A benthic organism individual biomass statistical method, system, device and medium

Abstract

The application discloses a benthic organism individual biomass statistical method, system, device and medium. The method extracts the outermost contour perimeter, internal cavity contour perimeter, outermost contour containing area and internal cavity contour area of a to-be-counted individual from a to-be-counted individual image. The net perimeter of the to-be-counted individual is calculated based on the outermost contour perimeter and the internal cavity contour perimeter of the to-be-counted individual. The net area of the to-be-counted individual is calculated according to the outermost contour containing area and the internal cavity contour area of the to-be-counted individual. A plurality of candidate indexes are calculated based on the net perimeter and the net area of the to-be-counted individual. The core index is determined according to the plurality of candidate indexes. The individual biomass of the to-be-counted individual is calculated based on the core index, the net perimeter and the net area. The net morphological parameters are extracted, the candidate indexes and the core index are screened, the benthic organism volume is fitted, and the individual biomass statistical precision and efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of biological monitoring technology, and in particular to a method, system, device and medium for statistical analysis of individual benthic organism biomass. Background Technology

[0002] Benthic animals are the core indicator organisms for water environment monitoring. Their individual biomass data is a key foundation for ecological research and intelligent water ecology monitoring. In particular, benthic organisms such as curved morphology (e.g., caddisfly larvae) and snails (e.g. turnip snails) have complex morphologies (three-dimensional curvature, internal cavities, irregular body shape), and there is an urgent need for the statistical analysis of individual benthic organism biomass in the field of aquatic organism monitoring.

[0003] Existing methods for calculating the biomass of benthic organisms include manual weighing and two-dimensional image analysis. Manual weighing is cumbersome, inefficient, and prone to damaging soft samples, leading to errors. Two-dimensional image analysis relies on conventional parameters such as body length, area, and equivalent sphere diameter for modeling. The measurement results fluctuate greatly due to variations in curvature and closed-loop structures caused by individual activity, making it impossible to guarantee model accuracy. Consequently, the accuracy and efficiency of benthic organism biomass statistics are insufficient. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0005] The main objective of this disclosure is to propose a method, system, device, and storage medium for calculating the individual biomass of benthic organisms. This method can improve the accuracy and efficiency of individual biomass statistics by extracting net morphological parameters, constructing candidate indicators, and screening core indicators to fit the volume of benthic organisms.

[0006] A first aspect of this application provides a method for calculating the biomass of benthic organisms, used in a central controller, the method comprising:

[0007] Obtain images of the individuals to be analyzed; Extract the outermost contour perimeter, the inner cavity contour perimeter, the area contained in the outermost contour, and the area of ​​the inner cavity contour from the image of the individual to be counted. Based on the outermost contour perimeter and the inner cavity contour perimeter of the individual to be counted, the net biological perimeter of the individual to be counted is calculated. The net biological area of ​​the individual to be counted is calculated based on the area of ​​the outermost contour and the area of ​​the internal cavity contour of the individual to be counted. Based on the net perimeter and net area of ​​the organism, multiple candidate indicators are calculated; the multiple candidate indicators are used to characterize the volume of the individual to be counted. Based on the multiple candidate indicators, the core indicators are determined; Based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism, the individual biomass of the individual to be counted is calculated.

[0008] In some embodiments of this application, the images of the individuals to be counted include images of curved morphological organisms and images of benthic snails.

[0009] In some embodiments of this application, the first candidate index among the plurality of candidate indices is obtained by the following formula: ; in, This indicates the first candidate indicator among the multiple candidate indicators. This represents the net area of ​​the organism. This represents half of the net circumference of the organism.

[0010] In some embodiments of this application, the plurality of candidate indicators further includes a second candidate indicator and a third candidate indicator; the second candidate indicator is obtained based on the ratio between the square of the net area of ​​the organism and half of the net perimeter of the organism; the third candidate indicator is obtained based on the square root of the product of the first candidate indicator and the second candidate indicator.

[0011] In some embodiments of this application, determining the core indicator based on the plurality of candidate indicators includes: Extract the initial individual biomass of the individuals to be counted from the images of the individuals to be counted; The candidate indicators are respectively subjected to linear fitting analysis with the initial individual biomass to obtain the linear fitting correlation coefficient of each candidate indicator; The core indicators are determined based on the candidate indicators whose linear fitting correlation coefficient is higher than a preset score.

[0012] In some embodiments of this application, calculating the individual biomass of the individual to be counted based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism includes: Based on the aforementioned core indicators, construct the corresponding individual biomass statistical formula; Based on the net perimeter and net area of ​​the organism, the individual biomass of the individual to be counted is calculated using the individual biomass statistical formula.

[0013] In some embodiments of this application, after calculating the individual biomass of the individual to be counted based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism, the method further includes: The biomass level of the individual to be counted is determined based on the individual biomass of the individual to be counted; Extract the morphological features of the individuals to be counted from the images of the individuals to be counted; A statistical report for the individuals to be counted is generated based on their biomass level and morphological characteristics.

[0014] The first aspect of this application provides a method for calculating the biomass of benthic organisms. This method involves extracting the outermost contour perimeter, the inner cavity contour perimeter, the area encompassed by the outermost contour, and the area of ​​the inner cavity contour from an image of the organism to be counted. Based on the outermost contour perimeter and the inner cavity contour perimeter, the net perimeter of the organism is calculated. The net area of ​​the organism is calculated based on the area encompassed by the outermost contour and the area of ​​the inner cavity contour. Multiple candidate indicators are calculated based on the net perimeter and net area. A core indicator is determined based on the multiple candidate indicators. Finally, the individual biomass of the organism is calculated based on the core indicator, the net perimeter, and the net area. This method can improve the accuracy and efficiency of individual biomass calculation by extracting net morphological parameters, constructing candidate indicators, and screening core indicators to fit the volume of benthic organisms.

[0015] To achieve the above objectives, a second aspect of this application provides a system for counting the biomass of benthic organisms, the system comprising: The acquisition module is used to acquire images of the individuals to be analyzed. The extraction module is used to extract the outermost contour perimeter, the inner cavity contour perimeter, the area contained in the outermost contour, and the inner cavity contour area of ​​the individual to be counted from the image of the individual to be counted. The first module is used to calculate the net circumference of the organism of the individual to be counted based on the outermost contour perimeter and the inner cavity contour perimeter of the individual to be counted. The second module is used to calculate the net biological area of ​​the individual to be counted based on the area contained in the outermost contour and the area of ​​the internal cavity contour of the individual to be counted. The third module is used to calculate multiple candidate indicators based on the net perimeter and net area of ​​the organism; the multiple candidate indicators are used to characterize the volume of the individual to be counted. The determination module is used to determine the core indicator based on the multiple candidate indicators; The statistics module is used to calculate the individual biomass of the individual to be counted based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism.

[0016] To achieve the above objectives, a third aspect of this application provides an electronic device, comprising: at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform the above-described method for calculating the biomass of benthic organisms.

[0017] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the aforementioned method for calculating the biomass of benthic organisms.

