CN114532273A - Non-invasive active feeding method and system for recirculating aquaculture fish - Google Patents

Non-invasive active feeding method and system for recirculating aquaculture fish Download PDF

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CN114532273A
CN114532273A CN202210230904.8A CN202210230904A CN114532273A CN 114532273 A CN114532273 A CN 114532273A CN 202210230904 A CN202210230904 A CN 202210230904A CN 114532273 A CN114532273 A CN 114532273A
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赵建
魏丹
文彦慈
朋泽群
叶章颖
刘鹰
朱松明
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    • A—HUMAN NECESSITIES
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Abstract

The invention discloses a non-invasive active feeding method and a system for recirculating aquaculture fishes. The system mainly utilizes machine vision and a deep learning algorithm to analyze the community level of the recirculating aquaculture fishes, then couples the community level of the fish and the characteristics of the swimming strategy among individuals, realizes the representation of the real-time ingestion desire of the fish, and further judges when to trigger feeding. The system disclosed by the invention is simple in structure, simple, convenient and effective in method, and capable of realizing real-time quantification of the ingestion desire of cultured fishes under a non-invasive condition, making up for the defects of a traditional timed feeding (passive feeding) mode, ensuring the ingestion welfare of cultured objects to the greatest extent, improving the feed efficiency and being beneficial to maximization of culture benefits.

