206 results about "Species identification" patented technology

The invention discloses a device and a method for intelligently investigating types and quantity of fishes. The device comprises a self-adaptive sonar detection module, a three-dimensional image acquisition module, an image preprocessing module, a fish detection module, a main body segmentation module and an identification and classification module. The sonar module is integrated with a three-frequency-band transducer, can dynamically switch frequency according to fish school depth, and realizes target tracking counting and quantity estimation in combination with an algorithm; the image acquisition module constructs a fish school three-dimensional image model by combining a depth camera with a light attenuation compensation algorithm; the preprocessing module fuses acoustic and optical features and expands a data set; the detection and segmentation module is used for accurately positioning fishes and removing impurities; the identification and classification module realizes type identification based on transfer learning and supports incremental learning of new fingerlings. According to the method, through cooperation of multiple modules, the problems of insufficient detection precision, poor image quality and the like in traditional investigation are solved, efficient and accurate investigation of fish species and quantity is realized, and technical support is provided for fishery resource management and ecological protection.

The invention provides a wild animal and habitat remote sensing monitoring identification method and system, and belongs to the technical field of wild animal monitoring and habitat evaluation, and the method comprises the steps: obtaining a satellite remote sensing image based on the habitat range of a target research species, and delimiting the potential habitat range of the target research species; obtaining unmanned aerial vehicle remote sensing data in the range to obtain a first image feature vector identification activity trace, and determining a target research area of the target research species; processing species monitoring data monitored by an infrared camera in the target research area to obtain a second image feature vector; inputting the second image feature vector into a preset species identification model to obtain a species identification result; performing fusion analysis on the species identification result, the satellite remote sensing image and the unmanned aerial vehicle remote sensing data, and extracting multi-dimensional habitat variables; and inputting the multi-dimensional habitat variables and the species identification result into a preset habitat quality evaluation model to obtain a habitat quality evaluation result of the target research area.

The invention discloses a method for evaluating enhancement and release seedling resources based on environmental DNA polymerization analysis. The method comprises the following steps: carrying out gridding partition on a target water area, collecting a water sample through a designed sampling scheme, and carrying out DNA extraction and high-throughput sequencing to obtain species sequence information of each sampling point. Sequencing data is subjected to species identification by using a bioinformatics method, released species are identified, a spatial abundance model is established, and a preliminary distribution map is generated. And establishing a DNA degradation kinetic model in combination with water area environmental parameters, and carrying out reverse correction on abundance distribution. And through a resource inversion model coupled with hydrodynamics, analyzing biomass distribution characteristics and migration laws of the release group, and obtaining a resource evaluation result. And finally, a species environment preference model is constructed based on migration path analysis, an optimal release area is matched in a target water area, a scientific scheme including release point locations, opportunities and quantity is generated, and a whole-process technical support and a decision basis are provided for enhancement and release.

The invention discloses a remote monitoring system based on bird species identification and population number estimation, which comprises a front-end monitoring subsystem, a data transmission subsystem, a data analysis and processing subsystem and a user interaction subsystem, and is characterized in that the front-end monitoring subsystem is composed of a plurality of monitoring nodes; each monitoring node is provided with a high-definition camera, a sound sensor, a temperature and humidity sensor and an illumination sensor, the devices work cooperatively, and data of birds and the environment where the birds are located are collected in an omnibearing mode. According to the remote monitoring system based on bird type identification and population number estimation, manual intervention is greatly reduced through an automatic monitoring and analysis process, the monitoring efficiency is improved, the accuracy of bird type identification and population number estimation is remarkably improved based on an algorithm of deep learning and multi-modal data fusion, and the accuracy of bird type identification and population number estimation is improved. And real-time transmission and processing of data are realized through a remote monitoring system.