[0018] It is understood that the beneficial effects of the second to fourth aspects compared with the related technologies are the same as the beneficial effects of the first aspect compared with the related technologies. Please refer to the relevant description in the first aspect above, which will not be repeated here. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating a method for calculating the biomass of benthic organisms according to an embodiment of this application; Figure 2 This is a schematic diagram of a caddisfly image provided in an embodiment of this application; Figure 3 This is the optimized converted volume provided in the embodiments of this application. Corresponding linear fit plot; Figure 4 This is the optimized converted volume provided in the embodiments of this application. Corresponding linear fit plot; Figure 5 This is the optimized converted volume provided in the embodiments of this application. Corresponding linear fit plot; Figure 6 This is provided in the embodiments of this application. Optimized conversion volume Corresponding linear fit plot; Figure 7 This is provided in the embodiments of this application. Optimized conversion volume Corresponding linear fit plot; Figure 8 This is a linear fitting diagram of body length provided in the embodiments of this application; Figure 9 This is the area-corresponding linear fitting graph provided in the embodiments of this application; Figure 10 This is a linear fitting graph corresponding to ESD provided in the embodiments of this application; Figure 11 This is provided in the embodiments of this application. Linear fitting plot of body length; Figure 12 This is provided in the embodiments of this application. Area corresponds to linear fitting plot; Figure 13 This is provided in the embodiments of this application. Corresponding linear fit plot; Figure 14 This is a schematic diagram of a radish snail provided in an embodiment of this application; Figure 15 This is a schematic diagram of the structure of a benthic organism individual biomass statistics system provided in an embodiment of this application; Figure 16 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0021] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0022] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0023] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0024] Benthic animals are aquatic fauna that spend all or most of their life cycle at the bottom of water bodies. They are often used as indicator organisms for aquatic environment monitoring. There are huge morphological differences among different phyla of benthic animals, and many species undergo specific metamorphic development during their growth period. The morphology of larvae and adults also changes significantly, which makes the monitoring and analysis of benthic animals a high-level technical challenge.

[0025] Benthic animals are a vast and complex group of aquatic organisms, and measuring their individual morphological parameters presents significant technical challenges. The benthic fauna is incredibly diverse, with the most common species including chironomid larvae (Arthropoda, Insecta), caddisfly larvae (Trichoptera), dragonfly larvae, and mayfly nymphs (Arthropoda); and tubifex worms (Annulararia, Oligochaeta). These aquatic benthic animals possess numerous antennae and legs, exhibiting extremely complex and curved three-dimensional morphologies, making the measurement of their body length, width, and circumference extremely difficult. Furthermore, common benthic species also include snails (Molecula, Gastropoda). Individuals of the same species in this group exhibit significant differences in morphology, such as thickness-to-width ratio. Snails, with their irregular three-dimensional body structures, also present challenges in reliably measuring their morphological parameters.

[0026] Meanwhile, the identification, analysis of morphology and particle size, and biomass statistics of benthic animals have long relied on experienced technical experts for manual identification. Furthermore, there are significant individual differences among benthic animals of different phyla and growth stages. Measuring the biomass of many small individuals requires carefully removing surface moisture with filter paper and then manually measuring each individual on an analytical balance with a precision of 1 / 100,000 to 1 / 1,000,000. Such balances are not only extremely expensive but also involve a massive amount of manual weighing. Additionally, for some large snails with substantial biomass, the biomass must be measured using ordinary balances with a capacity of several hundred grams and low precision. Therefore, conducting biomass measurements of single benthic animals in conventional laboratories presents a series of difficulties, including high operational complexity, a large workload, demanding equipment requirements, and extremely low efficiency.

[0027] Another major challenge in benthic biomass statistics is that conventional methods for weighing individual benthic organisms are extremely labor-intensive, inefficient, and prone to error. Because different types of benthic organisms vary greatly in surface flexibility and water content, many species, such as those with highly curved forms, have soft surfaces that are easily damaged. Conventional methods require manual and rigorous procedures to remove as much water and impurities as possible from the individual's surface, a process that easily leads to breakage and makes accurate biomass measurement difficult.

[0028] Finally, current methods for calculating biomass from two-dimensional images of aquatic organisms employ commonly used morphological parameters such as body length, area, and equivalent sphere diameter (ESD) to establish regression relationships with biomass. However, the accuracy and predictive ability of the regression models established by these methods vary greatly for different biological groups. Furthermore, for the same living benthic organism with curved or complex three-dimensional morphology, changes in the degree of curvature and the formation of closed-loop structures due to individual activity, as well as differences in image acquisition angles due to individual flipping movements, all lead to significant fluctuations in the conventional morphological parameters such as body length, area, and equivalent sphere diameter (ESD) of the same living benthic animal in the acquired two-dimensional images during image processing. This directly reduces the accuracy and predictive ability of the regression models for these parameters and biomass.

[0029] Therefore, existing benthic animal image acquisition and analysis technologies are not yet able to perform high-precision morphological and biomass statistical analysis on benthic organisms with the aforementioned curved forms and snails. In addition, existing benthic animal identification and analysis standards only count the total biomass of a certain species, which is far from meeting the needs of a large number of ecological studies that require precise biomass statistics of individual benthic animals.

[0030] Based on this, embodiments of this application provide a method, system, electronic device, and medium for calculating the individual biomass of benthic organisms. The aim is to accurately fit the three-dimensional volume of benthic organisms with complex morphology by extracting net morphological parameters, constructing specific candidate indicators, and screening core indicators, thereby significantly improving the accuracy and efficiency of individual biomass statistics.

[0031] The benthic organism individual biomass counting method, system, electronic device and medium provided in the embodiments of this application are specifically described through the following embodiments. First, the benthic organism individual biomass counting method in the embodiments of this application is described.

[0032] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Specifically, artificial intelligence (AI) r Artificial Intelligence (AI) is the theory, methods, technology, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0033] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0034] The method for calculating the individual biomass of benthic organisms provided in this application relates to the field of biological monitoring technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for calculating the individual biomass of benthic organisms, but is not limited to the above forms.

[0035] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0036] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0037] Therefore, referring to Figure 1This application provides a method for counting the biomass of benthic organisms. This method is applied to a central controller, which can be a server, an electronic device, or a mobile terminal, etc. There are no specific limitations here. The method includes the following steps S110 to S170.

[0038] Step S110: Obtain the image of the individual to be counted; Step S120: Extract the outermost contour perimeter, the inner cavity contour perimeter, the area contained in the outermost contour, and the area of ​​the inner cavity contour from the image of the individual to be counted. Step S130: Based on the outermost contour perimeter and the inner cavity contour perimeter of the individual to be counted, calculate the net circumference of the organism of the individual to be counted. Step S140: Calculate the net biological area of ​​the individual to be counted based on the area of ​​the outermost contour and the area of ​​the internal cavity contour of the individual to be counted. Step S150: Calculate multiple candidate indicators based on the net perimeter and net area of ​​the organism; these multiple candidate indicators are used to characterize the volume of the individual to be counted. Step S160: Determine the core indicators based on multiple candidate indicators; Step S170: Based on the core indicators, net perimeter of the organism, and net area of ​​the organism, calculate the individual biomass of the individual to be counted.

[0039] In this step, images of the individuals to be counted are first acquired. These individuals include images of benthic organisms with curved morphology and images of snail-like benthic organisms. Specifically, the images are not limited to photographs taken directly by image processing equipment (such as microscopes or smart terminal devices) equipped with a benthic organism biomass counting system, but also include images taken by other external devices (such as webcams, portable cameras, etc.) and subsequently imported or synchronized to the image processing equipment. Furthermore, the images may also originate from cloud storage platforms and be transmitted directly to the image processing equipment for processing via the network. Similarly, these images may also be captured by independent imaging devices, such as high-end digital SLR cameras or professional camcorders, in different environments, and then uploaded to the image processing equipment via wireless transmission (Wi-Fi, Bluetooth, etc.) or data cable transmission.

