36 results about "Morphological trait" patented technology

The invention relates to the technical field of medical image processing and computer vision, in particular to a parasitic ovum microscopic image detection method and system based on polymorphic prior, and the method comprises the steps: obtaining a to-be-detected microscopic image, and carrying out the feature extraction of the to-be-detected microscopic image through a convolutional neural network, and obtaining an initial feature map; constructing a polymorphic convolution kernel library based on preset biological morphological characteristics of the parasitic ova; performing deep convolution and feature fusion operation on the initial feature map by using a polymorphic convolution kernel library to generate a space attention map; performing feature enhancement processing on the initial feature map by using the spatial attention map to obtain an enhanced feature map; and performing bounding box regression and category prediction on the enhanced feature map to obtain a parasitic ovum detection result. According to the method, morphological priori and attention mechanisms are introduced, so that the problems of egg form similarity, background interference and the like are solved, accurate and robust automatic detection is realized, and the clinical diagnosis efficiency is remarkably improved.

The invention discloses a morphological feature-based cell segmentation and cell nucleus identification method for a turned unstained bone tissue pathological image. The method comprises the following steps of: 1, eliminating tool marks by adopting a tool mark elimination method combining local frequency domain analysis and directional suppression; 2, performing cell segmentation by using a K-means method, and performing morphological expansion and topological analysis on a segmented single cell image to identify a cell nucleus in the single cell image; step 3, calculating morphological characteristic indexes of each region; the method comprises the following steps: establishing a multi-dimensional Gaussian mixture model according to existing bone cell labeled sample information, performing outlier detection according to statistical data analysis, and removing results which do not conform to cell morphology; classifying different regions, and removing non-cell regions; by calculating morphological characteristic indexes of each region, different regions are distinguished according to the indexes, and cells are preliminarily screened. According to the method, high-precision cell segmentation and cell nucleus identification can be carried out on the cut undyed bone tissue pathological image.

A method for detecting the extent of microplastic accumulation in bivalve organisms in extreme deep-sea environments is provided. This method is performed through sampling of biological communities to represent the community structure of that biological bed layer, then morphological characterization statistics are conducted to classify the age stage data of individual bivalves, multivariate factor analysis is used to obtain the individual bivalves with the greatest degree of contribution in each classified age stage, subsequently tissue-specific microplastic extraction is performed, the morphology of seafloor microplastics is restored and streamlined identification of full-size microplastics is considered, carbon-14 dating is used to trace the duration of microplastic adsorption by each individual bivalve, dynamic accumulation curves for seafloor bivalves are constructed by connecting each bivalve's survival duration, and finally this is scaled up to the entire bivalve bed to derive the microplastic accumulation rate and historical accumulation of the entire extreme ecosystem.

The invention provides a tailing particle classification method based on multi-dimensional morphological feature compression and clustering, and belongs to the technical field of geotechnical engineering, mineral processing and particle classification, and the method comprises the following steps: S1-S6, obtaining an STL file database of tailing particles; s7, calculating and extracting morphological parameters to form an original parameter data set; s8-S10: obtaining a three-dimensional morphological parameter set which retains the original morphological feature information; s11-S12, determining an optimal clustering number by adopting an elbow rule, a contour coefficient and Gap statistics, and performing particle morphology clustering grouping on the three-dimensional morphology parameters by adopting a K-means algorithm; and S13, carrying out stacking test simulation on the clustered particle categories by using a discrete element method, analyzing the stacking density and the average coordination number of different categories of particles, and verifying the physical significance of a classification result. The tailing particle classification method can systematically and efficiently integrate particle multi-dimensional form information, can directly associate the information with macroscopic physical and mechanical properties, and is data-driven, clear in mechanism and explainable in result.

An automated method for assessing the ZP binding capacity of sperm from morphological characteristics of sperm in assisted reproduction using deep learning is disclosed. The invention also provides a method for predicting fertilization success based on ZP binding capacity of sperm in clinical assisted reproduction.

