CN112967306A - Method for evaluating harvesting time in prawn culture, terminal equipment and readable storage medium - Google Patents
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Abstract
The application is applicable to the technical field of aquaculture and provides an evaluation method of harvesting opportunity in prawn culture, terminal equipment and a readable storage medium, wherein the method comprises the following steps: acquiring the total weight of the current live shrimps and the total weight of the prior live shrimps in the culture pond; drawing a live shrimp weight change curve corresponding to the culture pond according to the total weight of the current live shrimps and the total weight of the prior live shrimps; estimating the weight peak value and the peak value time of the live shrimps corresponding to the culture pond according to the weight change curve of the live shrimps; and determining the peak time as the optimal harvesting time of the culture pond. According to the method for evaluating the harvesting time in prawn culture, the terminal device and the readable storage medium, the change curve of the total weight of the live prawns in the culture pond is observed, the weight peak value and the peak time of the live prawns are estimated in advance by using data and a mathematical model, the problem that data support is lacked when the harvesting time is determined in the existing prawn culture field is solved, and the culture risk is favorably reduced.
Description
Technical Field
The application belongs to the technical field of aquaculture, and particularly relates to an evaluation method of harvesting opportunity in prawn culture, terminal equipment and a readable storage medium.
Background
In the process of culturing aquatic economic animals such as prawns and the like, the weight of live prawns in a culture pond is generally not evaluated before harvesting, but the harvesting time is freely determined according to the culture experience, and dead prawns in the culture pond are manually picked out during harvesting. The relatively rude breeding mode completely depends on the individual breeding experience of a user, lacks data support, is difficult to master the weight increase and death conditions of the prawns in time in the breeding process, and increases the breeding risk.
Disclosure of Invention
In view of this, embodiments of the present application provide a method for evaluating a harvesting time in prawn cultivation, a terminal device, and a readable storage medium, so as to solve the problem that data support is lacking in determining the harvesting time in the current prawn cultivation field.
According to a first aspect, an embodiment of the present application provides a method for evaluating harvest time in prawn culture, including: acquiring the total weight of the current live shrimps and the total weight of the prior live shrimps in the culture pond; drawing a live shrimp weight change curve corresponding to the culture pond according to the total weight of the current live shrimps and the total weight of the prior live shrimps; estimating the weight peak value and the peak value time of the live shrimps corresponding to the culture pond according to the weight change curve of the live shrimps; determining the peak time as the optimal harvest time of the culture pond.
According to the first aspect, in some embodiments of the present application, the step of obtaining the total weight of the current live shrimps in the culture pond comprises: identifying dead shrimps in the culture pond and counting the number of the dead shrimps; estimating the number of the current live shrimps according to the number of the current dead shrimps; and estimating the total weight of the current live shrimps according to the number of the current live shrimps and the growth stage of the shrimps in the culture pond.
According to the first aspect, in some embodiments of the present application, the step of identifying dead shrimps in the culture pond and counting the current number of dead shrimps comprises: acquiring a current image of the bottom of the culture pond; the current image is a pool bottom image of the culture pool; when the current image is identified to contain the prawn individuals according to a preset machine learning model, the color of each prawn individual in the current image is respectively obtained; and when the color of any prawn individual accords with a first preset color, determining that the prawn individual is a dead prawn.
According to the first aspect, in some embodiments of the present application, the step of separately obtaining the color of each prawn individual in the current image includes: acquiring an edge detection image corresponding to the current image; determining the position of each prawn individual in the current image according to the edge detection image; and determining the color of each prawn individual according to the position of each prawn individual in the current image.
According to the first aspect, in some embodiments of the present application, the method for evaluating harvest timing in prawn farming further comprises: when the color of any prawn individual does not accord with a first preset color, extracting the outline of the prawn individual in the edge detection image; and when the contour is incomplete, determining that any prawn individual is a dead prawn.