The invention provides a six-axis inertial data behavior identification universal platform method for cattle, pigs, sheep and other livestock, and is suitable for behavior monitoring and management scenes of various livestock and poultry animals. The method comprises the steps of animal identity and species recognition, corresponding behavior recognition template loading, six-axis data feature extraction, behavior judgment and state recognition, recognition result output, platform interface butt joint and the like. Uniform adaptation and efficient processing of different animal types and sampling strategies (including fixed frequency, intermittent sampling, event triggering and the like) are achieved through a modular structure and a parameterized configuration mode. The platform supports continuous expansion of newly added species and behavior templates, can be deployed on edge equipment or a cloud server, has low power consumption, high compatibility and cross-scene applicability, and is suitable for intelligent management requirements of health monitoring, oestrus recognition, abnormity early warning and the like of livestock such as dairy cattle, beef cattle, live pigs, sheep, goats and the like in a farm.

The invention relates to the technical field of biodiversity monitoring, and discloses a multi-region-oriented biodiversity intelligent monitoring system, which comprises a data acquisition module, a data transmission module, a data processing module, a data analysis module and a rule analysis module, infrared cameras are widely deployed in multiple areas, comprehensive biological activity data containing timestamps and area numbers are collected according to fixed intervals, and then the low-power-consumption LoRa technology is used for compression transmission, so that data richness is guaranteed, transmission efficiency is improved, and energy consumption is reduced; in the aspects of feature extraction and species identification, key features are scientifically and accurately extracted and species are identified by comprehensively applying a form and behavior feature extraction method and combining a constructed species feature database and an Euclidean distance algorithm, and the database is perfected by means of expert judgment on the condition that the species cannot be identified; and on the aspect of activity law analysis, not only can the active region and time of the same species be determined, but also the immigration and emigration periods of seasonal migration species can be accurately judged.

The present application relates to the technical field of biological sensing, and discloses a composite functional porous metal membrane and detection system for monitoring and identifying airborne microorganisms, and a method for monitoring and identifying airborne microorganisms. The monitoring and identification method comprises: applying an electric field to the composite functional porous metal membrane, causing the membrane to freely switch charge and capture airborne microorganisms to be tested; adding a fluorescent probe to the composite functional porous metal membrane, which binds with the microorganisms to be tested to obtain a fluorescent sample, and collecting imaging of light emitted after irradiation of excitation light, to achieve preliminary determination and addressing of the microorganisms to be tested; performing Raman spectroscopy scanning on individual bacteria, and comparing with a standard Raman spectrum library to achieve species identification of the individual bacteria. Relying on the "composite functional porous metal membrane," an integrated process of "selective enrichment-total bacteria monitoring-species identification" is realized; the present invention takes selective enrichment of the microorganisms to be tested as an entry point, lays the foundation for preliminary judgment accuracy and species identification accuracy, and improves the efficiency and precision of monitoring and identifying airborne microorganisms.

The invention provides a phytophagous insect food web DNA molecular data automatic analysis method and device based on high-pass sequencing and a storage medium, and relates to the field of molecular biological information detection.The method comprises the steps that sequence splicing, screening and species identification are carried out on obtained double-end sequencing data and local and downloaded DNA databases through an automatic system, and a DNA molecular database is obtained; generating an Excel table containing species names and a DNA bar code sequence file; performing comparative analysis on the double-end sequencing data by adopting matching splicing, and generating a contiguous group sequence based on a local DNA database; if the matching splicing cannot generate the effective sequence, generating a new gene file by adopting non-parameter splicing, and performing gene annotation in combination with the downloaded DNA database; all analysis steps are connected in series through standardized parameter input, including gene screening through threshold values and generation of insect recipe identification results. According to the method, the sequencing data can be subjected to full-process automatic analysis through a one-key command, and the efficiency of food web authentication high-throughput sequencing data processing is greatly improved.

The invention relates to a wildlife intelligent monitoring device and method based on multi-modal fusion and an ad hoc network, and relates to the technical field of wildlife monitoring. Through fusion of binocular infrared vision and microphone array hearing, more comprehensive and more accurate wild animal activity information can be obtained, and the limitation of a single sensor is overcome; intelligent false trigger screening and species identification are realized through a WS-YOLO algorithm of an equipment end, manual intervention is reduced, and data processing efficiency is improved; through the ad hoc network technology, the monitoring system can cover a wider network-free area, and equipment deployment is more flexible and is not limited by network infrastructures; by reducing false triggering, local intelligent processing and efficient data transmission, the cost of data storage, transmission and later manual analysis is reduced. The method provides a more powerful technical means for dynamic monitoring of wild animal populations, behavioral ecology research, biodiversity protection and the like, and has important ecological protection and scientific research values.