[0040] Furthermore, image analysis tools are used to batch process the acquired microscopic images: first, the color images are converted into grayscale images, and then Gaussian filtering algorithm is used to smooth and denoise the images to eliminate tiny noise points and interference pixels. Then, the adaptive thresholding method or OTSU algorithm is used to complete the binarization process, completely separating the individuals to be counted from the background, forming an image containing only black and white binary values.

[0041] Furthermore, morphological opening operations are performed on the binarized image to remove edge burrs and residual noise. However, during this process, the original state of the naturally enclosed internal cavities of the individuals to be analyzed (especially curved organisms and snails) must be strictly preserved without any filling operations to ensure the integrity of the cavity structure. Next, the contour detection function is activated to identify all contours in the image and establish hierarchical relationships. Valid biological contours are selected based on preset area and aspect ratio thresholds, while invalid contours caused by noise, sample fragments, or background interference are removed. Finally, based on the selected valid contours, the outermost contour and all internal cavity contours of the individuals to be analyzed are extracted. The perimeter of the outermost contour, the area of ​​the entire region enclosed by the outermost contour (i.e., the area contained in the outermost contour), and the perimeter and area of ​​each internal cavity contour are accurately calculated and recorded, laying the data foundation for subsequent calculation of net morphological parameters.

[0042] Furthermore, after extracting the perimeter of the outermost contour and the perimeter of all internal cavities, according to the definition of the net perimeter of an organism, its core is to comprehensively reflect the effective interaction interface length between the individual and the external aquatic environment medium. The perimeter of the outermost contour and the perimeter of all internal cavities are summed to obtain the net perimeter of the organism that can truly reflect the total interaction interface length between the individual to be counted and the external environment medium.

[0043] Furthermore, the net area of ​​an organism is determined based on the core definition of net area, which is to eliminate the cavity space occupied by non-tissues within the individual, accurately reflecting the two-dimensional projected area actually occupied by body tissues. Then, based on the area contained in the outermost contour, the total area of ​​all internal cavity contours is subtracted. The net area of ​​the individual to be analyzed is obtained by calculating "area contained in the outermost contour - sum of the areas of all internal cavity contours." This eliminates the cavity space occupied by non-tissues within the individual, truly reflecting the effective space occupied by the body tissues of the individual on the two-dimensional projection plane, providing key parameters for subsequently constructing an accurate volume-biomass correlation model.

[0044] Furthermore, core parameters are calculated based on the effective contour, where the net perimeter of the organism is the sum of the perimeter of the outermost contour and the perimeter of all internal cavity contours, and the net area of ​​the organism is the total area contained in the outermost contour minus the sum of the areas of all internal cavity contours. Finally, the extracted parameters are saved to a structured file, and the contour and key measurement values ​​are annotated in the original map for subsequent verification.

[0045] Furthermore, using three statistical methods, three optimized converted volume indices were calculated based on the net area and net perimeter of the organism. These indices possess a three-dimensional volume dimension mathematically, enabling efficient fitting of the actual volume of individuals with complex morphologies. Subsequently, two of these optimized converted volume indices underwent logarithmic transformation to obtain their corresponding logarithmic parameters. This resulted in five candidate indices comprising the three optimized converted volumes and two logarithmic parameters. All candidate indices revolve around characterizing the true volume of the individual being analyzed, laying the foundation for subsequent accurate biomass fitting.

[0046] Furthermore, statistical analysis tools were used to perform linear regression fitting between the five candidate indicators and the measured biomass. The optimized converted volume was directly fitted to the biomass, and the logarithmic parameter was fitted to the logarithmic form of the measured biomass. The linear regression formula, correlation coefficient, and significance level of each candidate indicator were statistically analyzed simultaneously.

[0047] Specifically, select those whose significance level meets the preset requirements (such as...). p <0.01) and the correlation coefficient is higher than the preset threshold (e.g., r Candidate indicators with a correlation coefficient greater than 0.85 were selected. The correlation coefficients of the indicators that passed the initial screening were then compared, and the indicator with the best linear fit (highest correlation coefficient) was selected as the core indicator to ensure that the core indicator has the strongest correlation with biomass and the highest reliability.

[0048] Furthermore, based on the linear fitting results between the core indicators and the measured biomass, and combined with the derivation logic related to the net perimeter and net area of ​​the organism, an individual biomass statistical formula is constructed. Then, by substituting the core indicators, net perimeter, and net area of ​​the individual to be statistically analyzed into the individual biomass statistical formula, the accurate biomass data for that individual can be quickly calculated.

[0049] In this step, the first candidate indicator among multiple candidate indicators is obtained using the following formula: ; in, This indicates the first candidate indicator among multiple candidate indicators. Represents the net surface area of ​​an organism. It represents half of the net circumference of an organism.

[0050] Furthermore, the candidate indicators also include a second candidate indicator and a third candidate indicator; the second candidate indicator is obtained based on the ratio between the square of the net area of ​​the organism and half of the net perimeter of the organism; the third candidate indicator is obtained based on the square root of the product of the first candidate indicator and the second candidate indicator.

[0051] In one embodiment, taking benthic organisms with curved morphology and snails as examples, image processing technology is first used to manually identify, classify, and sort the benthic organism samples collected in the field. Samples of the same species are transferred into the same glass container, with 40-100 samples of each species, comprehensively including large, medium, and small sizes of individuals of the same species. Among them, the number of samples used for candidate index calculation and screening and statistical formula construction is ≥30, and the number of samples used for data verification of formula reliability is ≥10.

[0052] Furthermore, under a stereomicroscope equipped with a CCD, the benthic organism samples to be photographed were placed in a glass petri dish for microscopic photography and image acquisition. During microscopic photography, the actual size of each pixel in the acquired image was first calibrated using the microscope scale, providing a calibration basis for obtaining the morphological parameters of the acquired sample images. During image acquisition, the background brightness parameter value of the acquired image was strictly maintained between 140 and 160 by adjusting the light source brightness and exposure time.

[0053] Further, after image acquisition, the samples were removed with plastic tweezers, individually numbered, and the surface moisture of the biological samples was carefully absorbed with qualitative filter paper. The wet weight of each benthic animal was then weighed and recorded on a 0.01% analytical balance. Strict attention was paid to careful operation to ensure that the surface moisture of the biological samples was absorbed and that the morphology of the biological samples remained intact and undamaged.

[0054] Furthermore, the OpenCV library is used to automate batch processing of the acquired benthic animal images to extract morphological parameters, specifically including the following steps: ① Image preprocessing: Convert the color microscopic image to grayscale, perform Gaussian filtering for smoothing and noise reduction, and then perform binarization using adaptive thresholding or the OTSU algorithm to separate benthic animals from the background.

[0055] ② Morphological optimization: Morphological opening operations are performed on the binary image to eliminate minor noise. During this process, special care is taken to preserve the original state of the internal cavities naturally formed by the curved benthic animals, without filling them, to ensure that the net morphological parameters of the organism extracted later can truly reflect the functional structure of bent or curled benthic organisms.

[0056] ③ Contour detection and screening: Detect all contours and establish hierarchical relationships. Filter valid biological contours based on area and aspect ratio thresholds, and exclude noise or debris.

[0057] ④ Extraction of morphological parameters such as net perimeter and net area of ​​organisms: Calculate the following key morphological parameters for each valid contour: Net perimeter of an organism: This refers to the sum of the perimeters of the organism's outer contour and the perimeters of all its internal cavities on a two-dimensional projection. This parameter characterizes the total interface boundary length between the body tissues and the external environmental medium of a benthic organism in its naturally closed state, when it is curved or curled up. The formula is: Net perimeter of organism = Perimeter of the outermost contour of the organism + Σ (Perimeter of the internal cavity contours); Net area of ​​an organism: This refers to the net projected area formed by the outer edge of the body wall of a benthic organism in its naturally closed state on a two-dimensional projection plane, after deducting all internal cavities formed by the curves of the body wall. The net area of ​​an organism is usually smaller than the biological area calculated by conventional methods. The formula is: Net area of ​​organism = Area contained in the outermost contour - Σ (Area of ​​the internal cavity contours).