According to the first aspect, in some embodiments of the present application, the method for evaluating harvest timing in prawn farming further comprises: when the contour is complete, judging whether the eye of any prawn individual is complete; and when the eyes of any one prawn individual are incomplete, determining that the prawn individual is a dead prawn.
According to the first aspect, in some embodiments of the present application, the step of determining whether the eye of any of the prawn individuals is intact includes: extracting the image of any prawn individual from the current image according to the contour; and when the image of any prawn individual does not contain a second preset color, determining that the prawn individual is a dead prawn.
According to the first aspect, in some embodiments of the present application, the step of determining whether the eye of any of the prawn individuals is intact further includes: when the image of any one prawn individual contains a second preset color, extracting a region with the second preset color; when the area is not a circular or quasi-circular area, determining that the eye of any prawn individual is incomplete.
According to a second aspect, an embodiment of the present application provides a terminal device, including an input end member for obtaining a total weight of current live shrimps and a total weight of previous live shrimps in a culture pond; and the evaluation unit is used for drawing a live shrimp weight change curve corresponding to the culture pond according to the total weight of the current live shrimps and the total weight of the prior live shrimps, estimating the weight peak value and the peak time of the live shrimps corresponding to the culture pond according to the live shrimp weight change curve, and determining the peak time as the optimal harvesting time of the culture pond.
According to a third aspect, an embodiment of the present application provides another terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect or any embodiment of the first aspect when executing the computer program.
According to a fourth aspect, embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the method according to the first aspect or any embodiment of the first aspect.
According to the method for evaluating the harvesting time in prawn culture, the terminal device and the readable storage medium, the change curve of the total weight of the live shrimps in the culture pond is observed, the weight peak value and the peak time of the live shrimps are estimated in advance by using data and a mathematical model, the current situation that the traditional aquaculture excessively depends on artificial experience is changed, the problem that the conventional prawn culture field lacks data support when the harvesting time is determined is solved, and the culture risk is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of a specific example of a method for evaluating harvest timing in prawn culture according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating a specific example of a method for identifying dead shrimps according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a terminal device provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of another terminal device provided in the embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
The embodiment of the application provides a method for evaluating a harvesting opportunity in prawn culture, which can comprise the following steps as shown in fig. 1:
step S101: and acquiring the total weight of the current live shrimps and the total weight of the prior live shrimps in the culture pond.
In one embodiment, the total weight of the live shrimps in the culture pond can be continuously collected in a time sequence according to a preset time interval or a preset time point to form a weight sequence. The weight sequence is dynamically changed, and when the time reaches a weight collection time, the total weight of the current live shrimps in the culture pond is collected, and the total weight of the prior live shrimps corresponding to the culture pond is obtained at the same time, so that the weight sequence containing the total weight of the current live shrimps is formed. Specifically, the weight values of the live shrimps in the weight sequence may be arranged in time order, with the total weight of the current live shrimps being ranked at the end.
In practical application, the total number of the shrimp seedlings put in the culture pond is known, and the number of the young shrimps in the culture pond can be estimated by combining the survival rate of the shrimp seedlings. Dead shrimps are generated almost every day in the culture process from the growth of the young shrimps to the growth of the adult shrimps, and the number of the dead shrimps per day and the accumulated number of the dead shrimps can be recorded. The number of the young shrimps obtained by calculating the survival rate of the young shrimps is different from the accumulated number of the dead shrimps, so that the current number of the live shrimps can be estimated. Specifically, the user can identify the dead shrimps in the culture pond and count the number of the dead shrimps at present, estimate the number of the live shrimps at present according to the number of the dead shrimps at present, and finally estimate the total weight of the live shrimps at present according to the number of the live shrimps at present and the growth stage of the shrimps in the culture pond. Different breeds of prawns correspond to different average weights in different growth stages, and the total weight of the current live prawns in the culture pond can be estimated by using the average weights of the prawns in the growth stages.
Step S102: and drawing a live shrimp weight change curve corresponding to the culture pond according to the total weight of the current live shrimps and the total weight of the prior live shrimps.