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 relates to the technical field of biological information processing and automatic sorting, and discloses a chick gender automatic identification method and system based on multi-modal fusion, and the method comprises the steps: collecting original three-dimensional point cloud data through a three-dimensional form sensing module, and calculating the key region coordinates of chicks; based on the coordinates, controlling the addressable micro sampling array to execute active sampling adaptation, and obtaining measurement response time sequence data of the target volatile matter; acquiring background response time sequence data by utilizing a background channel sensor array; and performing differential processing on the two to generate a differential biochemical response time sequence vector. And then, respectively extracting morphological feature vectors and biochemical fingerprint feature vectors, inputting the morphological feature vectors and the biochemical fingerprint feature vectors into respective independent classifiers to obtain prediction categories and confidence coefficients, finally, judging a bimodal prediction result through preset cross validation gating fusion logic, outputting a final decision, and controlling a sorting execution module to complete physical sorting. And the accuracy of biochemical sampling is improved through active sampling adaptation.

This invention provides a tailings particle classification method based on multi-dimensional morphological feature compression and clustering, belonging to the fields of geotechnical engineering, mineral processing, and particle classification technology. The method includes: S1-S6: acquiring an STL file database of tailings particles; S7: calculating and extracting morphological parameters to form an original parameter dataset; S8-S10: obtaining a three-dimensional morphological parameter set retaining the original morphological feature information; S11-S12: determining the optimal number of clusters using the elbow rule, silhouette coefficient, and Gap statistic, and using the K-means algorithm to cluster the three-dimensional morphological parameters into particle morphology groups; S13: using the discrete element method to conduct a stacking test simulation on the clustered particle categories, analyzing the stacking density and average coordination number of different particle categories, and verifying the physical meaning of the classification results. This tailings particle classification method can systematically and efficiently integrate multi-dimensional particle morphological information and directly correlate this information with macroscopic physical and mechanical properties. It is data-driven, has a clear mechanism, and the results are interpretable.

This invention belongs to the field of biological monitoring, specifically a method for detecting the viability of Poria cocos strains, comprising the following steps: Step S1: acquiring images of Poria cocos strains; Step S2: preprocessing the images acquired in Step S1; Step S3: extracting features from the preprocessed images, extracting key growth characteristics of the hyphae, including growth rate, biomass, and morphological characteristics; Step S4: quantitatively evaluating the viability of Poria cocos strains by combining the extracted growth rate, biomass, and morphological characteristics. In this application, the growth rate, biomass change rate, and morphological characteristic values ​​predicted by the ARIMA model can generate viability curves for future time periods. By performing time-series analysis on these predicted viability values, the viability trend of the strains over a future period can be determined, enabling early detection of the risk of decreased viability and timely adjustment of environmental conditions to maintain optimal growth. This also helps in selecting suitable temperature environments to maintain the optimal viability of Poria cocos strains.

The invention discloses a regenerated rice yield prediction method, device and equipment and a medium. The method comprises the following steps: acquiring a current first time period hyperspectral image and a current first time period color image of regenerated rice in a target area; according to the hyperspectral image in the current first time period, calculating the surface reflectance of the hyperspectral image in the current first time period to obtain a vegetation parameter corresponding to the hyperspectral image in the current first time period; determining morphological characteristics of the regenerated rice according to the color image in the current first time period; according to the morphological characteristics and a morphological characteristic library, comparing rice varieties in the morphological characteristics and the morphological characteristic library, and determining the variety type of the regenerated rice; and inputting the variety type and the vegetation parameters into a pre-trained regenerated rice yield prediction model to obtain the predicted yield of the regenerated rice in the current second time period. According to the embodiment of the invention, the efficiency and accuracy of regenerated rice yield prediction can be improved.

