Fish and shellfish anomaly prediction support program, fish and shellfish anomaly prediction support device, fish and shellfish anomaly prediction support method, and recording medium

Abstract

This program provides a simple and easy-to-use program to predict abnormal occurrences in fish and shellfish. [Solution] The aquatic anomaly prediction support device 19 includes an aquatic image acquisition unit, a feature extraction unit, a predictive determination unit, and an output unit. The aquatic image acquisition unit acquires aquatic images of the target aquatic species. The feature extraction unit extracts information for anomaly detection from the aquatic image. The anomaly detection information includes at least one of individual feature information and group feature information. The predictive determination unit determines, based on the anomaly detection information and predictive feature information, whether there are signs of an anomaly occurring in the target aquatic species. The predictive feature information includes at least one of individual feature information and group feature information and information showing the correlation with the mortality rate of the target aquatic species. The output unit outputs the determination result.

Description

Technical Field

[0001] The present disclosure relates to a program for supporting prediction of shellfish abnormalities, a device for supporting prediction of shellfish abnormalities, a method for supporting prediction of shellfish abnormalities, and a recording medium.

Background Art

[0002] From the perspective of ensuring a stable supply of food, shellfish farming is actively carried out. When farming shellfish, if the target shellfish get sick and die, it will cause damage, so various preventive methods have been tried. For example, in Patent Document 1, there is a preventive method for preventing shellfish diseases, which includes a step A of adding a microorganism having an inhibitory effect on the pathogenic microorganism of the disease to a microorganism carrier having a detachment effect of the microorganism, and a step B of putting the microorganism carrier into the water where the shellfish grow to allow the microorganism to colonize on the epidermis of the shellfish. The microorganism added to the microorganism carrier is a microorganism derived from the epidermis of the shellfish and is a microorganism of the genus Pseudomonas.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, even if preventive methods are used, it is impossible to completely prevent the occurrence of abnormalities including diseases. Early detection of the occurrence of abnormalities relies on the experience and intuition of skilled producers, and there is a problem of high dependence on humans.

[0005] Therefore, an object of the present disclosure is to provide a program for supporting prediction of shellfish abnormalities, a device for supporting prediction of shellfish abnormalities, a method for supporting prediction of shellfish abnormalities, and a recording medium that can easily detect signs of the occurrence of shellfish abnormalities.

Means for Solving the Problems

[0006] To achieve the aforementioned objectives, the fish and shellfish anomaly prediction support program of this disclosure is: This includes procedures for acquiring images of marine life, extracting features, predicting potential problems, and outputting results. The aforementioned procedure for acquiring images of marine life involves acquiring images of marine life being managed, The feature extraction procedure extracts information for anomaly detection from the fish and shellfish images, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive action determination procedure determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output procedure outputs the determination result. This is a fish and shellfish anomaly prediction support program that causes a computer to execute each of the above steps.

[0007] The fish and shellfish anomaly prediction support device disclosed herein is It includes a fish and shellfish image acquisition unit, a feature extraction unit, a predictive detection unit, and an output unit. The aforementioned seafood image acquisition unit acquires images of the seafood being managed, The feature extraction unit extracts information for anomaly detection from the fish and shellfish image, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive determination unit determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the managed fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output unit outputs the determination result.

[0008] The method for supporting the prediction of abnormalities in fish and shellfish as disclosed herein is: This process includes a process for acquiring images of fish and shellfish, a feature extraction process, a predictive detection process, and an output process. The aforementioned seafood image acquisition step acquires images of the seafood being managed, The feature extraction step extracts information for anomaly detection from the fish and shellfish image, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive determination step determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output step outputs the determination result. This is a method for supporting the prediction of abnormalities in fish and shellfish, in which a computer performs each of the aforementioned steps.

[0009] The recording medium disclosed herein is This includes procedures for acquiring images of marine life, extracting features, predicting potential problems, and outputting results. The aforementioned procedure for acquiring images of marine life involves acquiring images of marine life being managed, The feature extraction procedure extracts information for anomaly detection from the fish and shellfish images, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive action determination procedure determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output procedure outputs the determination result. This is a computer-readable recording medium that stores a fish and shellfish anomaly prediction support program that causes a computer to execute each of the above procedures. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to easily detect signs of abnormal occurrence of fishery products.