For example, the weight sequence curve, i.e., the weight curve of the live shrimps corresponding to the culture pond, can be plotted with time as the horizontal axis and weight as the vertical axis. The weight change curve of the live shrimps can reflect the change trend of the total weight of the live shrimps. Generally, all the weight monitoring values are not required to be listed in the weight sequence, and only the weight of the live shrimps in the last few days and the current weight of the live shrimps are required to be listed, and the weights can reflect the change trend of the total weight of the live shrimps in the culture pond in a more recent period. Premature live shrimp weight is not of great reference for timing of harvest. To avoid excessive data volume and increase computational efficiency, premature weight values should be discarded from the weight sequence.
Step S103: and estimating the weight peak value and the peak time of the live shrimps corresponding to the culture pond according to the weight change curve of the live shrimps.
The weight change curve of the live shrimps can reflect the change trend of the total weight of the live shrimps in the culture pond, and the weight of the live shrimps in the culture pond in a future period of time can be predicted by utilizing the change trend. And estimating the weight peak value and the peak time of the live shrimps corresponding to the culture pond through the predicted weight.
Step S104: and determining the peak time as the optimal harvesting time of the culture pond.
Since the most live shrimps can be harvested at the peak time estimated from the live shrimp weight change curve, the most economic benefit can be obtained, and therefore, the peak time can be set to the optimum harvesting timing of the culture pond.
The identification of dead shrimps is the key content for estimating the weight of live shrimps in the culture pond. Fig. 2 shows a dead shrimp identification method adopted in the embodiment of the application, which can indirectly grasp the number of live shrimps by identifying and counting dead shrimps in the culture pond at regular time. The number of the dead shrimps can be mastered in real time by recognizing the dead shrimps in the culture pond through images before pollution discharge and water change every time. The specific method for identifying dead shrimps is shown in fig. 2:
step S201: and acquiring a current image of the bottom of the culture pond.
Because dead prawn can sink into the bottom of the pool to collect near the drain that sets up at the bottom of the pool along with rivers, consequently, can also set up camera device near the drain to the bottom of the pool image of gathering breed bottom of the pool.
Step S202: and identifying whether the current image contains the prawn individuals or not according to a preset machine learning model. When the current image is identified to contain the prawn individuals according to the preset machine learning model, executing the step S203; and when the current image does not contain the prawn individual according to the preset machine learning model, returning to the step S201.
Step S203: and respectively obtaining the color of each prawn individual in the current image.
As an example, an edge detection image corresponding to the current image may be acquired first; determining the position of each prawn individual in the current image according to the edge detection image; and finally, determining the color of each prawn individual according to the position of each prawn individual in the current image.
Step S204: and judging whether the color of any one prawn individual accords with a first preset color. When the color of any one of the prawn individuals meets the first preset color, executing the step S205; when the color of any prawn individual does not accord with the first preset color, step S206 is executed.
Step S205: and determining any prawn individual as a dead prawn.
The body color of the dead prawns gradually changes from cyan which is translucent to white and then from white to red as time goes by. In one embodiment, both white and red may be set to the first preset color. When the body color of a pair of shrimp individuals is recognized to be changed into white or red, the shrimp individuals can be determined to be dead shrimps.
Step S206: and extracting the contour of any prawn individual in the edge detection image.
When the body color of a certain shrimp individual is identified not to be changed after death, whether the body shape is complete or not can be further examined. For this purpose, the contour of the prawn individual in the edge detection image can be acquired.
Step S207: and judging whether the contour is complete. When the contour is incomplete, step S205 is performed; when the contour is complete, step S208 is performed.
Step S208: and judging whether the eyes of the prawn individuals are complete or not. When the eyes of the prawn individuals are incomplete, executing step S205; when the eyes of the prawn individuals are intact, the process returns to step S201.