The invention relates to the technical field of edible mushroom cultivation, in particular to an indoor cultivation method and system for edible mushrooms and a storage medium. The method comprises the steps that differential monitoring frequencies are set according to different growth and development stages in the fruiting stage, and pileus diameter, pileus height and pileus expansion degree morphological characteristic data of sporocarps at the current monitoring time point are collected; the growth rate is calculated by comparing the morphological characteristic data of the adjacent time points; preliminarily judging whether the growth is stagnated according to the growth rate, if so, further judging whether the growth is mature according to a maturity standard, and triggering a harvesting prompt; if not, predicting the harvesting time length; and if the growth is stagnated but is immature, continuously monitoring and identifying growth abnormality through a secondary verification mechanism. Through dynamic monitoring and intelligent analysis of multi-dimensional morphological characteristics, accurate judgment of harvesting opportunity and early warning of abnormal growth are realized, the management difficulty of household users is effectively reduced, and the yield and quality of mushrooms are improved.

This invention discloses a bird-inspired wing-shaped deformable aircraft that can move across media. Using birds such as pelicans, which possess transmedia capabilities, as biomimetic prototypes, and based on research into their water-entry and exiting behaviors and morphological characteristics, an innovative structural design scheme for a biomimetic deformable wing aircraft is proposed. Based on the biomechanical mechanisms of multi-joint linkage in bird skeletons and the hydrophobic properties of feathers, the aircraft employs a deformable wing and tail design; combined with lightweight materials and a multi-degree-of-freedom actuation strategy, it achieves efficient coordination between dynamic morphological switching and transmedia motion.

The invention relates to a grazing rate prediction data processing method based on plant morphological characteristics, and belongs to the technical field of electric digital data processing and machine learning application. The method solves the problems that the grazing response signal is weak due to the dependence on the overall characteristics of the vegetation community, and the reproducibility is poor due to the lack of a standardized process. The method comprises the following steps: in a model training stage, receiving a plant individual form measurement value containing a plant height and a crown diameter and a grazing rate reference value, calculating an individual form index based on the plant height and the crown diameter, dividing a space unit and calculating spatial statistical characteristics of the form index in the unit, and training to obtain a grazing rate estimation model; in the model application stage, the same index calculation and space statistics steps are executed on the new plant morphology measurement value, the result is input into the trained model, and the estimated grazing rate is output. According to the method, through building individual index calculation, space unit statistics, model training and application, effective prediction of the grazing rate based on plant morphological characteristics is realized.

The present invention relates to a method of identifying and / or verifying a security article, the method comprising probing and / or analyzing (120) in at least a portion of the security article one or more markers representative of the presence, content and / or morphological characteristics of a calcium carbonate filler of bio-based origin, and determining the identity and / or authenticity of the security article based at least on a comparison of the one or more markers with one or more reference information (130).

The invention relates to the technical field of species identification, in particular to a cactus plant germplasm identification method and system based on morphological characters and a medium. The method comprises the following steps: constructing a morphological character data matrix of different cactus plants by adopting a standardized measurement rule, and screening to obtain key morphological characters by adopting an improved principal component analysis method based on the morphological character data matrix; carrying out standardization processing on each key morphological character; constructing an identification reference database containing standard species vectors based on the standardized key form vectors; key morphological character measurement data of a to-be-measured plant sample is collected and standardized, and a to-be-measured morphological vector is obtained; and calculating the spatial similarity between the to-be-detected morphological vector and each standard species vector in the identification reference database, and judging the cactus plant germplasm corresponding to the to-be-detected plant sample based on the calculation result of the spatial similarity. Therefore, a morphological identification technology system is optimized, and a scientific basis is provided for rapid, accurate and standardized identification of cactus plants.

