percentage of the aquatic animal.

80. The method of claim 66, further comprising: determining, with the control circuitry, a fixed starting point for the one or more images of the aquatic animal, wherein the one or more images are obtained along a scan length that extends from the fixed starting point; triggering, with the control circuitry, the imager to take the one or more images; and/or causing, with the control circuitry, the imager to continuously scan as aquatic animals pass through the channel, into and out of a field of view of the imager.

81. The method of claim 80, wherein the aquatic animal is a fish, or a crustacean, and the fixed starting point for the one or more images corresponds to a start of a tip of a nose or an end of a tail of the fish and/or the crustacean.

82. The method of claim 66, wherein the imager is configured to move along the channel and/or around the channel to obtain the one or more images of the aquatic animal, wherein the imager is configured to move around the channel to enhance an angle at which the imaging of the aquatic animal takes place; and/or wherein the imager moves along the channel to compensate for a speed of the aquatic animal in the channel.

83. The method of claim 66, wherein the imager comprises an ultrasound transducer and a camera, and wherein the camera is configured to obtain a red green blue (RGB) image set that includes a visual image; the ultrasound transducer is configured to obtain an ultrasound image set of the aquatic animal that includes the ultrasound image; and the control circuitry is configured to: determine a starting point for the ultrasound transducer based on the RGB image set, and determine the characteristic based on the ultrasound image set.

84. The method of claim 66, further comprising pumping the aquatic animal, in water, from the first location to the second location in the channel by a pump.

85. The method of claim 66, wherein the aquatic animal is fully conscious and free of sedatives in the channel; or partially sedated to facilitate a measure of calmness during pumping.

86. The method of claim 66, wherein the imager is contactless, configured to image the aquatic animal without contacting the aquatic animal.

87. The method of claim 66, further comprising controlling, with a bypass, a flow of water through a portion of the channel corresponding to where the imager operates.

88. The method of claim 87, wherein controlling the flow of water through the portion of the channel facilitates using a different diameter for the portion of the channel relative to one or more other portions of the channel.

89. The method of claim 87, wherein the portion of the channel is interchangeable such that different channel portions with different diameters may be interchangeably included in a system depending on the flow of water.

90. The method of claim 66, wherein the imager is modular and removable such that the imager is configured to be coupled to and used with multiple systems for analyzing aquatic animals.

92. The system of claim 91, further comprising an imager configured to obtain one or more images of the aquatic animal, the imager configured to obtain the one or more images while the aquatic animal moves; and control circuitry configured to determine, based on the one or more images, a characteristic of the aquatic animal.

93. The system of claim 92, further comprising: a pump configured to pump the aquatic animal from the first location to the second location; a separator configured to separate the aquatic animal from other aquatic animals such that the vaccinator can vaccinate the aquatic animal without interference from the other aquatic animals; and/or a positioner configured to position the aquatic animal in a target orientation for the vaccinator.

94. The system of claim 92, further comprising a sorter configured to sort the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic, wherein the sorter comprises one or more flaps and controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal, and wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

95. The system of claim 91, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.

97. The method of claim 96, further comprising obtaining, with an imager, one or more images of the aquatic animal, the imager configured to obtain the one or more images while the aquatic animal moves; and determining, with control circuitry, based on the one or more images, a characteristic of the aquatic animal.

98. The method of claim 97, further comprising: pumping, with a pump, the aquatic animal from the first location to the second location; separating, with a separator, the aquatic animal from other aquatic animals such that the vaccinator can vaccinate the aquatic animal without interference from the other aquatic animals; and/or positioning, with a positioner, the aquatic animal in a target orientation for the vaccinator.

99. The method of claim 97, further comprising sorting, with a sorter, the aquatic animal into a group, wherein the control circuitry is configured to control the sorter to sort the aquatic animal into the group based on the characteristic, wherein the sorter comprises one or more flaps controlled by the control circuitry to move between multiple positions such that sorting the aquatic animal into the group comprises moving the one or more flaps to direct the aquatic animal, and wherein the vaccinator is configured to vaccinate the aquatic animal after sorting.

100. The method of claim 96, wherein the aquatic animal is a fish, a shellfish, and/or a cephalopod.