In one embodiment, the images of the prawn individuals or the head images of the prawn individuals may be first extracted from the current image according to the contours. When the image of the prawn individual or the head image of the prawn individual does not contain the second preset color, determining that the eye of the prawn individual is incomplete; and when the image of the prawn individual or the head image of the prawn individual contains a second preset color, extracting a region with the second preset color. When the area is a circular or quasi-circular area, determining that the eye of each prawn is complete; when the area is not a circular or quasi-circular area, it is determined that the eyes of the prawn individual are incomplete.
The second preset color can be set to be black or brown because the eyeball of the prawn is black or brown. If the image of the prawn individual or the head image of the prawn individual does not contain any black or brown pixels, the eyes of the prawn individual can be considered to be completely eaten by other live shrimps, and the eyes of the prawn individual are incomplete. If the image of the prawn individual or the head image of the prawn individual contains a black or brown image surface, a region consisting of black and brown pixels can be further extracted from the image of the prawn individual or the head image, and the region is the eye of the prawn individual. The eye of prawn is generally round or elliptical. If the area composed of the black and brown pixels is not a circular or circular-like area, the eyes of the prawn individual can be determined to be eaten by the live prawn in the culture pond, and the eyes of the prawn individual are incomplete.
After the prawn dies, the prawn can show the change of body color after a period of time. The dead shrimp identification is carried out by the body color alone, and a certain time lag may exist. Once the prawns die, other live prawns in the culture pond can bite the bodies of the prawns immediately. Generally, a prawn will eat the eyeballs of a dead prawn first, and after eating all the eyeballs of the dead prawn, the prawn will eat the legs and the tail of the dead prawn, which results in the loss of the body of the dead prawn. Before the body color of the dead shrimps changes, the eyes and bodies of the dead shrimps can be gnawed by the rest of the live shrimps in the culture pond. In order to identify dead shrimps in the current image as early as possible, in addition to the body color, the embodiment of the application also detects whether the body types and the eyes of the individual shrimps are complete or not. When any one of the body color, the body type integrity or the eye integrity of the prawn individual is changed, the prawn individual can be determined to die.
According to the method for evaluating the harvesting time in prawn culture, the change curve of the total weight of live prawns in the culture pond is observed, the weight peak value and the peak time of the live prawns are estimated in advance by using data and a mathematical model, the current situation that the traditional aquaculture excessively depends on artificial experience is changed, the problem that the conventional prawn culture field lacks data support when the harvesting time is determined is solved, and the culture risk is favorably reduced.
Aiming at the identification of dead prawns in the culture pond, the embodiment of the application utilizes machine vision to acquire the current image of the bottom of the culture pond in real time, identifies the dead prawns in the culture pond through an image processing technology, further monitors the dead prawns in the culture pond, indirectly masters the health general of the prawn groups and the quantity of the existing live prawns in the culture pond, changes the problems that the traditional prawn culture completely depends on the personal culture experience of a user, lacks data support and the like, and can help the user master the condition of the dead prawns in real time in the culture process.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
The embodiment of the present application further provides a terminal device, as shown in fig. 3, the terminal device may include an input unit 301 and an evaluation unit 302.
Specifically, the input unit 301 is used for acquiring the total weight of the current live shrimps and the total weight of the prior live shrimps in the culture pond; the corresponding working process can be referred to the description of step S101 in the above method embodiment.
The evaluation unit 302 is configured to draw a live shrimp weight change curve corresponding to the culture pond according to the total weight of the current live shrimps and the total weight of the previous live shrimps, estimate a live shrimp weight peak value and a peak time corresponding to the culture pond according to the live shrimp weight change curve, and determine the peak time as an optimal harvesting time of the culture pond; the corresponding working process can be referred to the description of step S101 to step S104 in the above method embodiment.
In addition, the evaluation unit 302 is also used for identifying dead shrimps in the culture pond; the corresponding working process can be referred to the description of step S201 to step S208 in the above method embodiment.