The application discloses a molecular marker and specific primers and an identification method for identifying Takifugu obscurus, Takifugu poecilonotus and hybrid Arlea japonica. The application can realize multiple test results through one-time PCR, and simultaneously complete the rapid identification of the three species of Takifugu obscurus, Takifugu poecilonotus and hybrid Arlea japonica. The application provides a fast and accurate identification method for Arlea japonica variety identification, solves the problems of difficult naked-eye differentiation and low operation accuracy of morphological feature differentiation, and realizes the characteristics of easy result judgment, high conservation, high accuracy and the like. The application provides technical support for variety identification and parent selection of the special economic fish, provides a train of thought for further carrying out genetic scientific research of hybrid Arlea japonica, and is favorable for promoting the vigorous development of the hybrid Arlea japonica breeding industry.

This invention belongs to the field of forest resource monitoring and growth prediction technology, specifically involving a tree species strategy grouping and forest growth prediction method based on morphological traits. The method includes the following steps: acquiring individual-level monitoring data from sample plots; preprocessing the data to construct growth response variables, and transforming and standardizing the diameter at breast height (DBH) and competition index terms; constructing a species-level morphological trait table, standardizing the morphological traits, and generating morphological representations; clustering tree species based on morphological representations to form tree species strategy groups, and backfilling these strategy groups into individual-level samples to construct a training dataset; establishing a hierarchical growth prediction model based on the training dataset, and setting tree species random effects; when the target tree species is not present in the training dataset, determining the strategy group label based on its morphological representation, and outputting the growth prediction result solely based on the model's fixed effects. This invention is applicable to the monitoring and prediction analysis of multi-species natural forest growth and has good market application prospects.

The application belongs to the field of molecular biology, and particularly relates to a KASP marker related to a wheat kernel morphology trait and application thereof. The specific technical scheme is as follows: application of a QTL site in wheat breeding, the QTL site is located in the 12.5-23.5 cM interval of the 4B chromosome of wheat. The developed KASP marker is located at 12610251 bp in the interval, and the 12610251th base is A or G. The application locates a main stable QTL site capable of simultaneously controlling the grain roundness and grain size ratio traits on the short arm of the 4B chromosome of wheat, and provides a target site for genetic improvement of the wheat kernel morphology. The application also develops a closely linked KASP marker based on the QTL site, which is convenient to detect, and provides a marker for molecular marker assisted selection in the process of genetic improvement and breeding of the wheat kernel morphology, so that the detection rate can be effectively improved, and the breeding process is shortened.

The invention relates to a plant rapid breeding system and method based on optical detection. The system comprises an optical monitoring unit and a processing unit, and the optical monitoring unit is at least provided with a first monitoring part used for acquiring optical information of a first morphological characteristic of a plant and a second monitoring part used for acquiring optical information of a second morphological characteristic of the plant. The first monitoring part is started at least in response to a processing result obtained by the processing unit based on the monitoring data of the second monitoring part, and the processing result at least represents that the growth node of any cultivated plant enters a harvesting period. The method at least comprises the following steps: carrying out optical information acquisition on part of morphological characteristics of a plant by utilizing one or one group of monitoring parts; optical information collection is carried out on the other part of morphological characteristics of the plant through the other monitoring part or the other group of monitoring parts, and the other part of morphological characteristics at least comprise the harvesting tissue of the plant, so that the harvesting time and the harvesting scheme of the harvesting tissue of the plant are estimated and determined.

The invention discloses a gastroesophageal reflux AI auxiliary typing method based on esophageal function and morphological characteristics, and belongs to the technical field of gastroesophageal reflux, a first index parameter is acquired, esophageal functional characteristics are evaluated by using a pre-training model according to the first index parameter, and a functional score is calculated; wherein the first index parameter comprises a high-resolution esophageal power index parameter and an esophageal pH-impedance combined index parameter; obtaining esophageal endoscopic image index parameters, and according to the esophageal endoscopic image index parameters, evaluating the severity of the foraminal hernia defect and cardia relaxation by using the deep learning convolutional neural network prediction model, and calculating a morphological score; comprehensively evaluating the severity of the gastroesophageal reflux according to the functional score and the morphological score, and completing the precise typing of the gastroesophageal reflux. The severity of esophageal dysfunction and morphological defect is effectively evaluated based on a comprehensive index evaluation system, and precise typing of gastroesophageal reflux disease is completed.

