62 results about "Species classification" patented technology

The invention relates to the technical field of image processing, and discloses a mangrove forest ecological information extraction method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring overlooking and side-looking photos through an unmanned aerial vehicle; correcting the overlook picture by using the coordinate information and superposing the overlook picture to the remote sensing image; generating a photographing direction pattern layer based on the side view photo; establishing a mangrove forest species interpretation mark library by using a random forest algorithm; classifying selected samples to generate a species distribution diagram; and finally evaluating the mangrove forest ecological function to form ecological monitoring data. According to the method, multi-angle and multi-level high-precision ecological information extraction of the mangrove forest is realized, so that a more accurate and comprehensive scientific basis is provided for species classification, ecological system function evaluation and protection management of the mangrove forest.

The invention relates to a wild animal monitoring image hierarchical classification method based on a species classification tree, and the method comprises the steps: carrying out the classification of five classification levels, namely the outline, the objective, the family, the genus and the species, of the species classification tree, and reducing the labor cost of a rechecking model result through providing richer species judgment information. According to the method, a soft decision and path correction strategy is introduced by utilizing a hierarchical relationship between categories, so that the accuracy and consistency of hierarchical classification are improved. When classification at fine-grained species levels is wrong, a coarse-grained result provided by the model also has a reference value.

The invention relates to a medical image processing technology, and discloses a pan cancer species histopathologic full-slice classification method and device, which is a trained multi-modal pan cancer species classification model based on histopathologic full-slice image data and full-slice diagnosis report pairing data of various tissue parts and various cancer species, and has the advantages of wide application range and high efficiency. And full slices of various pathological tissues can be classified. For classification tasks of different tissue parts, only text prompts (prompts) are used, i.e., a candidate cancer species list is set, and zero-sample full-slice classification can be realized without training. And for rare cancer species, full-slice classification can be carried out. Besides, the model has high expansibility, in order to better adapt to downstream tasks, better classification performance can be achieved by combining slice embedding with a linear classifier for use, freezing a slice embedding part and only finely adjusting the linear classifier, and the requirement for the training data size is low.

The invention discloses a host exogenous nucleic acid identification method based on a high-throughput sequencing technology. The method comprises the following steps: acquiring sequencing data of a to-be-detected sample; filtering the sequencing data twice by using a host reference genome database to remove a host sequence; species classification is performed on the sequencing data after host filtering, and interference of homologous species sequences is reduced; performing sequence comparison by utilizing an identified species reference genome to obtain an accurate species sequence; low-complexity sequence removal is carried out on the compared sequences, so that the influence of sequence preference is reduced; and carrying out secondary comparison on the species reference genome by using BLAST or comparing the species reference genome to an NT library. By applying the method, the exogenous nucleic acid sequences of different host samples can be accurately and specifically identified, and effective analysis of biomass samples of high-host background and low-exogenous species is realized.

The invention provides a seed felt species classification analysis method and system based on a neural network model, and relates to the technical field of agricultural management, and the method comprises the steps: obtaining a high-definition digital image of a seed felt sample, extracting a single seed region in a seed felt, obtaining a binary mask image of a seed through contour extraction and morphological filtering, and carrying out the classification analysis of the seed felt species. Constructing a seed image data set; constructing a multi-scale pyramid pooling convolutional neural network, and based on the seed image data set, extracting seed morphological features through progressive feature spectrum up-sampling and cross-layer feature fusion; inputting the seed morphological characteristics into a long-short-term memory network of a gating circulation unit, and establishing a mapping relation between a seed morphological characteristic sequence and species categories; fusing the multi-scale pyramid pooling convolutional neural network and the gating circulation unit long-short term memory network, constructing a plurality of independent species classifiers, constructing a classification decision model, inputting a to-be-classified seed felt into the classification decision model, and determining seed felt species.

