175 results about "Phenotypic analysis" patented technology

The invention relates to a water and fertilizer management control method based on image data processing, and the method comprises the steps: collecting a visible light image, a multispectral image and a thermal infrared image of a crop, carrying out the feature extraction, calculating the plant morphological parameters, vegetation indexes and vegetation canopy temperature distribution of the crop, creating a three-dimensional physiological feature matrix, creating an image segmentation network model, and carrying out the feature extraction. Inputting the visible light image, the multispectral image, the thermal infrared image and the three-dimensional physiological feature matrix into an image segmentation network model to segment plant organs, quantifying organ-level phenotypic parameters, and establishing a water and fertilizer decision model based on reinforcement learning; and taking real-time data of the three-dimensional physiological feature matrix, the organ-level phenotypic parameters and the soil specified parameters as state input, taking a water and fertilizer proportioning scheme as an action space, and implementing a water and fertilizer management scheme through a dynamic optimization control strategy. The technical problems that data integration of a single sensor is insufficient, phenotype analysis precision is limited, and adaptability of a decision model is poor in a traditional water and fertilizer regulation scheme are solved.

The invention is suitable for the technical field of cell analysis, and particularly relates to a cell mechanical phenotype analysis method and device based on deep learning, and the method comprises the following steps: obtaining a time sequence image, mechanical response data and culture environment parameters of a target cell; based on the mechanical response data and the culture environment parameters, performing region segmentation on the time sequence image, determining a mechanical sensitive region on the surface of the target cell and local deformation features of a subcellular structure, and realizing refined and targeted feature recognition; generating a mechanical phenotype descriptor of the target cell according to the mechanical sensitive area, the local deformation characteristics and the culture environment parameters to realize multi-modal data fusion; a mechanical phenotype analysis model obtained by training a sample mechanical phenotype descriptor of a sample cell in advance is used for processing the mechanical phenotype descriptor to obtain a heterogeneity prediction result of a target cell, the heterogeneity of the mechanical characteristics of the cell in a complex culture environment is automatically identified, and the dynamic nature and generalization ability of heterogeneity classification are remarkable.

The invention discloses a cotton GhSNL1 protein as well as a coding gene and application thereof. The cotton GhSNL1 protein coding gene (GhA01G1757; according to the present invention, the CRISPR-Cas9 gene editing material of the GhSNL1 gene is subjected to phenotypic analysis to discover that the GhSNL1 gene knockout material has the phenotype of fiber start slowing, fiber density shortening and mature plant boll regeneration, such that the GhSNL1 gene participates in the cotton fiber development process, and the cotton fiber development efficiency is significantly improved; the method provides a theoretical basis for cotton variety improvement, and can be used for cultivating new cotton varieties with excellent fiber quality and high yield.

The invention discloses a plant three-dimensional Gaussian phenotype extraction method based on an improved parameter adaptive fast pose optimization algorithm, and the method comprises the steps: obtaining a multi-view image sequence of a plant through a camera system, estimating the pose of a camera through a global adaptive fast pose optimization algorithm, and generating a sparse three-dimensional point cloud of a scene, pose estimation errors in an agricultural scene can be effectively eliminated, the problem of error accumulation in a traditional method is avoided, and high-precision camera pose estimation is guaranteed; secondly, taking the camera pose and the sparse three-dimensional point cloud as input, converting an object in a scene into a plurality of three-dimensional Gaussian points, performing three-dimensional reconstruction based on a Gaussian representation method, and generating a dense three-dimensional point cloud, thereby solving common shielding and dynamic change problems in an agricultural scene, and improving the quality and efficiency of point cloud reconstruction; and finally, based on the obtained dense three-dimensional point cloud, phenotypic characteristics of the plants are extracted, and more accurate data support is provided for crop phenotypic analysis.