The invention provides a species detection and identification method based on a hybrid scaling strategy and a cascade architecture, which belongs to the technical field of image identification, integrates multiple technologies such as image preprocessing, neural network model construction and expansion, model transfer learning and fine tuning, optimizes the network structure by means of the hybrid scaling strategy, and improves the identification accuracy. A detection network and a recognition network are organically combined, species recognition tasks are concentrated in a region where detection is meaningful, fine-grained detection and accurate recognition of species are achieved, and the accuracy and reliability of species recognition are greatly improved. The hybrid scaling strategy realizes feature extraction optimization of targets with different scales by dynamically adjusting the depth, width and input resolution of the network; the cascade architecture reduces the influence of background interference on a classification task through progressive processing of positioning and then classification. In addition, an adaptive learning rate adjustment mechanism is introduced, and a verification set early stop strategy is combined, so that the convergence efficiency and generalization performance of the model are further improved.

Methods and systems for species identification are disclosed. The methods and systems include: obtaining first, second, and third trained artificial intelligence (AI) models; obtaining one or more runtime images including a subject; determining a first confidence level of a morphological group of the subject based on the first AI model; determining a second confidence level of a species of the subject based on the morphological group and the second AI model; in response to the second confidence level being lower than a predetermined confidence level; performing a genomic test for the subject based on the determined morphological group or the determined species of the subject; and identifying the species of the subject based on a test result of the genomic test based on the third AI model. Other aspects, embodiments, and features are also claimed and described.

The invention discloses a primer composition and a kit for identifying testudinate species and application of the primer composition and the kit. The primer composition comprises primers (SEQ ID NO.1-6) for targeted amplification of specific marker genes 16S, COI and Cytb of testudinate mitochondrial species and an enrichment universal primer (SEQ ID NO.7). According to the invention, full lengths of three mitochondrial genes 16S, COI and Cytb are taken as targets, a specific primer group added with an enrichment tag and an enrichment primer are designed, and a targeted nanopore sequencing method capable of realizing testudinate species identification is established and optimized, and the method can simultaneously capture testudinate mitochondrial species specific marker gene information of a family species level, and can be used for identifying testudinate species. The problem that the existing DNA bar code is difficult to accurately identify the testudinate species due to the lack of database information is effectively solved; and each marker gene can obtain a complete coding sequence, so that genetic information is richer, and the method can be used for geographical traceability analysis of testudinate species.

The invention discloses a species identification kit for animal mitochondrial bar code multiplex PCR amplification and nanopore sequencing, and relates to the technical field of species identification. The species identification kit comprises optimized primer pairs aiming at four hyper-variable genes (Cytb, COI, 16S rRNA and 12S rRNA) of mitochondria, by introducing R, Y, D and other degenerate bases at key sites, the binding capacity of the primers and template DNA of different species is remarkably improved, the problems that an existing universal primer is prone to mismatch, low in efficiency and even failed in amplification during cross-species amplification are solved, and the species identification kit has the advantages of being high in specificity, high in specificity and high in specificity. The method can be used for species identification of a mixed DNA sample containing at least two different animal species sources. The species identification primer combination is applied to preparation of an animal species identification kit, and the detection coverage range of the kit is expanded. According to the animal species identification method provided by the invention, a multiple PCR amplification system and a nanopore length reading sequencing technology are combined, congeneric sibling species with highly similar sequences can be distinguished, and the method has good accuracy.

The invention relates to a floating algae detection method, system and device. The method comprises the following steps: preprocessing a multi-vision original image sequence to obtain a multi-vision preprocessed image sequence; performing image splicing on the multi-vision preprocessed image sequence according to the boundary features to obtain a panoramic image; carrying out step-by-step edge identification and labeling on the panoramic image by adopting a multi-operator and region growing method to obtain a labeled image; and performing type identification and counting on the floating algae in the labeled image based on a stacked integrated learning method to obtain floating algae biological information. According to the method, image splicing is performed firstly, then edge recognition is performed, and multi-operator step-by-step edge recognition from low precision to high precision is performed on the panoramic image, so that the detection efficiency can be improved; in addition, the types of the floating algae in the annotated image are identified and counted based on the stacked integrated learning method, and the identification accuracy is higher than that of an existing single model.