[0058] In addition, body length and body width are calculated by taking the ellipse that best fits the outermost contour of the organism, with the length of the major axis of the ellipse as the body length and the length of the minor axis as the body width; area refers to the total pixel area enclosed by the outermost contour in the two-dimensional projection image of the organism. This parameter is a primary morphological measurement value, without deducting internal cavities, and represents the maximum contour or space occupied by the organism in two-dimensional space; Equivalent Sphere Diameter (ESD) is based on the extracted body length and body width, which are regarded as the major and minor axes of a rotating ellipsoid. After calculating the volume of the ellipsoid, the diameter of the sphere with the same volume is obtained, which is the ESD.

[0059] ⑤ Structured data storage and visualization: Save all extracted morphological parameters to a structured file in CSV format, while marking the contours and key measurement values ​​on the original image, and output a visualization result image for verification.

[0060] Furthermore, using the extracted net perimeter and net area of ​​the organism as core data, and substituting them into the first, second, and third index calculation formulas derived from numerous experiments, the optimized converted volumes of curved and snail-like benthic organisms were calculated respectively. 、 、 and calculation , It is also used as a candidate indicator for estimating the volume of benthic and snail-like benthic organisms.

[0061] Specifically, the formula for calculating the candidate indicators is as follows: ; ; ; in, As the primary indicator, As the second indicator, As the third indicator, The net surface area of ​​the organism. It is half the net circumference of the organism.

[0062] Furthermore, based on the results of image acquisition and biomass measurement, the optimized calculated volume of each individual's curvature and the benthic snail organisms were determined. 、 、 Corresponding one-to-one with the manually weighed biomass, respectively 、 、 With biomass, and and Linear regression fitting was performed, and linear fitting plots were generated for each of the five candidate indicators. The linear regression formula and correlation coefficient were then calculated sequentially. r , p value.

[0063] Furthermore, firstly, based on the overall significance test of the statistical regression model, regression models that satisfy [the following criteria] are selected. p <0.01、 r Candidate indicators with a correlation coefficient >0.85 were selected, and then the correlation coefficients of the regression models for the selected candidate indicators were analyzed. r Compare and select the correlation coefficient for linear fitting analysis. r The highest-ranking indicator is used as the core indicator. Further analysis and establishment are then conducted. 、 、 With wet weight and and and The linear regression formula was used. For the core indicators, appropriate formulas were selected to construct rapid digital statistical formulas for the individual biomass of benthic animals such as hornets and snails.

[0064] Among them, different candidate indicators 、 、 , , The linear regression formula for biomass is as follows: ; ; ; ; ; in, express The corresponding biomass, express The corresponding biomass, express The corresponding biomass, express The corresponding biomass, express The corresponding biomass, As the primary indicator, As the second indicator, As the third indicator, The net surface area of ​​the organism. It is half the net circumference of the organism. , , , , , , , , , This is a preset constant.

[0065] Furthermore, for the morphological parameters of benthic organisms extracted from the microscopic images, the body length, etc., were statistically analyzed and calculated. ,area, ESD These data parameters serve as traditional auxiliary indicators for comparison. These auxiliary indicators are used to verify the reliability of the systems developed by companies conducting rapid digital biomass statistics.

[0066] Specifically, the reliability verification process includes two steps: traditional indicator comparison verification and predictive capability verification. The traditional indicator comparison verification first establishes linear regression models using commonly used biomass statistics metrics such as body length, outline area, and ESD index, as well as actual measured biomass of benthic organisms like voles and snails. The regression equations are then calculated, and correlation coefficients are statistically analyzed. r With significance level p Value, and then calculate , , Data, respectively with Establish a linear regression model, calculate the regression equation, and calculate the correlation coefficient. r With significance level p Values. Among them, the data used for reliability verification auxiliary indicators and linear fit analysis of measured biomass were selected and retained. p Value < 0.01 and r Auxiliary indicators >0.85 were filtered out.p Value ≥ 0.01 or r Auxiliary indicators ≤0.85 include: body length, Furthermore, the correlation coefficients between the retained auxiliary indicators and the measured biomass linear fitting analysis were compared with the correlation coefficients between the candidate indicators and the measured biomass linear fitting analysis.

[0067] Furthermore, if the correlation coefficient between the candidate indicator and the measured biomass in the linear fitting analysis is greater than that between the auxiliary indicator and the measured biomass in the linear fitting analysis, it indicates that the reliability of the statistical formula has been verified and the formula is valid. If the correlation coefficient between the auxiliary indicator and the measured biomass is greater than that between the core indicator and the measured biomass, it indicates that the reliability of the statistical formula needs to be further optimized. The linear regression model with the highest correlation coefficient is selected as the final rapid digital statistical formula for biomass.

[0068] Specifically, predictive capability validation first uses validation samples independent of the model training set, with a sample size requirement of ≥10. Based on the core metrics selected in the above steps, the optimized converted volume and logarithmic transformation value corresponding to the core metrics are calculated according to the following formulas. Optimize the converted volume.

[0069] ; ; ; in, As the primary indicator, As the second indicator, As the third indicator, The net surface area of ​​the organism. It is half the net circumference of the organism.

[0070] Furthermore, the predicted wet weight (biomass) and its logarithmic transformation value corresponding to the core indicators are calculated according to the following formula. Predict wet weight (biomass) and quantify prediction error: ; ; ; ; ; in, express The corresponding predicted wet weight, express The corresponding predicted wet weight, express The corresponding predicted wet weight, express The corresponding predicted wet weight, express The corresponding predicted wet weight, As the primary indicator, As the second indicator, As the third indicator, The net surface area of ​​the organism. It is half the net circumference of the organism. , , , , , , , , , This is a preset constant.

[0071] Furthermore, based on the prediction error results, the standardized root mean square error (NRMSE) and the prediction determination coefficient are calculated. Pr edicted R ², calculated based on validation samples. The NRMSE calculation formula is as follows: ; in, The total number of samples, For the first The actual observed values ​​of each sample; No. The predicted value for each sample, This is the mean of all observations.

[0072] Furthermore, when , When the formula is accurate, its prediction accuracy and reliability are considered high; when... , When the value is between 0 and 1, it indicates that the prediction accuracy of the formula is acceptable; when 0 is between 0 and 1, it indicates that the prediction accuracy of the formula is acceptable. , This indicates that the formula has poor predictive ability and cannot pass the predictive ability verification.

[0073] Furthermore, based on the above verification steps, the final rapid statistical formulas for bending morphology and individual biomass of benthic snails were determined. According to the categories of core indicators, the verified final rapid statistical formulas for bending morphology and individual biomass of benthic snails were derived as follows: ; ; ; ; ; in, Based on core indicators Digital biomass statistics Based on core indicators Digital biomass statistics Based on core indicators Digital biomass statistics Based on core indicators Digital biomass statistics Based on core indicators Digital biomass statistics Optimize the equivalent volume for benthic organisms such as spherical and snail-like organisms. This refers to the net body surface area of ​​an individual benthic animal. It is half the net circumference of the organism. , , , , , , , , , This is a preset constant.