Description

Non-invasive active feeding method and system for recirculating aquaculture fish
Technical Field
The invention belongs to the field of circulating water intelligent culture, relates to an intelligent feeding technology suitable for a circulating water culture mode, and particularly relates to a non-invasive active feeding method and system for circulating water cultured fishes.
Background
The aquaculture advocates high-efficiency feeding, and the key point is to feed a proper amount of feed in a specific time period so as to meet the nutrient substances required by a specific growth rate of a cultured object. Most of the current research on efficient feeding is based on the theoretical feeding rhythm of a cultured object, namely, timed feeding (passive feeding); in fact, the rhythm of fish ingestion is influenced by multiple factors such as the culture environment, the size of the individual, and other individuals in the population. Therefore, active feeding according to the real-time ingestion desire degree of the fishes is the key for ensuring accurate and efficient feeding.
The existing research utilizes fish learning capacity to combine with a Demand-feeding system to realize self-Demand feeding of the cultured objects, or realizes real-time automatic feeding of the cultured objects by analyzing the active aggregation degree of the fishes in a specified feeding area, however, the method needs to train the cultured objects for a long time in advance and is mainly used for experimental research; and the Demand-feeding based on Demand-feeding can cause the continuous excitation and alertness of fishes due to frequent feeding stimulation, and then the cortisol content in the body is increased, thus influencing the welfare of the fishes. Research shows that decision compromise can be achieved among the individual animal communities according to respective requirements, and the community level richness influences the generation of the decision compromise. This phenomenon is also reflected in fish stocks: different individuals can adjust the positions and behaviors of the individuals in the fish school according to the self nutrition conditions, and the behaviors are influenced by the social level of the individuals; also, as a result, individuals of different community classes in a fish population do not have the same characteristic behavior at similar levels of desire to eat. Therefore, the method is theoretically feasible for representing the integral ingestion desire degree of the fish school by utilizing the fish community grade and the inter-individual swimming strategy characteristic.
On the basis of the background, the invention provides a non-invasive active feeding system for recirculating aquaculture fish, which analyzes the community level of the recirculating aquaculture fish by using machine vision and a deep learning algorithm, then couples the community level of the fish and the characteristics of a swimming strategy among individuals, realizes the representation of real-time ingestion desire of the fish, and further determines when to trigger feeding. The system disclosed by the invention is simple in structure, simple, convenient and effective in method, capable of realizing real-time quantification of the ingestion desire of the cultured fishes under non-invasive conditions, making up for the defects of the traditional timed feeding (passive feeding) mode, ensuring the ingestion welfare of cultured objects to the greatest extent, improving the feed efficiency and facilitating maximization of the culture benefit.
Disclosure of Invention
The invention aims to provide a non-invasive active feeding system for recirculating aquaculture fishes, which can realize accurate representation of real-time ingestion desire of fish schools according to the swimming strategy variation characteristics among individuals with different community levels of the fish schools and provide good technical support for intelligent feeding of recirculating aquaculture fishes.
The technical scheme adopted by the invention is as follows:
a non-invasive active feeding method and system for recirculating aquaculture fishes are disclosed.
The system can comprise a culture pond, a depth camera, a server, a light supplement lamp, a feeder and the like; the depth camera is arranged right above the culture pond and is connected with the server; meanwhile, the output end of the server is respectively connected with the light supplementing lamp and the feeder.
By applying the active feeding system, the characterization of the real-time ingestion desire of the fish school in the circulating water aquaculture can be realized by analyzing the swimming strategy variation characteristics among individuals with different community grades of the fish school, and then the time for feeding is judged. The method specifically comprises the following steps:
(1) the server triggers the depth camera to read the real-time picture, and calculates the individual length (L) and thickness (T) information of the fish school in the current culture pond by using a deep learning algorithm; then, carrying out normalization on the individual body length information of the fish school: when Ti≤0.08LiWhen the temperature of the water is higher than the set temperature,
Figure BDA0003540479120000021
when Ti＞0.08LiWhen the temperature of the water is higher than the set temperature,
Figure BDA0003540479120000022
wherein i is more than or equal to 1 and less than or equal to N, N is the number of the objects cultured in the current culture pond, and LiAnd TiBody length and body thickness, L, of the ith subjectmaxAnd LminRespectively representing the longest body length and the shortest body length of a culture object in the current culture pond;
(2) based on the normalized fish population individual length information, quantifying the fish population individual community level: high community-level individuals: l is more than or equal to 0.7 and less than or equal to 1; second, the community level individuals: l is more than 0.3 and less than 0.7; low community-level individuals: l is more than 0 and less than or equal to 0.3;
(3) the steps (1) and (2) are performed every n days;
(4) the method comprises the following steps of (1) representing the real-time appetite degree of a fish school by utilizing the swimming strategy change characteristics among individuals with different community levels of the fish school:
the real-time fish feeding desire is expressed as follows:
Figure BDA0003540479120000031
wherein, A is the calculation of a multiplied by V in the time period of sampling tp+a×Vs+b×DnndAverage value of, e.g. each timeSampling 5 minutes every 25 minutes; within 5 minutes of sampling time period, taking 1 frame per second for calculation; a. thefullCalculating AxV for t time period after feeding for satiationp+a×Vs+b×DnndAnd has an average value of:
1) parameter(s)
Figure BDA0003540479120000032
Wherein L ishAnd LlAverage body length, V, of high and low community-level individuals, respectivelyflowThe current average flow velocity of the water body in the culture pond;
2) variables of
Figure BDA0003540479120000033
Wherein
Figure BDA0003540479120000034
Figure BDA0003540479120000035
Dh、DmAnd DlRespectively representing the Euclidean distance average value L from the mass center of the individuals with high community level, the individuals with medium community level and the individuals with low community level to the geometric central point of the water surface coverage area of the feedmAverage body length of individuals of middle-community level, Nh、NmAnd NlRespectively representing the number of individuals with high community level, individuals with medium community level and individuals with low community level, N ═ Nh+Nm+Nl；
3) Variables of