The invention relates to an SNP (Single Nucleotide Polymorphism) marker for identifying silver pheasant species and application thereof, and relates to the technical field of biology. The SNP marker can effectively solve the current identification problem caused by the problem that the silver pheasants are hybridized, nestlings and female pheasants are difficult to distinguish, and provides solid technical support for silver pheasant protection, population monitoring and related scientific research.

The application discloses a biological individual species identification method based on a deep neural network, and relates to the technical field of zoology image recognition. Different direction image data of biological individual skull samples are collected as a data set, and the data set is divided into a "test set" and a "training set" according to a ratio of 8:2 based on a non-segmentation individual picture benchmark (the pictures of the same individual only exist in the same data set). Meanwhile, the training set images are subjected to data enhancement by using methods including but not limited to rotation, mirroring, changing brightness, blurring, random deletion and the like; the weight and value of the sample angle image data in a specific task are obtained through a corresponding algorithm, and the species identification result of the individual sample is obtained through a data fusion method.

This invention discloses a method, device, and storage medium for bird species identification based on bird calls. The method includes: (1) acquiring several segments of bird calls and preprocessing them; (2) filtering the power spectrum of the bird calls using two different filter banks, extracting the coefficients of the filtered signals, and then combining the two sets of coefficients, the short-time energy of the bird calls, and the short-time zero-crossing rate of the bird calls to form the feature vector of the current bird calls; (3) constructing a nonlinear classification model and using a prey optimization method to find the optimal kernel function in the nonlinear classification model; (4) inputting the extracted bird call feature vector into the nonlinear classification model for learning; and (5) extracting the feature vector of the bird call to be identified and inputting the feature vector into the learned nonlinear classification model to identify the bird species. This invention has low complexity and high accuracy.

The application discloses a kind of based on electronic nose's insect tea species identification method, belong to insect tea aroma detection technical field.The method includes: selecting training set and test set insect tea sample;Through the optimal parameter of single factor test screening carries out sample pretreatment and electronic nose detection, obtains sensor response characteristic value;Application principal component analysis (PCA) constructs category distribution model, obtains PCA distinction diagram;Application discriminant function analysis (DFA) constructs category discriminant model;Test set sample characteristic value is projected into PCA distinction diagram, and it is input DFA model to obtain discriminant result, and carries out visual verification.The method of the application is simple in operation, result is objective and reliable, can realize the accurate identification of different plant raw materials insect tea, and the overall accuracy reaches 91.84% by 49 unknown samples verification.

The invention discloses an algae species identification method and system based on a BEiT-CSA algorithm, and belongs to the technical field of computer vision. The method comprises the following steps: firstly, performing differential randomization adaptive enhancement on a microscopic image according to the form type of an algae body, calculating the comprehensive fidelity based on SSIM and normalized MSE, and only retaining samples with scores not lower than 0.7; the effective samples are input into a classification model with BEiT as a backbone, encoders of the fourth layer, the eighth layer and the twelfth layer are embedded into a cross-scale attention module, and hierarchical collaborative modeling of local details and global semantics is achieved through multi-scale feature generation, cross-scale cross attention calculation and a double fusion strategy. According to the method, the accuracy of 94.10% on a 214-type algae species data set is achieved, the capacity of distinguishing algae species with similar forms is remarkably improved, and meanwhile it is guaranteed that the biological authenticity of samples and the model robustness are enhanced.

The invention discloses a portable meat doping identification chip and a detection method, and the method comprises the following steps: placing a meat sample slice on a sample bearing layer of the chip, starting the chip to collect a terahertz time-domain spectral signal, and calculating optical parameters of a meat sample in a frequency band of 0.2-2.0 THz; performing principal component analysis on the high-dimensional data of the optical parameters in the whole frequency band to obtain feature vectors; and inputting the feature vector into a pre-trained artificial intelligence model, respectively performing qualitative identification of meat species and quantitative prediction of the doping content, and outputting an analysis report containing a species identification result and a doping ratio.