The invention discloses a plankton dynamic monitoring method and device based on deep learning and a medium, and the method comprises the steps: inputting a preprocessed image into a target detection model for detection, and the target detection model comprises the steps: carrying out the multi-level feature extraction of the preprocessed image through a backbone network, and obtaining a plurality of feature maps of different levels; respectively inputting the plurality of hierarchical feature maps into a feature enhancement unit to extract various types of semantic information, and performing path fusion and feature re-calibration on each layer of feature map to obtain enhanced different hierarchical feature maps; inputting the feature maps of different levels into a multi-scale fusion unit for spatial semantic alignment and cross-level feature fusion; inputting the fused feature map into a decoupling detection head for target classification and bounding box regression prediction; and the plankton is positioned based on a result output by the detection head to obtain the type of the plankton. According to the invention, the accuracy of category classification and positioning is improved based on the architecture of the target detection model.

A crop species identification system based on satellite telemetry data is disclosed, which includes: a receiver module, receiving plural telemetric vegetation indexes of a target area; a data cleaning module, cleaning anomalous data in the telemetric vegetation indices, to correspondingly generate cleaned index data; a feature extraction module, including at least two different convolution kernels for mapping the cleaned index data into at least two feature scale mapping data which respectively correspond to the convolution kernels, performing a pooling operation of the feature scale mapping data to generate a pooled data, and concatenating the at least two feature scale mapping data and the pooled data into concatenated data; and a classification module, including a fully-connected layer, for extracting features of the concatenated data, to generate a species classification result for the target area.

The invention provides a method and a system for identifying migrated insects in the air based on an insect radar and a high-altitude lamp. The method comprises the following steps of: 1, receiving four biological parameters of flapping frequency, body length, body width and body weight of each migrated insect in the air monitored by the insect radar in real time; 2, the received four biological parameters are input into an optimal insect type classification model, the type of each aerial migratory fly insect is obtained, and the optimal insect type classification model is determined according to the counted types of the insects trapped by the high-altitude lamp within the recent T3 time. According to the invention, real-time accurate identification of insect individual types can be realized for target insects captured by an insect radar; the real insect condition information is monitored in real time according to the received data of the insects trapped by the high-altitude lamp, the optimal insect type classification model is determined according to the real insect condition information, it is guaranteed that the optimal insect type classification model is matched with the real insect condition, and good expansibility and broad-spectrum adaptability are achieved.

The invention relates to the technical field of regional marine environment evaluation, in particular to a method for predicting the seawater quality pollution degree based on sensitive phytoplankton. Comprising the following steps: collecting a water body sample of surface seawater in an offshore area; dNA in the water body sample is filtered and extracted, and high-throughput sequencing analysis is carried out after PCR amplification; performing species classification annotation on each sequence, and constructing a data matrix according to obtained phytoplankton OUT data; calculating a Spearman correlation coefficient; constructing an inter-species correlation matrix, and selecting proper species to construct a co-occurrence network; exporting edge data and point data, and screening nodes with the maximum betweenness centrality or the highest tight centrality as key species; calculating an organic pollution index (OPI) and carrying out Spearman correlation analysis to determine OPI sensitive phytoplankton; and establishing a prediction model for prediction to obtain a predicted environmental pollution index. The method has the advantages that model complexity and noise influence are reduced; the prediction accuracy reaches 100% when the water quality is seriously polluted.

The invention discloses a water pollution source identification and source tracing method based on an environmental DNA macro bar code, which comprises the following steps: S1: eDNA sample collection: collecting a water sample through an alcohol preservation sample method, and collecting genetic materials in an aquatic environment by collecting the water sample and filtering instead of sampling an organism; s2, eDNA extraction: extracting eDNA by a CTAB (cetyltrimethyl ammonium bromide) method or a kit. According to the water pollution source identification and traceability method based on the environmental DNA macro bar code, through an eDNA macro bar code detection method, the specialty level requirement for species classification of researchers is not high; especially for plankton, part of benthic organisms, larvae and other objects which are difficult to identify by non-professionals, the eDNA macro bar code detection method has relatively high operability; for rare and endangered organisms with a small number of individuals, the eDNA macro bar code detection method does not need to catch target organisms, so that the operation process is greatly simplified.