[0074] Therefore, this embodiment, for the first time, introduces two core net morphological measurement parameters—net perimeter and net area—for the complex geometry of curved morphologies and benthic snails, enabling precise removal of non-organic regions within curved and coiled structures. The physiological and ecological significance of these "net morphological parameters" includes: the net area, obtained by accurately subtracting the internal cavities formed by the organism's curved, coiled, or closed-loop structures, provides a two-dimensional equivalent area that truly reflects the space occupied by the organism's tissues, thus more accurately characterizing the physical basis of biomass; the net perimeter comprehensively considers the sum of the boundaries of the organism's external contour and all internal cavities, characterizing the effective total interface length for material exchange and interfacial interaction between the organism and the external aquatic environment.

[0075] Furthermore, based on the net perimeter and net area parameters, this embodiment independently proposes an optimized converted volume index through extensive original experiments and statistical analysis. 、 、 and its logarithmic form and These indicators have a three-dimensional volume dimension mathematically. They are innovative formulas that directly and efficiently fit the three-dimensional volume of complex bent and closed organisms using two-dimensional image information. This avoids the time-consuming, expensive, and easily damaged three-dimensional scanning or complex statistical modeling of complex bent animals and snails. It is a core breakthrough in solving the problem of estimating the volume of bent and closed morphologies and complex three-dimensional bent bent bent organisms.

[0076] Furthermore, extensive experimental verification through the embodiments has been conducted. 、 、 , and The linear correlation coefficient of the biomass linear regression formula constructed from these five original candidate indicators r The results show that the accuracy of biomass statistics is significantly higher than that of traditional morphological parameter models commonly used in this field, such as body length, area, and equivalent sphere diameter (ESD), indicating that this embodiment has an overwhelming advantage in the accuracy of biomass statistics. Furthermore, the rapid biomass digitization statistical formula and method constructed in this embodiment, when compared with conventional models in actual predictive ability, show that its model stability is significantly better than that of traditional models. Based on the standardized root mean square error (N... R MSE) and forecasts Of the two key metrics, the model in this embodiment performs best, with the lowest prediction error and high fit, making it more suitable for rapid biomass estimation of curved morphology and benthic snails.

[0077] In summary, this embodiment proposes and constructs for the first time a rapid estimation system for curved morphology and volume and biomass of snail benthic organisms based on five original candidate indicators composed of dual net morphological parameters of "net biological area + net biological perimeter". It is significantly superior in terms of accuracy, predictive ability and efficiency, and has significant innovative and application value in the fields of benthic animal research and intelligent aquatic ecological monitoring.

[0078] In one implementation, such as Figure 2 As shown, taking the caddisfly as an example, it is a benthic animal belonging to the order Trichoptera in the class Insecta of the phylum Arthropoda. It exhibits a typical curved morphology, and the proportion of caddisfly individuals forming a closed internal cavity structure through curvature is relatively small. The following describes the steps for rapid statistical analysis of the biomass of individual caddisflies: First, caddisfly samples with curved shapes were collected in the field. After manual classification and sorting, samples of the same species were transferred into glass containers. The caddisfly samples included large, medium, and small sizes. In this embodiment, a total of 40 caddisfly samples were collected. Among them, 30 samples were used for candidate index calculation and screening and statistical formula construction, and 10 samples were used for data verification of formula reliability.

[0079] Furthermore, using a stereomicroscope equipped with a CCD camera (such as the Olympus SZ61), the caddisfly sample to be photographed was placed in a glass petri dish for microscopic photography and image acquisition. During microscopic photography, the actual size of each pixel in the acquired image was calibrated using the microscope scale, providing a calibration basis for obtaining sample morphology parameters in subsequent image acquisition. Next, the light source brightness and exposure time were adjusted to strictly ensure that the background brightness parameter value of the acquired image was between 140 and 160.

[0080] Further, the caddisfly samples, after image acquisition, were individually removed with plastic tweezers. The surface moisture of the biological samples was carefully absorbed with qualitative filter paper, and each individual sample was numbered sequentially. The wet weight of each benthic animal was then weighed and recorded on a 0.01% analytical balance. Strict attention was paid to careful operation to ensure that the surface moisture of the biological samples was absorbed and that the samples remained intact and undamaged.

[0081] Furthermore, the OpenCV library was used to automate batch processing of the acquired benthic animal images, specifically including image preprocessing, morphological optimization, contour detection and filtering, and morphological parameter extraction. Image preprocessing involved converting color microscopic images to grayscale, applying Gaussian filtering for smoothing and noise reduction, and then binarizing them using adaptive thresholding or the OTSU algorithm to separate the benthic animals from the background. Morphological optimization involved performing morphological opening operations on the binary images to eliminate minor noise. During this process, special attention was paid to preserving the original state of the internal cavities naturally formed by the curved benthic animals, without filling them, to ensure that the subsequently extracted morphological parameters accurately reflect the functional structure of the organism. Contour detection and filtering involved detecting all contours and establishing hierarchical relationships, filtering valid biological contours based on area and aspect ratio thresholds, and eliminating noise or debris. Morphological parameter extraction involved calculating key morphological parameters for each valid contour, such as the net perimeter, net area, body length and width, area, and equivalent sphere diameter.

[0082] Specifically, the net perimeter of an organism refers to the sum of the perimeters of its outer contour and the perimeters of all internal cavities on a two-dimensional projection map. It is used to characterize the total interface boundary length between the body tissues and the external environmental medium of benthic organisms in a naturally closed state, where the organism is curved or curled. The calculation formula is: Net perimeter of organism = perimeter of the outermost contour of the organism + Σ(perimeter of internal cavity contours); Net area of ​​organism: refers to the net projected area formed by the outer edge of the body wall of a benthic organism in a naturally closed state on a two-dimensional projection plane, after deducting all internal cavities formed by the curvature of the body wall. The area is calculated as follows: Net area of ​​organism = Area contained in the outermost contour - Σ (Area of ​​internal cavity contour); Body length and width are calculated by using the ellipse that best fits the outermost contour of the organism, with the length of the major axis of the ellipse as the body length and the length of the minor axis as the body width; Area refers to the total pixel area enclosed by the outermost contour in the two-dimensional projection image of the organism, with parameters being primary morphological measurements without deducting the internal cavity; Equivalent sphere diameter is calculated by treating the extracted body length and width as the major and minor axes of a rotating ellipsoid, calculating the volume of the ellipsoid, and then obtaining the diameter of the sphere with the same volume, which is the ESD.

[0083] Furthermore, all extracted morphological parameters are saved to a structured file in CSV format. Contours and key measurements are annotated on the original image, and a visualization is output for verification. Then, using the extracted net perimeter and net area data, the optimized converted volumes of curved and snail-like benthic organisms are calculated using the first, second, and third index calculation formulas, respectively. 、 、 ， and calculation , c Among them, the optimized equivalent volume of benthic organisms such as worms and snails. 、 、 , as well as These are all core candidate indicators for estimating the volume of benthic and snail-like benthic organisms.

[0084] Furthermore, based on the results of image acquisition and biomass measurement, the optimized calculated volume of each individual's curvature and the benthic snail organisms were determined. 、 、 Corresponding one-to-one with the manually weighed biomass, respectively 、 、 With biomass, and and Perform linear regression fitting, plot the linear fit graphs, and then calculate the linear regression formula and correlation coefficients. r , p Value, of which, optimized converted volume The corresponding linear fitting graph is as follows Figure 3 As shown, the optimized converted volume The corresponding linear fitting graph is as follows Figure 4 As shown, the optimized converted volume The corresponding linear fitting graph is as follows Figure 5 As shown, Optimized conversion volume The corresponding linear fitting graph is as follows Figure 6 As shown, Optimized conversion volume The corresponding linear fitting graph is as follows Figure 7 As shown, a caddisfly in a curved shape. 、 、 Compared with measured biomass and , and The results of the linear fitting analysis parameters are shown in Table 1.