The invention discloses a fish body morphological index measuring device, which relates to the technical field of fish body morphological characteristic measuring equipment, and comprises a rack, a measuring mechanism and a conveying mechanism, the measuring mechanism can measure the body weight and the fish body shape indexes, and the fish body shape indexes comprise one or more of the body length, the body height, the head length, the kiss length, the caudal peduncle length and the caudal peduncle width; the conveying mechanism is arranged on the machine frame, an outlet of the conveying mechanism is communicated with the measuring mechanism, the conveying mechanism can convey to-be-measured fishes to the measuring mechanism, and the machine frame and the walking mechanism at the bottom of the machine frame are arranged, so that the fish body shape index measuring device can be flexibly transferred to adapt to fish body shape index measuring and collecting in the field on-site environment. The interference on the fish wild protection population caused by transferring the fish sample to a laboratory to carry out species identification and morphological characteristic index determination is avoided.

The invention discloses an environmental DNA species monitoring method based on Chinese sturgeons, and relates to the technical field of biological gene detection.According to the environmental DNA species monitoring method based on the Chinese sturgeons, mitochondrial whole genomes of the Chinese sturgeons are determined through a method combining a second-generation library-building sequencing technology and a third-generation library-building sequencing technology, and the whole genomes of the mitochondria of the Chinese sturgeons are determined through probe design and PCR amplification sequencing. The sequence of the whole mitochondrial genome of the Chinese sturgeons is combined for comparison, and the specificity difference between the Chinese sturgeons and other related sturgeons is distinguished, so that the efficiency of detecting rare and endangered species Chinese sturgeons by an environmental DNA method is improved, and the accuracy of species identification is improved.

The invention discloses a primer, a kit and a method for identifying species of a sparrow. The primer comprises a primer SEQ ID NO.1 and a primer SEQ ID NO.2 of a targeted COI region, a primer SEQ ID NO.1 and a primer SEQ ID NO.2 of a targeted COI region, and a primer SEQ ID NO.2 of a targeted COI region, the primers of the target D-loop region are shown in SEQ ID NO.3 and SEQ ID NO.4. The invention also discloses a kit for detecting the target D The invention also provides a corresponding kit and a method for identifying the species of the sparrow by using the primer. According to the specific PCR primer for the sparrow, a specific gene sequence of a targeted species is adopted, and accurate detection of DNA of the sparrow can be stably realized in a complex environment medium. The method has the advantages of being easy and convenient to operate, high in sensitivity, rapid in detection and the like, is suitable for scenes such as species identification and environmental DNA monitoring, provides a reliable technical means for ecological research, species protection and dynamic monitoring of the ecological environment of the sparrow, and has wide application prospects and practical values.

The invention relates to a specific real-time fluorescent PCR (polymerase chain reaction) amplification primer, a probe and a detection method for identifying lutjanus purpureus, and belongs to the technical field of molecular detection and species identification. According to the invention, the specific primer and the probe for lutjanus purpuratus are designed, and the real-time fluorescent PCR technology is combined, so that the lutjanus purpuratus can be quickly and accurately identified. The method has the advantages of high specificity, high sensitivity, simplicity and convenience in operation and the like, and a reliable molecular biological method is provided for species identification of lutjanus purpurea.

The invention relates to a zooplankton automatic enrichment and physiological phenotype and species identification integrated device, which comprises a zooplankton enrichment module, a physiological phenotype data acquisition module, a species data acquisition module and a data processing identification module, the physiological phenotype data acquisition module comprises an automatic sorting mechanism and a plurality of observation bins, the temporary storage bin is communicated with the observation bins through the automatic sorting mechanism, the species data acquisition module comprises a treatment liquid adding mechanism, a plurality of treatment bins, a sampling pump and a second collector, and the observation bins are communicated with the treatment bins in a one-to-one correspondence mode through pipelines provided with liquid supply pumps. The first collector and the second collector are both in signal connection with the data processing identification module. The method has the advantages that the efficiency of collecting and analyzing the basic information of the zooplankton in the water body is improved, and the pollution state and the future evolution pattern of the water body can be comprehensively predicted through the two-dimensional information of the physiological state and the variety diversity.