The invention discloses a vegetation species identification method for enhancing AI computing power and time sequence tracing, and the method comprises the steps: collecting and preprocessing multi-source remote sensing data, and constructing a vegetation classification data set with time-space alignment and uniform resolution; generating a vegetation mask based on the vegetation classification data set, obtaining a standardized sample slice, and constructing a training data set in combination with spectral features; performing time-phase-sharing processing on the training data set based on vegetation phenological characteristics, and outputting a preliminary vegetation classification result; performing object-level optimization on the preliminary vegetation classification result to obtain an optimized vegetation classification result; and correcting an optimized vegetation classification result in combination with topographic data, generating a vegetation species classification map of a target year, and realizing vegetation dynamic change inversion in a specified time period through transfer learning. Therefore, traditional resolution limitation can be broken through, the classification error problem caused by independent use of multi-source data is solved, the distinction degree of complex vegetation types is remarkably improved, and historical vegetation dynamic backtracking analysis is supported.

The invention relates to the technical field of metagenomics, discloses an automatic analysis method and system for pit mud metagenomic data, and aims at solving the problem that an existing method is poor in efficiency and accuracy, and the scheme mainly comprises the steps that a sequencing data type, a file path and analysis parameters are received; performing quality control on the original offline data; sequence assembly is carried out, and a contigs file is generated; carrying out assembly quality evaluation on the contigs file; carrying out genome binning by using at least two binning tools; integrating output results of the binning tool, and performing optimization based on a preset integrity threshold value and a preset pollution degree threshold value to obtain an optimized binning genome data set; evaluating and optimizing the integrity, the pollution degree and the strain heterogeneity of the binning genome; calculating coverage and relative abundance; performing species classification annotation and function annotation; and integrating the result data of the previous steps to generate an analysis report. According to the method, automatic analysis of metagenome data is realized, and the analysis efficiency and accuracy are improved.

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.

The invention discloses a target pathogen detection method based on a feature vector hash table, software and application. Based on the combination of a deep learning neural network model and a K-mer sequence comparison technology, a K-mer sequence is converted into a feature vector considering local features and global features so as to predict possible species classification information. In the actual operation process, only the belonging-level reference hash table corresponding to the model prediction result needs to be loaded, and the whole genome reference database does not need to be loaded. Compared with the traditional method, the method provided by the invention has the advantages that the memory is reduced by 90% when the 136GB data is processed, the analysis time is shortened from more than 3 minutes to less than 10 seconds, and huge potential and practical application value are shown in large-scale sequencing application.

This invention relates to a method for species classification in wildlife monitoring images based on point context, belonging to the field of image classification technology. The method involves modeling the correlation between monitoring points and species, assigning weights to samples based on the strength of the correlation, and decoupling the point classification and species classification tasks to learn environmental and species characteristics. By using the environmental information represented by the points as context for species classification, the method enhances the model's understanding of the relationship between species and habitat environments, achieving effective generalization to out-of-distribution data.

This application relates to the field of computer vision technology, and in particular to a method for wildlife identification and tracing using infrared image enhancement and multi-scale fusion. The method includes: acquiring raw images from an infrared camera and enhancing the raw images using Retinex and contrast-limited adaptive histogram equalization; performing animal target detection, feature extraction, and feature fusion by optimizing the YOLOv8 model and using a multi-modal fusion Transformer to obtain multi-scale fused features; identifying species categories using a pre-defined primary species classification model and extracting and matching individual biometric features of key species using a pre-defined secondary individual identification model to generate individual identifiers for corresponding animals; and automatically associating the species identification results, individual identifiers, and multi-source forestry data to generate an intelligent decision-making report for wildlife population analysis and conservation management. This application contributes to achieving high-precision, automated wildlife identification and tracing.