The invention discloses a fluidity grading method and system based on morphological characteristics of milk powder. The method comprises the following steps: obtaining fluidity indexes of training samples, constructing a PCA model, and forming grading labels in a principal component score space; the method comprises the following steps: acquiring a microscopic image of to-be-detected milk powder, preprocessing, extracting nine types of two-dimensional shape factors, and summarizing sample layers by using a median; respectively training a random forest and XGBoost to carry out regression prediction on the mobility index; modeling is carried out on the PCA hierarchical labels, and synthetic minority oversampling is introduced to improve coverage and robustness; in the inference stage, good / medium / difference grading is completed only based on images, compared with a process depending on a single instrument and subjective interpretation, the method integrates multi-index information, is objective and reproducible, has the advantages of being non-destructive, rapid, available for small samples and the like, can be suitable for online quality control and inter-batch consistency evaluation of large-scale production, and improves the quality control efficiency and stability.

Methods and systems for antibacterial susceptibility testing of a bacterium are provided. The method includes exposing a bacterium to an antimicrobial agent. A series of images of the bacterium is captured over time after exposure The series of images are captured during an imaging period. For each image of the series of images, the method includes extracting a value of each feature in a set of morphological features of the bacterium. The set of morphological features includes one or more of area, aspect ratio, length, circularity, perimeter, angularity, curvature, ferret, pole, roundness, sinuosity, width, trajectory, morphology, orientation, solidity, and z-score. A rate of change is calculated for each feature of the set of morphological features during the imaging period. An inhibition status of the bacterium is determined using a machine-learning classifier applied to input data.

This disclosure relates generally to estimating age of a leaf using morphological features extracted from segmented leaves. Traditionally, leaf age estimation requires a single leaf to be plucked from the plant and its image to be captured in a controlled environment. The method and system of the present disclosure obviates these needs and enables obtaining one or more full leaves from images captured in an uncontrolled environment. The method comprises segmenting the image to identify veins of the leaves that further enable obtaining the full leaves. The obtained leaves further enable identifying an associated plant species. The method also discloses some morphological features which are fed to a pre-trained multivariable linear regression model to estimate age of every leaf. The estimated leaf age finds application in estimation of multiple plant characteristics like photosynthetic rate, transpiration, nitrogen content and health of the plants.

The invention discloses a multi-source fusion-based pig important growth trait prediction method, and belongs to the technical field of animal growth trait prediction. The objective of the invention is to solve the problems of long time consumption and poor measurement result accuracy of the existing method for obtaining important growth traits such as backfat thickness and eye muscle area. According to the method, body size parameters and morphological characteristics of pigs are obtained based on point cloud data corresponding to the pigs, then body size parameter characteristics are adopted to enhance the morphological characteristics to obtain enhanced morphological characteristics, the enhanced morphological characteristics are sent to a Flaten layer to be flattened, and a second characteristic vector is obtained; obtaining a first feature vector based on the pig breed type feature vector, the growth stage feature vector, the slaughter day age and the exercise amount corresponding to the live pig; and the first feature vector and the second feature vector are spliced and then are sent to a neural network model to obtain a predicted output important growth trait predicted value.

The invention discloses a method and system for predicting infant brain function connection based on morphological characteristics and a diffusion model, a storage medium and electronic equipment. The method in the formula comprises the following steps: extracting cortex morphological characteristics in an infant structure magnetic resonance image (sMRI), constructing a morphological similarity network (MSN), and realizing infant brain function full-connection layer prediction by utilizing a diffusion model in combination with classifier irrelevant guidance and a cross-modal attention mechanism. According to the method, individual development features are stably captured by using a longitudinal information extraction module, accurate mapping from morphological features to function connection is realized in combination with a classifier irrelevant guidance diffusion model and a morphological guidance attention mechanism, the prediction precision and stability are remarkably improved, and the prediction efficiency is improved. The problem that infant functional magnetic resonance imaging (fMRI) data is scarce or low in quality can be effectively solved, functional networks in different development stages are accurately reconstructed, and the accuracy of infant early brain development monitoring is improved.