The invention relates to the technical field of image recognition, in particular to a mosquito type recognition method and system fusing an image and spatio-temporal information, and the method comprises the steps: obtaining an image of a resident roof in a preset time period in real time, and dividing the image into a plurality of regions; the method comprises the following steps: acquiring a light area at each sampling moment, and according to a change characteristic of a gray value of each pixel point in each area along with time, a gray distribution characteristic and a gray difference degree of each area at each sampling moment and a motion characteristic of each area relative to a previous moment, combining a gray distribution characteristic of the light area closest to each area; and acquiring the mosquito gathering degree of each area at each sampling moment according to the distance between each area and the nearest bright area and the distance between each area and the nearest bright area, so as to acquire recognizable areas at all sampling moments, and further recognize the types of mosquitoes. According to the method, the area where the mosquito is located is positioned more accurately, so that the mosquito identification accuracy is improved.

The invention discloses a DNA language model optimization method and system based on unsupervised noise contrast learning, and relates to the technical field of species identification by using DNA barcodes. The embedded vectors from the same sequence are used as positive samples, and the embedded vectors from different sequences are used as negative samples; in order to increase sample diversity, global and local feature noise layers are introduced, and positive sample embedded vectors are disturbed. And inputting the enhanced embedded vectors into an encoder, and mapping similar enhanced vectors to similar embedding spaces by integrating information and capturing really important modes in a sequence. By mixing positive and negative samples of an anchor point sample, a difficult negative sample with the characteristics of the two samples is constructed, so that the training difficulty is continuously increased, and the discrimination capability of the model is enhanced; the model is optimized by using an unsupervised comparative learning loss function with positive sample similarity penalty, so that the embedding space distribution of the model is effectively enhanced, and the accuracy of the model in DNA bar code species level classification is remarkably improved.

The present application relates to the technical field of biological fermentation detection, and discloses a kind of biological fermentation product kind identification method and system based on image recognition.The pixel offset of real-time image relative to background reference image is extracted to generate the first characteristic vector field;Response liquid level fluctuation obtains the time sequence image sequence of inner wall in preset boundary area, and the residual coverage rate decay rate constant is extracted to generate the first characteristic index;Based on real-time image gray information entropy, generate environment sampling operator.Three nonlinear weighted coupling generates species identification comprehensive index, realizes the dynamic weight inhibition of airspace scattering interference and the synchronous quantification of interface adhesion dynamics characteristics, guarantees the stability of species identification conclusion under complex fermentation conditions.

The present invention discloses a method, system, device, and medium for viral species identification. The method comprises: obtaining an original reference database comprising non-redundant viral species and subspecies sequences; breaking sequences in the original reference database and comparing them with the original reference database, replacing sequences that match more than one instance in the original reference database to obtain a first reference database; aligning raw data from second- and third-generation sequencing to the first reference database, and identifying species based on abundance; breaking sequences in the original reference database and comparing them with the original reference database, replacing matched sequences to obtain a second reference database; aligning the spliced ​​genomic data to the second reference database, and identifying species based on bit fractions. The present invention designs a standardized analytical process that directly utilizes a reads data alignment strategy for viral species identification. The present invention can identify a wide range of species with high accuracy down to the subspecies level, and achieves a high and rapid identification rate.

The invention relates to the technical field of plant ecological monitoring and computer vision, and discloses a grassland dominant species identification method based on unmanned aerial vehicle remote sensing and deep learning, and the method comprises the steps: carrying out the fusion processing of a to-be-identified unmanned aerial vehicle remote sensing image of a target grassland region, and obtaining a to-be-identified fusion image; inputting the fusion image to be identified into a trained species identification model to obtain a species classification result of the target grassland area; combining the species classification result with the space coordinates of the target grassland area to generate a species distribution diagram of the target grassland area; according to the method, the problem of insufficient species identification precision in a complex grassland environment can be solved, manual investigation cost and subjective deviation are reduced, grassland dominant species identification accuracy and spatial analysis capability are improved, and refined data support is provided for grassland ecological protection.