Figure BDA0003540479120000036
Wherein
Figure BDA0003540479120000037
Dhm、DmmAnd DlmRespectively representing the moving distance of the space geometric center of the high-community-level individual, the medium-community-level individual and the low-community-level individual in the current sampling time periodGeometric center of a small group formed by individuals with a specific community grade;
4) variables of
Figure BDA0003540479120000041
Wherein
Figure BDA0003540479120000042
Figure BDA0003540479120000043
Dm-hRepresents the closest distance, D, between the individuals with high social level and the individuals with medium social levell-mRepresenting the closest distance, D, between the middle and low community-level individualsh'、Dm' and Dl' are the average values of the spatial distances between different individuals and the nearest same-level individuals in the high-community-level individuals, the medium-community-level individuals and the low-community-level individuals respectively;
5) variable AfullIs a x Vp+a×Vs+b×DnndCalculating to obtain an average value in a time period of 25-30 minutes after the feeding;
6) step 5) executing once every n days;
(5) when Appetite is more than or equal to 0.75, the server triggers the feeder to feed;
(6) after the feeding is finished, the operation is repeated from the step (4).
The invention has the advantages that;
the non-invasive active feeding system for the recirculating aquaculture fishes is simple in structure and accurate and effective in method, and the representation of the real-time ingestion desire degree of the fish school is calculated based on the swimming strategy change characteristics among individuals of different community grades of the fish school. The system can realize real-time quantification of the ingestion desire of the cultured fishes under a non-invasive condition, makes up the defects of the traditional timing feeding (passive feeding) mode, ensures the ingestion welfare of cultured objects to the maximum extent, improves the feed efficiency, and is beneficial to the maximization of the culture benefit.
Drawings
FIG. 1 is a schematic diagram of a non-invasive active recirculating aquaculture fish feeding system.
In the figure: 1-a culture pond; 2-a depth camera; 3-a server; 4-a light supplement lamp; 5-a feeder.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Referring to fig. 1, a concrete structure example of a non-invasive active feeding system for recirculating aquaculture fish of the invention comprises a culture pond 1, a depth camera 2, a server 3, a light supplement lamp 4 and a feeder 5; the depth camera 2 is arranged right above the culture pond 1 and is connected with the server 3; meanwhile, the output end of the server 3 is respectively connected with the light supplement lamp 4 and the feeder 5.
The device is applied to active feeding of the recirculating aquaculture, and one example of the decision method comprises the following steps:
(1) the server triggers the depth camera to read the real-time picture, and calculates the individual length (L) and thickness (T) information of the fish school in the current culture pond by using a deep learning algorithm; then, carrying out normalization on the individual body length information of the fish school: when Ti≤0.08LiWhen the temperature of the water is higher than the set temperature,
Figure BDA0003540479120000051
when Ti＞0.08LiWhen the temperature of the water is higher than the set temperature,
Figure BDA0003540479120000052
wherein i is more than or equal to 1 and less than or equal to N, N is the number of the objects cultured in the current culture pond, and LiAnd TiBody length and body thickness, L, of the ith subjectmaxAnd LminRespectively representing the longest body length and the shortest body length of a culture object in the current culture pond;
(2) based on the normalized fish population individual length information, quantifying the fish population individual community level: high community-level individuals: l is more than or equal to 0.7 and less than or equal to 1; second, the community level individuals: l is more than 0.3 and less than 0.7; low community-level individuals: l is more than 0 and less than or equal to 0.3;
(3) the steps (1) and (2) are performed every 3 days;
(4) the method comprises the following steps of (1) representing the real-time appetite degree of a fish school by utilizing the swimming strategy change characteristics among individuals with different community levels of the fish school:
Figure BDA0003540479120000053
wherein A is the calculation of a × V in the sampling time periodp+a×Vs+b×DnndIn this example, samples were taken at intervals of 25 minutes for 5 minutes; within 5 minutes of sampling time period, taking 1 frame per second for calculation; a. thefullCalculating AxV for the time period after feeding for satietyp+a×Vs+b×DnndWherein:
1) parameter(s)
Figure BDA0003540479120000054
Wherein L ishAnd LlAverage body length, V, of high and low community-level individuals, respectivelyflowThe average flow speed of the water body of the current culture pond is obtained;
2) variables of
Figure BDA0003540479120000055
Wherein
Figure BDA0003540479120000056
Figure BDA0003540479120000061
Dh、DmAnd DlRespectively representing the Euclidean distance average value L from the mass center of the individuals with high community level, the individuals with medium community level and the individuals with low community level to the geometric central point of the water surface coverage area of the feedmAverage body length of individuals of middle-community level, Nh、NmAnd NlRespectively representing the number of individuals with high community level, individuals with medium community level and individuals with low community level, N ═ Nh+Nm+Nl；
3) Variables of
Figure BDA0003540479120000062
Wherein
Figure BDA0003540479120000063
Dhm、DmmAnd DlmRespectively representing the moving distances of the space geometric centers of the high-community-level individual, the medium-community-level individual and the low-community-level individual in the current sampling time period;
4) variables of
Figure BDA0003540479120000064
Wherein
Figure BDA0003540479120000065
Figure BDA0003540479120000066
Dm-hRepresenting the closest distance, D, between the high and medium community-level individualsl-mRepresenting the closest distance, D, between the middle and low community-level individualsh'、Dm' and Dl' are the average values of the spatial distances between different individuals and the nearest same-level individuals in the high-community-level individuals, the medium-community-level individuals and the low-community-level individuals respectively;
5) variable AfullIs a x Vp+a×Vs+b×DnndCalculating to obtain an average value in a time period of 25-30 minutes after the feeding;
6) step 5) is executed once every 3 days;
(5) when Appetite is more than or equal to 0.75, the server triggers the feeder to feed;
(6) after the feeding is finished, the operation is repeated from the step (4).
The system disclosed by the invention is simple in structure, simple, convenient and effective in method, and capable of realizing real-time quantification of appetite expectation of cultured fishes under a non-invasive condition, making up for the defects of a traditional timed feeding (passive feeding) mode, ensuring feeding welfare of cultured objects on a great extent, improving feed efficiency and being beneficial to maximization of culture benefits.
The above disclosure is only for the specific embodiment of the present invention, but the present invention is not limited thereto, and it should be understood by those skilled in the art that the modifications made without departing from the present invention shall fall within the protection scope of the present invention.