[0085] Table 1

[0086] Furthermore, the screening process for core candidate indicators is as follows: ① Based on the overall significance test of the statistical regression model, indicators that satisfy the regression model are first selected. p <0.01、 r The core indicators under the premise of >0.85, among which the core candidate indicators 、 、 Compared with measured biomass and , and All regression models satisfy this screening rule. ② The correlation coefficients of the regression models for the core candidate indicators that passed the screening are... r Compare and select the correlation coefficient for linear fitting analysis. r Higher parameters are used as the final selection criteria.

[0087] Furthermore, based on the analysis results, the optimized equivalent volume of benthic organisms and snails was determined. 、 、 Compared with measured biomass, , and The mean regression relationship of the data is significant. p <0.01、 r All values ​​are greater than 0.85, thus fully meeting the significance test requirements for the candidate indicators. Furthermore, in 、 、 Compared with measured biomass, , and In the regression relationship of the data, ,use These are the final candidate indicators.

[0088] Furthermore, based on the above measurement and calculation results, a rapid digital statistical formula for the biomass of individual caddisflies was finally constructed: ; The rapid statistical formula for the biomass of individual caddisflies was further simplified by removing the logarithm, resulting in the following rapid statistical formula for the biomass of caddisflies with curved morphology: ; in, The biomass of an individual caddisfly is expressed in mg (wet weight). Net surface area of ​​the organism, in mm. 2 ; It is half the net circumference of the organism, and the unit is mm.

[0089] Furthermore, based on the collected microscopic images of benthic organisms such as slugs and snails, morphological parameters such as body length, body width, perimeter, and area of ​​the benthic organisms in the images were extracted, and the body length, width, perimeter, and area were statistically analyzed and calculated. ,area, ESD Data such as these are used to compare the reliability verification auxiliary indicators constructed by companies conducting rapid digital biomass statistics.

[0090] This yielded auxiliary indicators for verifying the reliability of the caddisflies and a linear fitting analysis diagram of the measured biomass. The linear fitting diagram corresponding to body length is shown below. Figure 8 As shown, the area corresponds to the linear fitting plot. Figure 9 As shown, the linear fitting plot corresponding to ESD is as follows: Figure 10 As shown, The linear fitting graph corresponding to body length is shown below. Figure 11 As shown, The area corresponds to the linear fitting graph as shown below. Figure 12 As shown, The corresponding linear fitting graph is as follows Figure 13 Core indicators of curved morphology of caddisflies 、 、 , , The linear fitting comparison between auxiliary indicators and measured biomass is shown in Table 2.

[0091] Table 2

[0092] Specifically, a rapid digital statistical formula for biomass was constructed based on five core indicators. p The values ​​are all much less than 0.01, and the correlation coefficients are... r All are greater than 0.96, and the correlation coefficients of the five core indicators are... r All of these values ​​significantly exceeded the conventional auxiliary indicators used for reliability verification. Therefore, the rapid digitization statistical formula for biomass constructed from the five core indicators all passed reliability data verification.

[0093] Furthermore, among the five selected core indicators, the core indicators 、 、 Compared with measured biomass, and and In linear fitting analysis, and and In the regression relationship of data , determined to adopt These are the final selection criteria.

[0094] Furthermore, according to and The constructed rapid statistical formula for biomass was validated using traditional indicator comparison steps. Specifically, the predictive ability was validated using validation samples independent of the model training set (n≥10), and the parameters of the validation samples were calculated. Value, substitute into the established and The regression equation was used to calculate the predicted value of biomass and quantify the prediction error. The prediction results and error analysis of the caddisfly validation samples are shown in Table 3.

[0095] Table 3

[0096] Therefore, based on the quantitative prediction error results, the standardized root mean square error is further calculated. ,predict Optimized conversion volume is adopted. The linear regression equation with biomass, when , When the equation is accurate, it can be determined that the prediction accuracy and reliability are relatively high.

[0097] Based on the analysis of the verification results, the conversion volume was optimized. In the prediction of biomass for independent validation samples of caddisflies using a linear regression equation for biomass, Less than 25%; forecast Much greater than 0.8 and prediction All values ​​meet the criteria for judging the reliability of equations based on predictive capability verification. According to the analysis results, the selected equations show excellent reliability in prediction verification, can well reflect the relationship between zooplankton morphological characteristics and body weight, and have good ecological application value in wet weight estimation.

[0098] In this embodiment, the rapid statistical formula for digitizing caddisfly biomass was compared with the model of conventional methods (as shown in Table 4). Independent verification showed significant differences between the two methods. and Of the two indicators, the statistical formula model based on the dual-parameter system of "net biological area and net biological perimeter" in this embodiment performs best, with the lowest prediction error and high fitting degree. The model stability is significantly better than conventional models such as body length, area and ESD, and it is more suitable for rapid biomass estimation.

[0099] Table 4

[0100] Furthermore, based on the results of image acquisition and weighing, morphological parameter extraction, core candidate index calculation, construction of a rapid statistical formula for digitizing biomass, and data verification of formula reliability, the statistical formula for rapid digitization of individual biomass of curved-morphology caddisflies is finally determined as follows: ; in, The biomass of an individual caddisfly is expressed in mg (wet weight). Net surface area of ​​the organism, in mm. 2 ; It is half the net circumference of the organism, and the unit is mm.

[0101] In one implementation, such as Figure 14As shown, taking the radish snail as an example, the radish snail is a benthic animal belonging to the class Gastropoda of the phylum Mollusca, exhibiting a complex and curved three-dimensional morphological feature. In this implementation, a total of 43 radish snail samples were used, with 33 samples used for candidate index calculation and screening and statistical formula construction, and 10 samples used for data verification of formula reliability.

[0102] In the core candidate indicator selection step of this implementation method, the core indicator of the radish snail is... 、 、 , , The linear fitting comparison between auxiliary indicators and measured biomass is shown in Table 5. 、 、 The linear regression correlation coefficient with measured biomass r The values ​​are 0.9230, 0.9235, and 0.9232 respectively; log 10 V b , log 10 With log 10 linear regression correlation coefficient of biomass r The values ​​are 0.9160 and 0.9152 respectively. p All values ​​were less than 0.001. After screening, all five candidate indicators passed the significance test. Further... 、 、 Comparative analysis of the regression relationship with biomass. > > , The linear regression equation between the measured biomass and the actual biomass is y = 0.895x + 39.079. Based on this, the following approach is adopted... These are the final selection criteria.

[0103] Table 5

[0104] Furthermore, based on the indicator screening results, the rapid statistical formula for the individual biomass of the Radish Snail is as follows: ; in, For biomass, The net surface area of ​​the organism. It is half the net circumference of the organism.

[0105] Specifically, in the traditional indicator comparison verification of this implementation method, as shown in Table 5, the linear regression correlation coefficients of the traditional indicators body length, area, ESD, and biomass are... r The values ​​are 0.7058, 0.8557, and 0.8299, respectively. , area, and linear regression correlation coefficient r The correlation coefficients were 0.7568, 0.8221, and 0.7997, respectively. The linear regression correlation coefficients of the rapid digitization statistical formula for biomass, constructed based on the five core candidate indicators, were also [not specified]. r All are above 0.91 p The values ​​are all less than 0.001. Based on comparative analysis, the correlation coefficients of the six traditional indicators... r All correlation coefficients were less than 0.86, and only the area index showed a correlation coefficient. r> 0.85, correlation coefficient of the five core indicators r All five core indicators are significantly higher than the auxiliary indicators used for reliability verification. All five core indicators have been verified by reliability data, and their reliability is far superior to the six conventional indicators used for comparative verification. Based on this, the following selection was made. As the final selection criteria, based on The rapid digital statistical formula for biomass, constructed with biomass, passed the final reliability verification step.