The invention provides an urban tree species classification method, system and device, and belongs to the technical field of tree species classification, and the method comprises the steps: firstly, selecting candidate wavebands in a red-edge spectrum range, traversing all dual-waveband combinations, and defining a vegetation index through a preset formula; secondly, for each index, generating a vegetation mask by using a plurality of candidate thresholds, and calculating a confusion matrix evaluation index by comparing the vegetation mask with a real mask; and finally, based on the index, automatically screening out an index with optimal performance and a threshold pair. Wherein the index defined by the optimal dual-band combination is the final vegetation index, and according to the final vegetation index, classification and identification of urban tree species are carried out through a machine learning model. The method solves the problem that classification of the classic vegetation index on the urban tree species is not accurate.

The invention relates to a solanaceae stress resistance multi-omics database, a construction method thereof, equipment and a medium. A construction method of a solanaceae stress resistance multi-omics database comprises the following steps: S10: downloading solanaceae plant multi-omics original data, and obtaining a standardized metabolic pathway annotation file; s20, filtering low-quality sequences, unifying metadata naming rules, and performing semantic standardization processing; s30, performing differential expression gene analysis and screening, constructing a gene-metabolite co-expression network, and generating a visual map; s40: constructing a MySQL relational database to store structured data such as gene expression and metabolites, and establishing a gene-metabolite-protein interaction network model; and S50, implementing a composite index to accelerate high-frequency query, performing database horizontal fragmentation according to species classification, and deploying middleware to realize distributed data transparent access. According to the solanaceae stress-resistant multi-omics database and the construction method, equipment and medium thereof, a special database system supporting high-throughput data management and deep mining is constructed.

The invention discloses an active living object-oriented fresh food weighing data edge calculation optimization method, and belongs to the technical field of fresh food weighing, and the method comprises the following steps: collecting weighing time sequence data of fresh food species in high, medium and low vigor states, extracting quantitative characteristics, and calculating the weight of the fresh food species in the high, medium and low vigor states; meanwhile, a freshness classification rule and a species classification rule are trained through machine learning to establish a rule base, and then filtering and calculation strategies are set according to the freshness rule; the method comprises the following steps: acquiring an image through a camera to judge the type of a living object, loading a corresponding rule library from an edge device species feature library, and when the classification confidence is lower than 90%, secondarily deducing and matching species classification rules through a weight fluctuation mode; the invention discloses an active living object-oriented fresh food weighing data edge calculation optimization method, which can improve the weighing precision and greatly improve the weighing efficiency through species classification, freshness judgment and secondary identification.

The invention discloses an animal parasite intelligent diagnosis system and method based on artificial intelligence. The system comprises a terminal device, a terminal device used for obtaining a microscopic image of an animal parasite, and a cloud server used for receiving the microscopic image. The cloud server deploys a parasite target detection model and a parasite type classification model; the parasite target detection model processes the received microscopic image and outputs bounding box coordinate information in the image; cutting out corresponding suspected area image blocks based on the bounding box coordinate information; inputting the suspected region image blocks into a parasite type classification model to obtain an authenticity discrimination result and a parasite type identification result of each suspected region; according to the authenticity judgment result and the parasite type recognition result, a diagnosis report is generated and returned to the terminal equipment for display, the problems that special equipment is expensive and terminal application computing power is insufficient are solved, and unification of high performance, low cost and high portability is achieved.

The application discloses a pangolin species identification method based on nail piece element characteristics, which comprises the following steps: step S1: collecting nail piece samples covering existing 10 kinds of pangolin species, and obtaining initial data of multi-element characteristics of the nail piece samples; step S2: identifying the species of the nail piece samples by a molecular biology method, and obtaining the species labels of each nail piece sample; step S3: constructing an "element characteristic-species label" standard data set; step S4: selecting a table prior fitting network as a core machine learning algorithm, training a model by using training set data, and constructing the model; and step S5: identifying whether a to-be-detected sample belongs to a pangolin nail piece according to morphological judgment. The method has a single sample data collection time of less than 60 seconds, does not need to damage the sample, is used for nail piece identification of samples without prior clues, and the overall accuracy of a global ten-species classification model reaches 0.93.