Fig. 4 is a schematic diagram of another terminal device provided in an embodiment of the present application. As shown in fig. 4, the terminal device 400 of this embodiment includes: a processor 401, a memory 402 and a computer program 403, such as an evaluation program of the timing of harvesting in prawn farming, stored in said memory 402 and executable on said processor 401. The processor 401, when executing the computer program 403, implements the steps in the above-described embodiments of the method for evaluating the harvest timing in prawn farming, such as the steps shown in fig. 1. Alternatively, the processor 401 implements the functions of the modules/units in the above-described device embodiments when executing the computer program 403.
The computer program 403 may be partitioned into one or more modules/units that are stored in the memory 402 and executed by the processor 401 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program 403 in the terminal device 400. For example, the computer program 403 may be partitioned into a synchronization module, a summarization module, an acquisition module, a return module (a module in a virtual device).
The terminal device 400 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 401, a memory 402. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 400 and does not constitute a limitation of terminal device 400 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the terminal device 400, such as a hard disk or a memory of the terminal device 400. The memory 402 may also be an external storage device of the terminal device 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 400. Further, the memory 402 may also include both an internal storage unit and an external storage device of the terminal device 400. The memory 402 is used for storing the computer programs and other programs and data required by the terminal device. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the scope of protection of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions are intended to be included within the scope of the present application without departing from the spirit and scope of the present application.
Claims (10)
1. A method for evaluating harvesting time in prawn culture is characterized by comprising the following steps:
acquiring the total weight of the current live shrimps and the total weight of the prior live shrimps in the culture pond;
drawing a live shrimp weight change curve corresponding to the culture pond according to the total weight of the current live shrimps and the total weight of the prior live shrimps;
estimating the weight peak value and the peak value time of the live shrimps corresponding to the culture pond according to the weight change curve of the live shrimps;
determining the peak time as the optimal harvest time of the culture pond.
2. The method of claim 1, wherein the step of obtaining the total weight of the live shrimps in the pond comprises:
identifying dead shrimps in the culture pond and counting the number of the dead shrimps;
estimating the number of the current live shrimps according to the number of the current dead shrimps;
and estimating the total weight of the current live shrimps according to the number of the current live shrimps and the growth stage of the shrimps in the culture pond.
3. The method for evaluating timing of harvest in prawn farming of claim 2, wherein said step of identifying dead shrimps in said farming pond and counting the current number of dead shrimps comprises:
acquiring a current image of the bottom of the culture pond; the current image is a pool bottom image of the culture pool;
when the current image is identified to contain the prawn individuals according to a preset machine learning model, the color of each prawn individual in the current image is respectively obtained;
and when the color of any prawn individual accords with a first preset color, determining that the prawn individual is a dead prawn.
4. The method for evaluating harvesting time in prawn farming of claim 3, wherein the step of separately obtaining the color of each prawn individual in the current image comprises:
acquiring an edge detection image corresponding to the current image;
determining the position of each prawn individual in the current image according to the edge detection image;
and determining the color of each prawn individual according to the position of each prawn individual in the current image.
5. The method of assessing harvest timing in prawn farming of claim 4, wherein the method of assessing harvest timing in prawn farming further comprises: when the color of any prawn individual does not accord with a first preset color, extracting the outline of the prawn individual in the edge detection image;
and when the contour is incomplete, determining that any prawn individual is a dead prawn.
6. The method of assessing harvest timing in prawn farming of claim 5, wherein the method of assessing harvest timing in prawn farming further comprises: when the contour is complete, judging whether the eye of any prawn individual is complete;
and when the eyes of any one prawn individual are incomplete, determining that the prawn individual is a dead prawn.
7. The method for evaluating the harvest time in prawn farming according to claim 6, wherein the step of judging whether the eye of any prawn individual is intact comprises:
extracting the image of any prawn individual from the current image according to the contour;
and when the image of any prawn individual does not contain a second preset color, determining that the prawn individual is a dead prawn.
8. The method for evaluating the harvest time in prawn farming according to claim 7, wherein the step of determining whether the eye of any prawn individual is intact further comprises:
when the image of any one prawn individual contains a second preset color, extracting a region with the second preset color;
when the area is not a circular or quasi-circular area, determining that the eye of any prawn individual is incomplete.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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