The invention discloses an image restoration method for soybean leaf phenotype analysis. The method comprises the following steps: firstly, complementing missing edge information of soybean leaves through an edge generation network; then, extracting a coarse-to-fine layered feature map from the complemented edge information by using a structural auto-encoder, and inputting the layered feature map as a guide feature into an image restoration network to assist a restoration process; the soybean leaf structure information is accurately repaired based on the guiding characteristics; and finally, according to the completely repaired leaf image, obtaining phenotypic parameters such as a leaf area index, a structure texture and a color of the soybean plant. By means of the mode, gradient structured edge information and hierarchical feature guidance are introduced, the repairing effect of the soybean leaf picture can be improved, and therefore support is better provided for accurate and complete analysis of soybean leaf phenotype data.

Disclosed in the present invention are a cumulative pressure stress effect evaluation system and method. The system comprises: an animal modeling module, configured to construct various pressure stress level models by means of applying different durations of pressure to a target model animal; a spontaneous behavior capture module, configured to capture spontaneous behaviors of the target model animal corresponding to different pressure stress levels to obtain corresponding action recognition data, and construct a first data set according to a corresponding relationship between the action recognition data and the pressure stress levels; a behavior phenotype analysis module, configured to use linear discriminant analysis to perform dimension reduction on the first data set, so as to obtain a second data set in a discrimination space; and a pressure degree evaluation module, configured to use the second data set to train a machine learning model, so as to obtain an optimized cumulative pressure stress effect evaluation model. The present invention can be used for cumulative pressure stress effect evaluation, is suitable for precise medicine, and can achieve behavior phenotype difference recognition induced by the same pressure stress source and different pressure degrees.

The invention provides a single ear grain extraction method and device based on three-dimensional CT data. According to the method, the stem area in the plant three-dimensional CT data can be removed to obtain the stem-removed three-dimensional data, interference of the stem area on ear grain extraction is avoided, the area with the gray value larger than or equal to the gray threshold value in the stem-removed three-dimensional data is reserved, the remaining area is removed, and therefore the accurate ear grain three-dimensional data is obtained, and the data comprises a plurality of ear grains. Each communication area determined through communication analysis corresponds to one ear grain area, the communication areas are matched with corresponding labels respectively, and then different ear grain areas are distinguished through different labels; according to the method, a single ear grain is extracted according to a label to obtain a single ear grain mask, and an accurate single ear grain three-dimensional image is extracted from plant three-dimensional CT data by using the single ear grain mask, so that the accuracy and the stability of extraction of the single ear grain three-dimensional image are remarkably improved, and phenotype analysis and quality detection according to the single ear grain three-dimensional image are facilitated.

The invention belongs to the field of molecular biology, and particularly relates to application of OsDOF3 protein or a coding gene thereof to improvement of rice broad-spectrum disease resistance and regulation of agronomic traits. According to the application, an OsDOF3 knockout mutant and an overexpression transgenic line are constructed under the background of susceptible rice, resistance phenotype analysis is performed by inoculating various pathogenic bacteria (including pyricularia oryzae, rhizoctonia solani and bacterial blight), and it is proved that OsDOF3 participates in regulation and control of an immunoreaction pathway of rice to fungal diseases (rice blast and sheath blight) and bacterial diseases (bacterial blight). Meanwhile, the application also finds that after a strong promoter ubi is adopted for driving, an overexpression material of the OsDOF3 is shorter than that of a wild plant, and the OsDOF3 influences normal growth and development of rice while stimulating plant immune response. The invention provides important gene resources and molecular theoretical basis for developing a new variety of broad-spectrum disease-resistant rice.

The invention relates to the technical field of computer vision and soybean plant phenotype measurement, and discloses a soybean plant phenotype analysis method, system and device based on three-dimensional reconstruction, and the method comprises the following steps: setting a device for carrying out multi-view image collection on a soybean plant to collect multi-view soybean plant images; and an end-to-end soybean plant leaf phenotype measurement model taking Pointnet Transform as a trunk is constructed, and the model can directly predict key phenotype characteristics such as leaf area and leaf perimeter from high-precision leaf point cloud data. Discrete point cloud voxelization processing needs to be carried out before the model is constructed, and conditions are provided for semantic segmentation. According to the method, automatic acquisition of the soybean plant point cloud, automatic segmentation of the soybean plant point cloud and automatic measurement of the soybean plant leaf point cloud phenotype are realized, a data basis is provided for soybean plant phenotype analysis, and high-throughput and intelligent soybean plant phenotype analysis is realized.