The application provides an aerial migration insect identification method and system based on an insect radar and a high-altitude lamp, the method comprising the following steps: 1, receiving the wing beat frequency, body length, body width and body weight of each aerial migration insect monitored by the insect radar in real time; 2, inputting the four biological parameters into an optimal insect species classification model to obtain the species of each aerial migration insect, wherein the optimal insect species classification model is determined according to the insect species captured by the high-altitude lamp in the latest T3 time. The application can realize real-time accurate identification of the species of the target insect captured by the insect radar, can monitor real insect information in real time according to the received insect data captured by the high-altitude lamp, and can determine the optimal insect species classification model according to the real insect information, so that the optimal insect species classification model is matched with the real insect information, and the application has good expansibility and broad spectrum adaptability.

The invention relates to the technical field of smart agriculture, and provides an unmanned aerial vehicle hyperspectral technology-based pecan tree species classification method, which comprises the following steps of: obtaining a hyperspectral image, and performing image cutting on the hyperspectral image to obtain a plurality of block images with specified sizes; analyzing a single hickory tree variety accurately, performing first feature extraction on the block image to obtain first feature information corresponding to the block image, and performing second feature extraction on the spatial feature to obtain second feature information corresponding to the spatial feature; feature information corresponding to the target is accurately collected through two times of feature extraction of different dimensions, the block images are classified based on the first feature information and the second feature information, classification information corresponding to the block images is obtained, and the classification information comprises pecan tree species information; based on the block images and the classification information, the pecan tree species classification image is obtained, an accurate pecan tree species classification result can be obtained, planting distribution of pecan tree species in the forest land is analyzed, and sustainable development of the forest land is promoted.

The invention discloses a pathogen gene sequence detection and analysis method based on nanopore sequencing, and relates to the field of customs quarantine, and the method comprises the following steps: carrying out nanopore sequencing on a collected sample to obtain original sequencing data, and carrying out quality screening to obtain a high-quality sequence. Species classification and mutation recognition are completed, and pathogen types and mutation characteristics are extracted. And constructing a propagation map based on the classification information, the mutation information, the sampling time and the sampling place, and forming a dynamic propagation structure chart. And calculating a propagation path score between the samples through graph diffusion modeling. And constructing a Bayesian causal model in combination with mutation characteristics and propagation scores, and reasoning the posterior probability of high-risk events caused by pathogens. And generating a risk level label according to the posterior probability, outputting a pathogen analysis report and early warning information, and realizing integrated analysis of rapid detection and risk assessment.

The invention discloses an algae community detection method and system based on polarized light characteristics, and relates to the field of water environment monitoring, and the method comprises the steps: obtaining an algae single particle polarization scattering signal through multiple observation angles, resolving a Stokes parameter, further extracting a polarization phase to construct a polarization phase divergence matrix, and carrying out the structural representation of the polarization difference between the angles; meanwhile, a morphological complexity index is obtained through multi-angle scattering intensity statistics and is used for depicting scattering distribution characteristics; on the basis, a polarization-form coupling coefficient is provided, polarization phase differences of different angle combinations are subjected to weighted convergence and modulated by form complexity, and stable fusion of polarization information and form statistical information is achieved; and the coupling coefficients are subjected to statistical convergence in a time window to form an algal group cooperation index, so that unified output of single-particle species classification and community risk early warning is realized. Therefore, the distinction degree, the stability and the time sequence characterization capability of algae community detection are improved.