Brief Description of the Drawings

[0011] [Figure 1] FIG. 1 is a block diagram showing a configuration example of a fishery product abnormality prediction support device of the present disclosure. [Figure 2] FIG. 2 is a block diagram showing an example of the hardware configuration of the fishery product abnormality prediction support device of the present disclosure. [Figure 3] FIG. 3 is a flowchart showing an example of processing in the fishery product abnormality prediction support device of the present disclosure.

Embodiments for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiments. In the following figures, the same parts are denoted by the same reference numerals. In addition, unless otherwise specified, the descriptions of each embodiment can be mutually referred to, and the configurations of each embodiment can be combined unless otherwise specified.

[0013] In the present disclosure, fishery products include, for example, fish, crustaceans, etc. The fishery products may be, for example, edible fishery products or non-edible fishery products. The edible fishery products may be, for example, natural fishery products or cultured fishery products. The non-edible fishery products include, for example, ornamental fishery products, but are not limited thereto. The fish are not particularly limited, and examples include sea bass, sardine, eel, tuna, trout, yellowtail, puffer fish, flatfish, sea bream, amberjack, horse mackerel, mackerel, goldfish, medaka, arowana, etc. The crustaceans include, for example, shrimp, crayfish, crabs, etc. The growth stage of the fishery products is not particularly limited, and may be, for example, fry (larva), juvenile fish, immature fish (young fish, juvenile), and adult fish. In the present disclosure, the fishery products may be, for example, one type of fishery product or two or more types of fishery products.

[0014] [Embodiment 1] The seafood anomaly prediction support program of this disclosure is a program that causes a computer to execute a seafood image acquisition procedure, a feature extraction procedure, a predictive indicator determination procedure, and an output procedure. The seafood anomaly prediction support program of this disclosure can also be described as a program that causes a computer to function as the seafood image acquisition procedure, a feature extraction procedure, a predictive indicator determination procedure, and an output procedure. Furthermore, the seafood anomaly prediction support program of this disclosure can also be described as a program that causes a computer to execute each step of the seafood anomaly prediction support method described later.

[0015] The aforementioned procedure for acquiring images of marine life involves acquiring images of marine life being managed, The feature extraction procedure extracts information for anomaly detection from the fish and shellfish images, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive action determination procedure determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output procedure outputs the determination result.

[0016] Each of the aforementioned steps can be reinterpreted, for example, by substituting "step" with "process." The fish and shellfish anomaly prediction support program of this disclosure may also be recorded on, for example, a computer-readable storage medium. The storage medium is, for example, a non-transitory computer-readable storage medium. The storage medium is not particularly limited and includes, for example, random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., SSD (Solid State Drive), USB flash memory, SD / SDHC card, etc.), optical disc (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy disk (FD), etc. The fish and shellfish anomaly prediction support program of this disclosure (for example, also called a programming product or program product) may also be distributed, for example, from an external computer. The "distribution" may be, for example, distribution via a communication network or distribution via a wired connected device. The seafood anomaly prediction support program of this disclosure may be installed and executed on the distributed device, or it may be executed without installation. An information processing device capable of executing the seafood anomaly prediction support program of this disclosure can be, for example, the seafood anomaly prediction support device of this disclosure.

[0017] Next, the configuration of an example of the seafood anomaly prediction support device of this disclosure will be described with reference to Figure 1. Figure 1 is a block diagram showing the configuration of an example of the seafood anomaly prediction support device 10 (hereinafter also referred to as "this device") of this embodiment. As shown in Figure 1, this device 10 includes a seafood image acquisition unit 11, a feature extraction unit 12, a predictive determination unit 13, and an output unit 14. In addition, although not shown, this device 10 may also include, for example, an input unit, an output unit, a display unit and / or a storage unit. The seafood image acquisition unit 11, the feature extraction unit 12, the predictive determination unit 13, and the output unit 14 can each execute, for example, the seafood image acquisition procedure, the feature extraction procedure, the predictive determination procedure, and the output procedure in the seafood anomaly prediction support program of this disclosure.