[0106] Furthermore, in the verification of the predictive capability of this implementation, according to The predicted wet weight was calculated using the regression equation of biomass, and the prediction error was quantified. Based on the validation results, In the prediction of biomass for independent validation samples of Radix Sinica using the linear regression equation for biomass, 18.06%, less than 25%; forecast 0.8546, greater than 0.8. Traditional indicators... The value ranged from 23.42% to 32.51%. The values ​​range from 0.5291 to 0.7556. The equations implemented in this way demonstrate good reliability in prediction verification.

[0107] Based on the analysis and verification results of the above steps, the statistical formula for the rapid digitization of the biomass of individual Radish snails with complex, curved, three-dimensional morphology was finally determined as follows: ; in, The biomass of an individual Radish Snail is expressed in mg (wet weight). Net surface area of ​​the organism, in mm. 2 , It is half the net circumference of the organism, and the unit is mm.

[0108] In some embodiments, the core indicator is determined in step S140 based on multiple candidate indicators, including the following steps S210 to S230: Step S210: Extract the initial individual biomass of the individuals to be counted from the images of the individuals to be counted; Step S220: Perform linear fitting analysis between each candidate index and the initial individual biomass to obtain the linear fitting correlation coefficient of each candidate index; Step S230: Determine the core indicators based on the candidate indicators whose linear fitting correlation coefficient is higher than the preset score.

[0109] In this embodiment, the initial individual biomass of the individuals to be counted is extracted from the images of the individuals to be counted. Preferably, the measured baseline data is acquired simultaneously with the image acquisition. After the acquisition of the microscopic images of the individuals to be counted is completed, the samples are taken out one by one with plastic tweezers and numbered sequentially. Then, the moisture adhering to the sample surface is carefully absorbed with qualitative filter paper. During the operation, it is necessary to ensure that the sample morphology is intact and undamaged to prevent the weighing accuracy from being affected by individual breakage. Finally, the processed samples are placed on a 0.01% analytical balance for weighing, and the wet weight data of each individual is recorded. This measured wet weight is the initial individual biomass required for subsequent linear fitting analysis, providing a real baseline for the validity verification of candidate indicators.

[0110] Furthermore, professional statistical analysis tools were used to conduct fitting analysis. The fitting process needed to be combined with the classification of candidate indicators: for the three candidate indicators of optimized converted volume, they were directly fitted with the corresponding initial individual biomass using linear regression; for the two candidate indicators in logarithmic form, the initial individual biomass was first logarithmically transformed, and then the logarithmic candidate indicators were fitted with the logarithmically transformed initial biomass using linear regression. After the fitting was completed, not only was the linear fitting correlation coefficient for each candidate indicator obtained, but the regression equation and significance level were also statistically analyzed simultaneously to comprehensively reflect the reliability and effectiveness of the fitting model.

[0111] Specifically, the determination of core indicators requires a two-step screening process: the first step is preliminary screening, with a preset score typically set at 0.85, while also requiring a certain significance level for the fitted model. p <0.01, filter out those that simultaneously meet the criteria of "linear fitting correlation coefficient higher than 0.85" and " p Candidate indicators with a correlation coefficient <0.01 were eliminated, and those with low correlation or statistical insignificance were removed. The second step was to select the best candidate indicators. For the candidate indicators that passed the initial screening, their linear fitting correlation coefficients were compared, and the candidate indicator with the highest correlation coefficient and the closest association with the initial individual biomass was selected as the core indicator. This ensured that the biomass statistical formula constructed later had the best fitting effect and prediction accuracy.

[0112] In some embodiments, in step S150, the individual biomass of the individual to be counted is calculated based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism, including the following steps S310 to S320: Step S310: Construct the corresponding individual biomass statistical formula based on the core indicators; Step S320: Based on the net perimeter and net area of ​​the organism, calculate the individual biomass of the individual to be counted using the individual biomass statistical formula.

[0113] In this embodiment, the corresponding linear regression equation is first extracted based on the core indicator type. Specifically, if the core indicator is the optimized converted volume, the coefficients and constant terms in the regression equation are determined directly with the core indicator as the independent variable and the initial individual biomass as the dependent variable; if the core indicator is in logarithmic form, the regression equation based on the logarithmized indicator and the logarithmized initial biomass is first simplified to an intuitive biomass calculation form by delogization.

[0114] Furthermore, after the construction is completed, reliability verification needs to be carried out simultaneously, including traditional indicator control verification (comparing the fitting effect of conventional indicators such as body length and area) and prediction ability verification (calculating the standardized root mean square error and prediction coefficient of determination through independent samples). Only when the verification results meet the condition of "standardized root mean square error < 25% and prediction coefficient of determination > 0.8" can the individual biomass statistical formula be used for subsequent calculations.

[0115] Furthermore, based on the definition of core indicators, the two basic net morphological parameters are substituted into the calculation logic of the core indicators to obtain the core indicator values ​​corresponding to the individuals to be counted. Then, the core indicator values ​​are substituted into the statistical formula that has been verified for reliability, and the biomass results of the individuals to be counted are directly output through formula calculation, ensuring high accuracy of biomass calculation results and meeting the needs of biomass statistics for a single individual.

[0116] In some embodiments, after calculating the individual biomass of the individual to be counted based on the core indicators, net perimeter of the organism, and net area of ​​the organism in step S150, the following steps S410 to S430 are included: Step S410: Determine the biomass level of the individuals to be counted based on their individual biomass. Step S420: Extract the morphological features of the individuals to be counted from the images of the individuals to be counted; Step S430: Generate a statistical report for the individuals to be counted based on their biomass level and morphological characteristics.

[0117] In this embodiment, the classification of biomass levels needs to be based on standards established according to the biomass distribution characteristics of individuals of the same species to be counted, and combined with the measured data of large, medium, and small-sized samples, the level thresholds are determined through statistical analysis. For example, the quartile method or the mean-standard deviation method is used to classify the biomass of individuals of the same species into three levels: small, medium, and large, or further refined into more levels according to the needs of ecological research, ensuring that the level classification conforms to the growth patterns of the species itself. During the classification process, the biological characteristics of the species need to be considered simultaneously, such as the difference in growth rate between snails and curved-shaped organisms; finally, based on the specific biomass value of the individual to be counted, the corresponding level label is matched to provide a classification basis for subsequent individual characteristic association analysis.

[0118] Furthermore, morphological feature extraction needs to consider both quantitative parameters and qualitative descriptions, comprehensively extracting key information based on image processing results. Quantitative features include calculated core and auxiliary parameters such as the organism's net perimeter, net area, body length, body width, outline area, and equivalent sphere diameter, accurately reflecting the quantitative dimensions of individual morphology. Qualitative features focus on species-specific morphological attributes, such as the degree of curvature and whether a closed-loop structure is formed in curved organisms, and the shell regularity and thickness-to-width ratio of snails, recorded through image observation and feature annotation. Finally, after extraction, all morphological features are associated with individual identifiers to form a structured morphological feature dataset.