Claims (6)

1. A non-invasive active feeding method for fish cultured by circulating water is characterized in that,
firstly, analyzing the community grade of the recirculating aquaculture fishes by using a machine vision and deep learning algorithm;
then, coupling the fish community grade and the inter-individual swimming strategy characteristics, and representing the real-time ingestion desire of the fish;
and judging whether to trigger feeding according to the characterization result.
2. The active feeding method of non-invasive recirculating aquaculture fish as claimed in claim 1, wherein analyzing the community level of recirculating aquaculture fish by using machine vision and deep learning algorithm comprises:
(1) the depth camera reads the real-time picture, and calculates the individual body length L and body thickness T information of the fish school in the current culture pond by using a deep learning algorithm; then, carrying out normalization on the individual body length information of the fish school: when Ti≤0.08LiWhen the temperature of the water is higher than the set temperature,
Figure FDA0003540479110000011
when Ti＞0.08LiWhen the temperature of the water is higher than the set temperature,
Figure FDA0003540479110000012
wherein i is more than or equal to 1 and less than or equal to N, N is the number of the objects cultured in the current culture pond, and LiAnd TiBody length and body thickness, L, for the ith subjectmaxAnd LminRespectively representing the longest length and the shortest length of the cultured objects in the current culture pond;
(2) based on the normalized fish population individual length information, quantifying the fish population individual community level: high community-level individuals: l is more than or equal to 0.7 and less than or equal to 1; second, the community level individuals: l is more than 0.3 and less than 0.7; low community-level individuals: l is more than 0 and less than or equal to 0.3.
3. The active feeding method of non-invasive recirculating aquaculture fish as claimed in claim 1, wherein the real-time feeding desire of the fish is characterized by coupling the community level of the fish and the inter-individual swimming strategy characteristics, specifically:
the real-time fish feeding desire is expressed as follows:
Figure FDA0003540479110000013
wherein, A is the calculation of a multiplied by V in the time period of sampling tp+a×Vs+b×DnndAverage value of (A)fullCalculating a x V within a time period t after feeding for satietyp+a×Vs+b×DnndAnd has an average value of:
1) parameter(s)
Figure FDA0003540479110000021
Wherein L ishAnd LlAverage body length, V, of high and low community-level individuals, respectivelyflowThe current average flow velocity of the water body in the culture pond;
2) variables of
Figure FDA0003540479110000022
Wherein
Figure FDA0003540479110000023
Figure FDA0003540479110000024
Dh、DmAnd DlRespectively representing the mean value L of Euclidean distances from the centroids of individuals with high community level, individuals with medium community level and individuals with low community level to the geometric central point of the water surface coverage area of the feedmAverage body length of individuals of middle-community level, Nh、NmAnd NlRespectively representing the number of individuals with high community level, individuals with medium community level and individuals with low community level;
3) variables of
Figure FDA0003540479110000025
Wherein
Figure FDA0003540479110000026
Dhm、DmmAnd DlmRespectively representing the moving distances of the space geometric centers of the high-community-level individual, the medium-community-level individual and the low-community-level individual in the current sampling time period;
4) variables of
Figure FDA0003540479110000027
Wherein
Figure FDA0003540479110000028
Figure FDA0003540479110000029
Dm-hRepresenting the closest distance, D, between the high and medium community-level individualsl-mRepresenting the closest distance, D, between the middle and low community-level individualsh'、Dm' and Dl' is the average value of the spatial distance between different individuals and the nearest adjacent individuals with the same level in the individuals with high community level, the individuals with medium community level and the individuals with low community level respectively.
4. A non-invasive active feeding method for recirculating aquaculture fish as claimed in claim 3, wherein said recirculating aquaculture fish are ranked every n days and a isfullAn update is performed.
5. The active feeding method for the non-invasive recirculating aquaculture fish as claimed in claim 3, wherein the feeding is triggered when Appetite is greater than or equal to 0.75.
6. A non-invasive active feeding system for recirculating aquaculture fishes is characterized by comprising a depth camera, a server and a feeding machine; the depth camera is arranged above the culture pond and is connected with the server; with the output of the server connected to a feeder, the system operating according to the method of any one of claims 1 to 5.
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Citations (2)

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CN110089477A (en) * 2019-05-23 2019-08-06 浙江大学 A kind of fish welfare intelligent cultivation system and method for circulating water cultivation mode
CN113749030A (en) * 2021-09-09 2021-12-07 浙江大学 Fish welfare self-adaptive feeding system suitable for recirculating aquaculture mode

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110089477A (en) * 2019-05-23 2019-08-06 浙江大学 A kind of fish welfare intelligent cultivation system and method for circulating water cultivation mode
CN113749030A (en) * 2021-09-09 2021-12-07 浙江大学 Fish welfare self-adaptive feeding system suitable for recirculating aquaculture mode

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