[0018] The device 10 may be, for example, a single device including the aforementioned parts, or it may be a device in which each of the aforementioned parts can be connected via a communication network. Furthermore, the device 10 can be connected to an external device described later via the communication network. The communication network is not particularly limited and can use a known network, for example, it may be wired or wireless. Examples of the communication network include the Internet, WWW (World Wide Web), telephone lines, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, LPWA, etc. The wireless communication may be in the form of direct communication between devices (Ad Hoc communication), infrastructure communication, indirect communication via an access point, etc. The device 10 may be, for example, incorporated into a server as a system. Furthermore, the device 10 may be, for example, a personal computer (PC, e.g., desktop or notebook), smartphone, tablet terminal, etc., on which the program of this disclosure is installed. The device 10 may also be in the form of cloud computing or edge computing, for example, in which at least one of the aforementioned parts is on a server and the other aforementioned parts are on a terminal.

[0019] Figure 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, memory 102, bus 103, storage device 104, input device 105, output device 106, communication device 107, etc. Each part of the device 10 is interconnected via the bus 103 through its respective interface (I / F).

[0020] The central processing unit 101 operates in coordination with other components via controllers (system controller, I / O controller, etc.) and is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as a fish and shellfish image acquisition unit 11, a feature extraction unit 12, a predictive detection unit 13, and an output unit 14. The device 10 may also include other computing devices such as a CPU, GPU (Graphics Processing Unit), APU (Accelerated Processing Unit), or a combination thereof.

[0021] Bus 103 can also be connected to external devices, for example. Examples of such external devices include a user terminal, an external storage device (such as an external database), a printer, an external input device, an external display device, and an external imaging device. The device 10 can be connected to an external network (the aforementioned communication network) via a communication device 107 connected to bus 103, for example, and can also be connected to other devices via the external network.

[0022] Memory 102 may be, for example, main memory. When the central processing unit 101 performs processing, memory 102 reads various operational programs, such as the program of this disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from memory 102 and executes the program. The main memory may be, for example, RAM (random access memory). Alternatively, memory 102 may be, for example, ROM (read-only memory).

[0023] The storage device 104 is also called a so-called auxiliary storage device, for example, in relation to the main memory (primary memory). As described above, the storage device 104 stores an operating program including the program of this disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing to the recording medium. The recording medium is not particularly limited and may be internal or external, for example, an HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

[0024] In this device 10, the memory 102 and storage device 104 can also store various types of information, such as log information, information obtained from an external database (not shown) or external devices, information generated by this device 10, and information used by this device 10 when executing processing. In this case, the memory 102 and storage device 104 may store, for example, the user information of this device as described above. At least some of the information may be stored on an external server other than the memory 102 and storage device 104, or it may be stored in a distributed manner across multiple terminals using blockchain technology or the like. Furthermore, if this device 10 includes the storage unit, for example, the memory 102 and storage device 104 function as the storage unit.

[0025] The device 10 further includes, for example, an input device 105 and an output device 106. The input device 105 may include, for example, a pointing device such as a touch panel, trackpad, or mouse; a keyboard; imaging means such as a camera or scanner; a card reader such as an IC card reader or magnetic card reader; an audio input means such as a microphone; and so on. The output device 106 may include, for example, a display device such as an LED display or liquid crystal display; an audio output device such as a speaker; a printer; and so on. In this embodiment 1, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated unit, such as a touch panel display.

[0026] An example of processing by the fish and shellfish anomaly prediction support program of this disclosure will be explained in more detail with reference to Figure 3. Figure 3 is a flowchart showing an example of each step of the fish and shellfish anomaly prediction support program of this disclosure. The fish and shellfish anomaly prediction support program of this disclosure is implemented, for example, using the apparatus 10 shown in Figure 1 or Figure 2 on which the fish and shellfish anomaly prediction support program of this disclosure is installed, as follows. Note that the implementation of the fish and shellfish anomaly prediction support program of this embodiment is not limited to the use of the apparatus 10 shown in Figure 1 or Figure 2.