The invention discloses a protaetia brevitarsis sex identification method and system based on computer vision, and relates to the field of insect sex identification, and the method comprises the steps: S1, obtaining a high-definition digital image of a protaetia brevitarsis adult at least comprising a back, an abdomen and a forefoot shin segment area; s2, processing the acquired digital image; s3, segmenting a forefoot shin segment region and a web region from the preprocessed image; s4, extracting morphological features from the segmented regions, wherein the morphological features at least comprise glossiness features, forefoot tibia tooth profile features and web morphological features; s5, inputting the extracted morphological features into a pre-trained classification model; according to the method, by comprehensively utilizing the multi-dimensional external morphological characteristics such as glossiness, front foot shin section tooth shape, web shape and stripes stably existing on the body surface of the adult protaetia brevitarsis, non-destructive living body identification of the protaetia brevitarsis is achieved, and the follow-up utilization value of the protaetia brevitarsis in breeding and scientific research is guaranteed.

This invention discloses SSR marker primers specific to the filamentous petals of gerberas and their applications. This invention utilizes SSR molecular markers to effectively identify different filamentous petal traits in gerberas, resulting in an SSR marker primer that can rapidly and accurately identify the petal traits of gerbera plants with different filamentous petals and the F1 generation of interspecific hybrids. The SSR marker primers provided by this invention can effectively distinguish between filamentous and non-filamentous petals in gerberas and exhibit significant polymorphism, with an accuracy greater than 65%. Furthermore, compared with traditional morphological trait observation methods, the identification method provided by this invention has better repeatability, saves time, and has high specificity. It can replace traditional morphological identification methods, effectively improving the molecular-level breeding process of gerberas and reducing the identification cycle of traditional morphological trait observation. It also allows for early screening to obtain gerbera offspring with superior filamentous varieties, which is of great significance for gerbera breeding.

The invention provides a data-driven product family design method and system based on artificial intelligence. The method comprises the following steps: respectively extracting appearance dominant DNA features and recessive DNA features of a to-be-designed product; product family appearance design is carried out in combination with dominant DNA features and recessive DNA features, pictures are preliminarily generated through text cues, and the cues are iterated until pictures meeting design requirements are generated; screening the pictures meeting the design requirements to obtain a plurality of pictures with highest scores, determining the appearance design of the to-be-designed product in combination with design elements in the dominant DNA feature pictures, and migrating all dominant feature heritable design elements in the appearance design of the to-be-designed product to the to-be-designed product to form a product family appearance design; according to the method, design objectivity and data driving are achieved, recessive DNA keywords are obtained by combining semi-structured interviews and perceptual intention investigation, fusion of emotional appeals and morphological characteristics is achieved, and a generated design scheme is made to conform to brand tonality and have innovativeness.

The invention relates to the technical field of fungus identification, in particular to a mushroom macro fungus identification method and system and a storage medium. The method comprises the steps that morphological characteristic data and original habitat environment data of target fungi are collected, the morphological characteristic data comprise image data of pilei, gels and stipes, the original habitat environment data comprise temperature, humidity and soil pH value, and the morphological characteristic data and the original habitat environment data are used for obtaining morphological information and growth environment information of the target fungi. And semantic segmentation processing is performed on the morphological feature data, key morphological features are extracted, and normalization processing is performed on the original habitat environment data, so that an effective region is separated from the morphological feature data and an environment data format is unified. According to the method, the morphological characteristic data of the target fungi, including the image data of pilei, gels and stipes, and the original habitat environment data, including temperature, humidity and soil pH value, are collected, so that the morphological dimension and environment dimension of fungus identification are covered, and a comprehensive information basis is provided for accurate identification.