This application relates to the field of image processing technology, and discloses a method and system for extracting mangrove ecological information based on an unmanned aerial vehicle. The method includes: obtaining top-down and side-view photos through the unmanned aerial vehicle; correcting the top-down photos using coordinate information and overlaying them on the remote sensing image; generating a photo-taking direction layer based on the side-view photos; establishing a mangrove species interpretation marker library using the random forest algorithm; selecting samples accordingly for classification to generate a species distribution map; and finally evaluating the ecological functions of the mangroves to form ecological monitoring data. This application realizes the extraction of high-precision ecological information of mangroves from multiple angles and at multiple levels, thereby providing a more accurate and comprehensive scientific basis for the species classification, ecological system function evaluation, protection and management of mangroves.

The application discloses a pathogen gene sequence detection and analysis method based on nanopore sequencing, and relates to the field of customs quarantine.The method comprises the following steps: nanopore sequencing is performed on collected samples, original sequencing data is acquired, quality screening is performed, and high-quality sequences are obtained; species classification and mutation recognition are completed; pathogen types and mutation characteristics are extracted; a transmission map is constructed based on classification information, mutation information, sampling time and location, and a dynamic transmission structure diagram is formed; a graph diffusion modeling calculation is performed to calculate the transmission path score between samples; a Bayesian causal model is constructed by combining the mutation characteristics and the transmission score, and the posterior probability of a pathogen causing a high-risk event is inferred; a risk level label is generated according to the posterior probability, a pathogen analysis report and early warning information are output, and integrated analysis of rapid detection and risk assessment is realized.

The embodiment of the present invention relates to the technical field of pollen identification, and discloses a method for intelligent identification of pollen images, including: obtaining a pollen image to be identified; extracting a first pollen image of each pollen to be identified according to the position information of each pollen in the pollen image to be identified; inputting the second pollen image of each pollen to be identified and the acquisition time of the pollen image to be identified into a species classification model for identification to determine the species information of each pollen to be identified, and outputting the pollen image of each pollen to be identified and the corresponding species information. The intelligent identification method of pollen images in the embodiment of the present invention first extracts a single pollen high-definition image through a pollen high-definition image extraction model, which can avoid impurity images and extract high-definition feature information of pollen; further, the pollen classification model fused with phenological features is used to accurately classify pollen species, which establishes a high-precision, multi-species pollen image intelligent identification method based on phenological classification.

The invention provides a biological classification character related gene identification method, device and equipment, and relates to the technical field of biological gene identification, and the method comprises the following steps: obtaining gene sequence data and image data of a target organism; a classification model is constructed, a bilinear convolutional neural network is adopted to perform feature extraction on image data to obtain macroscopic image features, a hierarchical attention mechanism model is applied to gene sequence data, and a word-level and sentence-level attention mechanism is used to extract microscopic gene features in a gene sequence; the macroscopic image features and the microscopic gene features are fused, and an incidence matrix is generated through an attention mechanism; performing convolution pooling processing on the incidence matrix through a convolutional neural network, extracting joint features, and completing a species classification task by using a multi-layer perceptron; and outputting a final species classification result, and identifying a gene most related to species classification. According to the method, the genetic characteristics of the organism can be more comprehensively understood, and the model is helped to more accurately position and analyze key genes.

The invention relates to an automatic birdsong species identification method and system based on a random forest model. The method comprises the following steps: firstly, selecting a target species, and acquiring chirp data of different birds; carrying out feature extraction on the bioacoustic feature data to obtain a bioacoustic feature index; then, constructing a sample data set based on species classification and biological acoustic characteristic indexes of the birds; training a random forest model; calculating an AUC value as an evaluation index of prediction precision; and inputting new unknown twitter data into the model, outputting a prediction result, repeating the steps, averaging the prediction and evaluation results of the model, and obtaining and outputting a final prediction result and a precision evaluation result. The bird species identification method has the advantages that an effective method is provided for bird species identification, the prediction accuracy is high, the robustness is high, the over-fitting risk is small, species identification can be well conducted through birdsong features under the condition that additional parameter adjustment is not needed, the system composition is clear and simple, and operation is convenient.