[0027] The aquatic life image acquisition unit 11 acquires an image of the aquatic life being managed (S1, aquatic life image acquisition procedure). The aquatic life image is not particularly limited as long as it is an image of the aquatic life being managed, and may be a video or a still image. The aquatic life image may be an image of the aquatic life being managed underwater, an image of the aquatic life being managed from above the water, or an image of the aquatic life being managed from multiple angles. The aquatic life image may be an image of the aquatic life being managed from above, an image of the aquatic life being managed from below, an image of the aquatic life being managed from the side, or a combination of these. The timing of the acquisition of the aquatic life image is not particularly limited and can be acquired at any time. The applicant has found that, for example, if an abnormality (e.g., disease outbreak) occurs in the aquatic life being managed, there is a possibility that there will be a change in the activity of the aquatic life being managed during feeding. For this reason, the aquatic life image may be an image of the aquatic life being managed during feeding. The aforementioned seafood images may be recorded, for example, in the storage unit of the device 10, or on a recording medium outside the device 10. The seafood image acquisition unit 11 may acquire the seafood images recorded in the storage unit or on an external recording medium, or it may acquire the seafood images from an imaging device that captured the seafood images.

[0028] The aforementioned seafood image may include, for example, seafood identification information that identifies the captured seafood, and information such as the date and time of imaging. The seafood identification information is not particularly limited as long as it is information that can identify the captured seafood. For example, it may be information that identifies the seafood itself, or it may be information that identifies the area (e.g., a fish tank) in which the seafood is contained.

[0029] The feature extraction unit 12 extracts anomaly detection information from the fish and shellfish image (S2, feature extraction procedure). The anomaly detection information is, for example, information for detecting signs of anomaly occurrence in the fish and shellfish being managed. The anomaly detection information includes at least one of individual feature information and group feature information. The feature extraction unit 12 can extract the anomaly detection information from the fish and shellfish image by, for example, a known image processing method that detects and tracks a specified object included in the image. Examples of known image processing methods include, but are not limited to, R-CNN (Regional CNN), YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and DETR (End-to-End Object Detection with Transformers).

[0030] The aforementioned colony feature information is, for example, information indicating the characteristics of a colony (e.g., a school of fish) formed by multiple managed fish and shellfish. The aforementioned colony feature information may include, but is not limited to, at least one selected from a group consisting of, for example, colony formation time, colony area increase time, maximum colony area, and average colony area. The colony formation time is the time from the start of feeding until the managed fish and shellfish begin to form a colony. The colony area increase time is the time from the start of feeding until the area of ​​the colony formed by the managed fish and shellfish reaches its maximum. The maximum colony area is the maximum area of ​​the colony formed by the managed fish and shellfish. The average colony area is the average value of the area of ​​the colony formed by the managed fish and shellfish. The feature extraction unit 12 can extract the colony feature information by, for example, detecting and tracking colonies (schools of fish) from the fish and shellfish images.

[0031] The individual characteristic information includes the number of abnormal individuals included in the managed fish and shellfish. In this case, the feature extraction unit 12 extracts the abnormal individuals from the fish and shellfish image based on abnormality determination information, for example. The abnormal individuals are not particularly limited and include individuals that exhibit predetermined abnormal characteristics, such as abnormal color, abnormal body shape (for example, different fin length or shape from normal individuals, presence or absence of body tumors, presence or absence of scale peeling, presence or absence of bleeding, different gill condition from normal individuals, etc.), and abnormal behavior. The abnormality determination information is, for example, information for determining the abnormal state of the managed fish and shellfish. The abnormality determination information may be, for example, information of a pair of images showing abnormal individuals and the type of abnormality shown in the images, or it may be an image showing a normal individual. If the abnormality determination information is, for example, information of a pair of images showing abnormal individuals and the type of abnormality shown in the images, the feature extraction unit 12 can, for example, extract each individual of the fish and shellfish from the fish and shellfish image and determine whether each individual is an abnormal individual by comparing the images of the extracted individuals with the images showing the abnormal individuals. For example, if discoloration is observed under the jaw of an ayu fish, it may be a sign of coldwater disease. In such cases, a combination of information such as "image of an ayu fish with discoloration under the jaw" and "fish species: ayu, abnormality characteristic: discoloration under the jaw, type of abnormality: coldwater disease" can be used as the abnormality detection information. The abnormality detection information may be recorded in the storage unit of the device 10, or it may be recorded in an external storage device. If the abnormality detection information is, for example, an image showing a normal individual, the feature extraction unit 12 can, for example, extract each individual of the fish and shellfish from the fish and shellfish image, compare the images of the extracted individuals with the image showing a normal individual, and determine that individuals with a matching rate below a threshold are abnormal. The feature extraction unit 12 can then extract the individual characteristic information by, for example, counting the number of abnormal individuals included in the fish and shellfish image.