[0119] Furthermore, the statistical report includes basic individual information, specifically sample number, species classification, and collection location; it then lists core biomass data, covering the calculated biomass values, corresponding grade labels, and key reliability parameters of the statistical formula to support data credibility; it also includes detailed morphological characteristics, with quantitative parameters presented precisely in tabular form and qualitative characteristics described in textual terms combined with image annotations; finally, a data comparison module can be added as needed, such as morphological differences between individuals of the same grade and correlation analysis conclusions between individual biomass and morphological parameters. Moreover, the report supports export in a structured format, facilitating subsequent batch data integration and in-depth research.

[0120] like Figure 15 As shown in some embodiments of this application, a system for counting the biomass of benthic organisms is provided. The system includes an acquisition module 1510, an extraction module 1520, a first module 1530, a second module 1540, a third module 1550, a determination module 1560, and a statistics module 1570. Specifically: The acquisition module 1510 is used to acquire images of the individuals to be analyzed. Extraction module 1520 is used to extract the outermost contour perimeter, the inner cavity contour perimeter, the area contained in the outermost contour, and the area of ​​the inner cavity contour from the image of the individual to be counted. The first module 1530 is used to calculate the net circumference of the organism of the individual to be counted based on the outermost contour perimeter and the inner cavity contour perimeter of the individual to be counted. The second module 1540 is used to calculate the net biological area of ​​the individual to be counted based on the area contained in the outermost contour and the area of ​​the internal cavity contour. The third module 1550 is used to calculate multiple candidate indicators based on the net perimeter and net area of ​​the organism; the multiple candidate indicators are used to characterize the volume of the individual to be counted. Module 1560 is used to determine the core indicator based on multiple candidate indicators; The statistics module 1570 is used to calculate the individual biomass of the individual to be counted based on core indicators, net perimeter of the organism, and net area of ​​the organism.

[0121] It should be noted that the benthic organism individual biomass counting system provided in this embodiment is based on the same inventive concept as the benthic organism individual biomass counting method described above. Therefore, the relevant content of the benthic organism individual biomass counting method described above also applies to the benthic organism individual biomass counting system. Therefore, it will not be repeated here.

[0122] In this embodiment, the system extracts the outermost contour perimeter, the inner cavity contour perimeter, the area encompassed by the outermost contour, and the area of ​​the inner cavity contour from the image of the individual to be counted. Based on the outermost contour perimeter and the inner cavity contour perimeter, the system calculates the net perimeter of the individual. Based on the area encompassed by the outermost contour and the area of ​​the inner cavity contour, the system calculates the net area of ​​the individual. Based on the net perimeter and net area, the system calculates multiple candidate indicators. Based on the multiple candidate indicators, the system determines the core indicator. Based on the core indicator, the net perimeter, and the net area, the system calculates the individual biomass. In this way, by extracting net morphological parameters, constructing candidate indicators, and screening core indicators, the system can fit the volume of benthic organisms, thereby improving the accuracy and efficiency of individual biomass statistics.

[0123] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method for calculating the biomass of benthic organisms.

[0124] like Figure 16 , Figure 16 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device includes: At least one battery; At least one memory; At least one processor; At least one program; The program is stored in memory, and the processor executes at least one program to implement the above-described method for calculating the biomass of benthic organisms.

[0125] This electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0126] The electronic devices according to embodiments of this application will now be described in detail.

[0127] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure. The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1700 and is called and executed by the processor 1600 to execute a method for calculating the biomass of benthic organisms according to an embodiment of this disclosure.

[0128] The input / output interface 1800 is used to implement information input and output. The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900); The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0129] This disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for calculating the biomass of benthic organisms.

[0130] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0131] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0132] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any related variations, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The above is a detailed description of the preferred embodiments of this application. However, the embodiments of this application are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of this application.

[0136] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A method of statistical determination of biomass of benthic organism individuals, characterized in that, The method includes: Obtain images of the individuals to be analyzed; Extract the outermost contour perimeter, the inner cavity contour perimeter, the area contained in the outermost contour, and the area of ​​the inner cavity contour from the image of the individual to be counted. Based on the outermost contour perimeter and the inner cavity contour perimeter of the individual to be counted, the net biological perimeter of the individual to be counted is calculated. The net biological area of ​​the individual to be counted is calculated based on the area of ​​the outermost contour and the area of ​​the internal cavity contour of the individual to be counted. Based on the net perimeter and net area of ​​the organism, multiple candidate indicators are calculated; the multiple candidate indicators are used to characterize the volume of the individual to be counted. Based on the multiple candidate indicators, the core indicators are determined; Based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism, the individual biomass of the individual to be counted is calculated.

2. The benthic organism individual biomass statistical method according to claim 1, characterized in that, The images of the individuals to be analyzed include images of benthic organisms with curved morphology and images of snail-like benthic organisms.

3. The benthic organism individual biomass statistical method according to claim 1, characterized by, The first candidate indicator among the multiple candidate indicators is obtained by the following formula: ； in, This indicates the first candidate indicator among the multiple candidate indicators. This represents the net area of ​​the organism. This represents half of the net circumference of the organism.

4. The method for calculating the individual biomass of benthic organisms according to claim 3, characterized in that, The plurality of candidate indicators also includes a second candidate indicator and a third candidate indicator; the second candidate indicator is obtained based on the ratio between the square of the net area of ​​the organism and half of the net perimeter of the organism; the third candidate indicator is obtained based on the square root of the product of the first candidate indicator and the second candidate indicator.

5. The method for calculating the individual biomass of benthic organisms according to claim 1, characterized in that, The process of determining the core indicator based on the multiple candidate indicators includes: Extract the initial individual biomass of the individuals to be counted from the images of the individuals to be counted; The candidate indicators are respectively subjected to linear fitting analysis with the initial individual biomass to obtain the linear fitting correlation coefficient of each candidate indicator; The core indicators are determined based on the candidate indicators whose linear fitting correlation coefficient is higher than a preset score.

6. The method for calculating the individual biomass of benthic organisms according to claim 1, characterized in that, The individual biomass of the individual to be counted is calculated based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism, including: Based on the aforementioned core indicators, construct the corresponding individual biomass statistical formula; Based on the net perimeter and net area of ​​the organism, the individual biomass of the individual to be counted is calculated using the individual biomass statistical formula.

7. The method for calculating the individual biomass of benthic organisms according to claim 1, characterized in that, After calculating the individual biomass of the individual to be counted based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism, the method further includes: The biomass level of the individual to be counted is determined based on the individual biomass of the individual to be counted; Extract the morphological features of the individuals to be counted from the images of the individuals to be counted; A statistical report for the individuals to be counted is generated based on their biomass level and morphological characteristics.

8. A system for counting the biomass of benthic organisms, characterized in that, The system includes: The acquisition module is used to acquire images of the individuals to be analyzed. The extraction module is used to extract the outermost contour perimeter, the inner cavity contour perimeter, the area contained in the outermost contour, and the inner cavity contour area of ​​the individual to be counted from the image of the individual to be counted. The first module is used to calculate the net circumference of the organism of the individual to be counted based on the outermost contour perimeter and the inner cavity contour perimeter of the individual to be counted. The second module is used to calculate the net biological area of ​​the individual to be counted based on the area contained in the outermost contour and the area of ​​the internal cavity contour of the individual to be counted. The third module is used to calculate multiple candidate indicators based on the net perimeter and net area of ​​the organism; the multiple candidate indicators are used to characterize the volume of the individual to be counted. The determination module is used to determine the core indicator based on the multiple candidate indicators; The statistics module is used to calculate the individual biomass of the individual to be counted based on the core indicators, the net perimeter of the organism, and the net area of ​​the organism.

9. An electronic device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform a method for calculating the biomass of benthic organisms according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform a method for calculating the biomass of benthic organisms as described in any one of claims 1 to 7.

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