[0032] Furthermore, the feature extraction unit 12 may, for example, extract the types of abnormalities that may be occurring in the target fish and shellfish, using the abnormality determination information and individual feature information extracted from the fish and shellfish images.

[0033] The premonitory determination unit 13 determines, based on the abnormality detection information and the premonitory characteristic information, whether there are signs of an abnormality occurring in the managed fish and shellfish (S3, premonitory determination procedure). The premonitory determination unit 13 may, for example, determine whether there are signs of death of the managed fish as an abnormality of the managed fish, or whether there are signs of an event that will cause death (for example, the occurrence of disease, a sudden change in the environment (for example, water temperature, water quality), etc.). The premonitory characteristic information includes information showing the correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the managed fish and shellfish. The mortality rate can be calculated, for example, by dividing the amount of death of the managed fish and shellfish by the total number of managed fish and shellfish. The amount of death used to calculate the mortality rate can be, for example, the amount of death during a predetermined period from the occurrence of the individual characteristic information and the group characteristic information. The predetermined period is not particularly limited and can be set to, for example, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 14 days, 30 days, etc. Furthermore, if the managed fish and shellfish are farmed fish and shellfish, the environmental conditions of the farming tanks are generally controlled, so there is a correlation between the mortality rate of the managed fish and shellfish and the occurrence of diseases (fish diseases) in the managed fish and shellfish. For this reason, if the managed fish and shellfish are farmed fish and shellfish, the predictive determination unit 13 can determine, for example, whether there are signs of disease occurring in the managed fish and shellfish based on the abnormality detection information and the predictive characteristic information.

[0034] Specific examples of the aforementioned predictive feature information are shown in Tables 1 and 2 below. Note that the predictive feature information in Tables 1 and 2 below is illustrative and does not limit this disclosure in any way.

[0035] In the predictive characteristic information shown in Table 1 below, the mortality rate for a group (fish school) formation time of 13 seconds is five times higher than the mortality rate for a group formation time of 12 seconds. Therefore, in the predictive characteristic information shown in Table 1 below, a group formation time of 13 seconds is set as the threshold, and the predictive determination unit 13 can determine, for example, in S2, if the group characteristic information is extracted as a group formation time of 13 seconds or more, that there is a sign that an abnormality is about to occur in the managed fish and shellfish. [Table 1]

[0036] In the predictive characteristic information shown in Table 2 below, the mortality rate when the individual characteristic information (number of abnormal individuals) exceeds 110 (110-129) is five times higher than the mortality rate when the number of abnormal individuals is 109 or less (90-109). Therefore, in the predictive characteristic information shown in Table 2 below, a threshold of 110 or more for the individual characteristic information (number of abnormal individuals) is set, and the predictive determination unit 13 can determine, for example, in S2, if the individual characteristic information (number of abnormal individuals) is extracted as exceeding 110, that there is a sign that an abnormality is about to occur in the fish and shellfish being managed. [Table 2]

[0037] The output unit 14 outputs the determination result (S4, output procedure). The output unit 14 may output the determination result to the storage unit of the device 10, for example, or it may output the determination result to an external device 10. In the latter case, the determination result can be output to the manager of the managed fish and shellfish (for example, a fisheries operator, aquaculture operator, seller, etc.). The output unit 14 may also output the fish and shellfish identification information and information such as the date and time of imaging, based on the fish and shellfish image that has been determined to show signs of an impending abnormality.

[0038] The seafood anomaly prediction support method of this disclosure is a method implemented by, for example, replacing each "procedure" in the seafood anomaly prediction support program of this disclosure with a "process". The seafood anomaly prediction support method of this disclosure can be implemented, for example, using the seafood anomaly prediction support device 10 of this disclosure shown in Figure 1 or Figure 2. However, the seafood anomaly prediction support method of this disclosure is not limited to, for example, a method using the seafood anomaly prediction support device 10. The seafood anomaly prediction support method of this disclosure can be implemented by referring to, for example, the descriptions in the seafood anomaly prediction support program and the seafood anomaly prediction support device of this disclosure.

[0039] The seafood anomaly prediction support program of this disclosure includes a seafood image acquisition procedure, a feature extraction procedure, a predictive indicator determination procedure, and an output procedure. The seafood image acquisition procedure acquires images of the target seafood; the feature extraction procedure extracts anomaly detection information from the seafood images; the anomaly detection information includes at least one of individual feature information and group feature information; the predictive indicator determination procedure determines, based on the anomaly detection information and the predictive indicator feature information, whether there are signs of an anomaly occurring in the target seafood; the predictive indicator feature information includes information showing a correlation between at least one of the individual feature information and the group feature information and the mortality rate of the target seafood; and the output procedure can output the determination result. Therefore, according to the seafood anomaly prediction support program of this disclosure, for example, signs of anomalies in the target seafood can be detected without relying on skilled producers.

[0040] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure are possible, as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0041] <Note> Some or all of the above embodiments may be described as follows, but are not limited to the following: (Note 1) This includes procedures for acquiring images of marine life, extracting features, predicting potential problems, and outputting results. The aforementioned procedure for acquiring images of marine life involves acquiring images of marine life being managed, The feature extraction procedure extracts information for anomaly detection from the fish and shellfish images, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive action determination procedure determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output procedure outputs the determination result. A program that assists in predicting abnormalities in marine life by having a computer execute each step. (Note 2) The above procedure for acquiring images of fish and shellfish involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction procedure extracts at least one selected from the group consisting of group formation time, group area increase time, maximum group area, and average group area as the group feature information. The colony formation time is the time from the start of feeding until the managed fish and shellfish begin to form colonies. The colony area increase time is the time from the start of feeding until the area of ​​the colony formed by the managed fish and shellfish reaches its maximum. The aforementioned maximum colony area is the maximum area of ​​the colony formed by the managed fish and shellfish. The average colony area is the average value of the colony area formed by the fish and shellfish being managed, as described in Appendix 1 of the fish and shellfish anomaly prediction support program. (Note 3) The above procedure for acquiring images of fish and shellfish involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction procedure is a fish and shellfish anomaly prediction support program as described in Appendix 1 or 2, which extracts abnormal individuals included in the managed fish and shellfish as individual feature information and counts the number of extracted abnormal individuals. (Note 4) The aforementioned managed fish and shellfish are farmed fish and shellfish, The aforementioned precursor determination procedure is a fish and shellfish abnormality prediction support program according to any one of the appendices 1 to 3, which determines whether there are signs of disease occurring in the fish and shellfish being managed, based on the abnormality detection information and the precursor characteristic information. (Note 5) It includes a fish and shellfish image acquisition unit, a feature extraction unit, a predictive detection unit, and an output unit. The aforementioned seafood image acquisition unit acquires images of the seafood being managed, The feature extraction unit extracts information for anomaly detection from the fish and shellfish image, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive determination unit determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the managed fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output unit is a fish and shellfish abnormality prediction support device that outputs the judgment result. (Note 6) The aforementioned fish and shellfish image acquisition unit acquires images of the fish and shellfish being managed during feeding, The feature extraction unit extracts at least one selected from the group consisting of group formation time, group area increase time, maximum group area, and average group area as group feature information. The colony formation time is the time from the start of feeding until the managed fish and shellfish begin to form colonies. The colony area increase time is the time from the start of feeding until the area of ​​the colony formed by the managed fish and shellfish reaches its maximum. The aforementioned maximum colony area is the maximum area of ​​the colony formed by the managed fish and shellfish. The average colony area is the average value of the area of ​​colonies formed by the fish and shellfish being managed, as described in Appendix 5 of the fish and shellfish anomaly prediction support device. (Note 7) The aforementioned fish and shellfish image acquisition unit acquires images of the fish and shellfish being managed during feeding, The feature extraction unit extracts abnormal individuals included in the managed fish and shellfish as individual feature information, and counts the number of extracted abnormal individuals, as described in Appendix 5 or 6, for the fish and shellfish abnormality prediction support device. (Note 8) The aforementioned managed fish and shellfish are farmed fish and shellfish, The aforementioned indicator determination unit determines, based on the anomaly detection information and the indicator characteristic information, whether there are signs of disease occurring in the managed fish and shellfish, as described in any of appendices 5 to 7, for the fish and shellfish anomaly prediction support device. (Note 9) This process includes a process for acquiring images of fish and shellfish, a feature extraction process, a predictive detection process, and an output process. The aforementioned seafood image acquisition step acquires images of the seafood being managed, The feature extraction step extracts information for anomaly detection from the fish and shellfish image, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive determination step determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output step outputs the determination result. A method for supporting the prediction of abnormalities in fish and shellfish, with each step performed by a computer. (Note 10) The aforementioned fish and shellfish image acquisition step involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction step extracts at least one selected from the group consisting of group formation time, group area increase time, maximum group area, and average group area as the group feature information. The colony formation time is the time from the start of feeding until the managed fish and shellfish begin to form colonies. The colony area increase time is the time from the start of feeding until the area of ​​the colony formed by the managed fish and shellfish reaches its maximum. The aforementioned maximum colony area is the maximum area of ​​the colony formed by the managed fish and shellfish. The method for supporting the prediction of abnormalities in marine life as described in Appendix 9, wherein the average colony area is the average value of the area of ​​colonies formed by the marine life being managed. (Note 11) The aforementioned fish and shellfish image acquisition step involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction step involves extracting abnormal individuals included in the target fish and shellfish as individual feature information, and counting the number of extracted abnormal individuals, as described in Appendix 9 or 10, for the fish and shellfish abnormality prediction support method. (Note 12) The aforementioned managed fish and shellfish are farmed fish and shellfish, The aforementioned precursor determination step determines whether there are signs of disease occurring in the target fish and shellfish, according to any one of the appendices 9 to 11. (Note 13) This includes procedures for acquiring images of marine life, extracting features, predicting potential problems, and outputting results. The aforementioned procedure for acquiring images of marine life involves acquiring images of marine life being managed, The feature extraction procedure extracts information for anomaly detection from the fish and shellfish images, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive action determination procedure determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output procedure outputs the determination result. A computer-readable recording medium containing a program that supports the prediction of abnormalities in marine life, which instructs a computer to execute each step. (Note 14) The above procedure for acquiring images of fish and shellfish involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction procedure extracts at least one selected from the group consisting of group formation time, group area increase time, maximum group area, and average group area as the group feature information. The colony formation time is the time from the start of feeding until the managed fish and shellfish begin to form colonies. The colony area increase time is the time from the start of feeding until the area of ​​the colony formed by the managed fish and shellfish reaches its maximum. The aforementioned maximum colony area is the maximum area of ​​the colony formed by the managed fish and shellfish. The recording medium described in Appendix 14, wherein the average colony area is the average value of the area of ​​colonies formed by the fish and shellfish being managed. (Note 15) The above procedure for acquiring images of fish and shellfish involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction procedure involves extracting abnormal individuals from the managed fish and shellfish as individual feature information, and counting the number of extracted abnormal individuals, as described in Appendix 14 or 15, on the recording medium. (Note 16) The aforementioned managed fish and shellfish are farmed fish and shellfish, The aforementioned predictive action determination procedure determines whether there are signs of disease occurring in the target fish and shellfish based on the anomaly detection information and the predictive characteristic information, and is a recording medium according to any one of appendices 14 to 16. [Industrial applicability]

[0042] According to this disclosure, signs of abnormal occurrences in fish and shellfish can be easily detected. For this reason, this disclosure can be suitably used, for example, in the fisheries industry. [Explanation of symbols]

[0043] 10. Support device for predicting abnormalities in fish and shellfish 11. Fish and shellfish image acquisition unit 12 Feature Extraction Unit 13. Precursor detection unit 14 Output section 101 Central Processing Unit 102 memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication devices

Claims

1. This includes procedures for acquiring images of marine life, extracting features, predicting potential problems, and outputting results. The aforementioned procedure for acquiring images of marine life involves acquiring images of marine life being managed, The feature extraction procedure extracts information for anomaly detection from the fish and shellfish images, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive action determination procedure determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output procedure outputs the determination result. A program that assists in predicting abnormalities in marine life by having a computer execute each step.

2. The above procedure for acquiring images of fish and shellfish involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction procedure extracts at least one selected from the group consisting of group formation time, group area increase time, maximum group area, and average group area as the group feature information. The colony formation time is the time from the start of feeding until the managed fish and shellfish begin to form colonies. The colony area increase time is the time from the start of feeding until the area of ​​the colony formed by the managed fish and shellfish reaches its maximum. The aforementioned maximum colony area is the maximum area of ​​the colony formed by the managed fish and shellfish. The fish and shellfish anomaly prediction support program according to claim 1, wherein the average colony area is the average value of the area of ​​colonies formed by the fish and shellfish being managed.

3. The above procedure for acquiring images of fish and shellfish involves acquiring images of the fish and shellfish being managed during feeding, The feature extraction procedure includes extracting abnormal individuals included in the managed fish and shellfish as individual feature information, and counting the number of extracted abnormal individuals, as described in claim 1 or 2, for the fish and shellfish abnormality prediction support program.

4. The aforementioned managed fish and shellfish are farmed fish and shellfish, The fish and shellfish abnormality prediction support program according to claim 1 or 2, wherein the aforementioned predictive determination procedure determines whether there are signs of disease occurring in the fish and shellfish being managed, based on the abnormality detection information and the predictive characteristic information.

5. It includes a fish and shellfish image acquisition unit, a feature extraction unit, a predictive detection unit, and an output unit. The aforementioned seafood image acquisition unit acquires images of the seafood being managed, The feature extraction unit extracts information for anomaly detection from the fish and shellfish image, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive determination unit determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the managed fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output unit is a fish and shellfish abnormality prediction support device that outputs the judgment result.

6. The aforementioned fish and shellfish image acquisition unit acquires images of the fish and shellfish being managed during feeding, The feature extraction unit extracts at least one selected from the group consisting of group formation time, group area increase time, maximum group area, and average group area as group feature information. The colony formation time is the time from the start of feeding until the managed fish and shellfish begin to form colonies. The colony area increase time is the time from the start of feeding until the area of ​​the colony formed by the managed fish and shellfish reaches its maximum. The aforementioned maximum colony area is the maximum area of ​​the colony formed by the managed fish and shellfish. The fish and shellfish anomaly prediction support device according to claim 5, wherein the average colony area is the average value of the area of ​​colonies formed by the fish and shellfish being managed.

7. The aforementioned fish and shellfish image acquisition unit acquires images of the fish and shellfish being managed during feeding, The feature extraction unit extracts abnormal individuals included in the managed fish and shellfish as individual feature information, and counts the number of extracted abnormal individuals, as described in claim 5 or 6, for the fish and shellfish abnormality prediction support device.

8. The aforementioned managed fish and shellfish are farmed fish and shellfish, The fish and shellfish abnormality prediction support device according to claim 5 or 6, wherein the predictive determination unit determines whether there are signs of disease occurring in the fish and shellfish being managed, based on the abnormality detection information and the predictive characteristic information.

9. This process includes a process for acquiring images of fish and shellfish, a feature extraction process, a predictive detection process, and an output process. The aforementioned seafood image acquisition step acquires images of the seafood being managed, The feature extraction step extracts information for anomaly detection from the fish and shellfish image, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive determination step determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output step outputs the determination result. A method for supporting the prediction of abnormalities in fish and shellfish, with each step performed by a computer.

10. This includes procedures for acquiring images of marine life, extracting features, predicting potential problems, and outputting results. The aforementioned procedure for acquiring images of marine life involves acquiring images of marine life being managed, The feature extraction procedure extracts information for anomaly detection from the fish and shellfish images, The anomaly detection information includes at least one of individual characteristic information and group characteristic information. The aforementioned predictive action determination procedure determines, based on the anomaly detection information and the predictive characteristic information, whether there are any signs of an anomaly occurring in the target fish and shellfish. The aforementioned predictive characteristic information includes information showing a correlation between at least one of the individual characteristic information and the group characteristic information and the mortality rate of the fish and shellfish being managed. The output procedure outputs the determination result. A computer-readable recording medium containing a program that supports the prediction of abnormalities in marine life, which instructs a computer to execute each step.

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