The invention belongs to the technical field of ryegrass plant tillering regulation and control methods, and discloses a ryegrass LpZAT12 gene for regulating and controlling plant tillering and application, and the nucleotide sequence of the LpZAT12 gene is as shown in SEQ ID NO: 1; the amino acid sequence is shown as SEQ ID NO: 2; and the nucleotide sequence of the corresponding promoter is as shown in SEQ ID NO: 3. Transgenic ryegrass obtained through a perennial ryegrass genetic transformation system is subjected to phenotypic analysis, and the result shows that the gene inhibits tillering of perennial ryegrass. The discovery of the LpZAT12 gene provides a new gene resource for cultivating a new perennial ryegrass variety with strong tillering ability. The overexpression of the gene can reduce the management cost of the lawn and prolong the management period; knockout of the gene can promote plant tillering, increase plant biomass and improve industrial benefits, and application of the gene has important production practice significance.

The present application provides a plant phenotyping method that can determine the corresponding depth map based on the original image of a potted plant. For potted plants, the background is relatively far from the shooting lens. Based on the depth map, the potted plant can be quickly and accurately separated from the background to obtain corresponding background clusters and plant clusters, and then an accurate potted plant mask image can be obtained based on the plant cluster. Since the color of the flower pot is relatively easy to distinguish, color analysis is performed on the original image to determine the corresponding flower pot area and extract an accurate flower pot mask image. Using an image processing model that can accurately identify soil, the original image is analyzed to identify the soil area and obtain an accurate soil mask image. Finally, the flower pot mask image portion and the soil mask image portion can be removed from the potted plant mask image, and the remaining portion is the accurate plant mask image. Accurate phenotypic analysis can be performed based on the plant mask image to obtain accurate phenotypic parameters of the plant.

The invention discloses a high-precision phenotype analysis system for plant growth stress response, and relates to the field of agriculture. Comprising a central analysis platform, and the central analysis platform comprises a calculation engine, a stress diagnosis model and a visual interface; the edge calculation stage comprises a data preprocessing module, a feature extraction module and a time sequence compression module; and the sensor array comprises a main control unit, a hyperspectral camera, a thermal infrared camera, a 3DDoF camera and an environment sensor group. Based on a space-time registration mechanism of transfer learning, errors are reduced, the problem of stomatal conductance modeling distortion caused by time difference is solved, a dynamic weight fusion model adjusts feature weights in real time through sensor confidence and environment fitness, illumination abrupt change and temperature and humidity fluctuation interference are eliminated, and the accuracy of stomatal conductance modeling is improved. Compared with a traditional fixed weight model, the stress identification precision is improved.

This invention relates to methods and systems for isolation of species in semi-permeable capsules and processing of encapsulated species through series of steps and / or reactions. To produce capsules, first aqueous two-phase system (ATPS) droplets are generated using microfluidics system. Then the hydrogel shell layer is hardened by inducing polymerization. As exemplified in this invention to achieve concentric ATPS droplet formation density-matched PEGDA and Dextran polymer solutions can be used. Once a capsule is formed, its composition can be changed by adding new reagents or replacing out old ones (e.g. by resuspending capsules in desired aqueous solution). The hydrogel shell of semi-permeable capsules can be dissolved at selected step during multi-step procedures to release the encapsulated species. This invention exemplifies isolation of individual cells within capsules and using the encapsulated cells for genotypic and phenotypic analysis. This invention also exemplifies use of capsules in multi-step procedures to perform complex biological reactions.

The invention relates to a corn ZmmtRF2a gene mutation marker, an identification primer and application of the corn ZmmtRF2a gene mutation marker in regulation and control of grain development. A natural grain mutant crk5 found in a corn field breeding line selection process is utilized, a target gene ZmmtRF2a causing crk5 grain mutation is obtained through phenotypic analysis, linkage analysis and map-based cloning technologies, the gene encodes a peptide chain release factor 2a for mitochondrial localization, the peptide chain release factor 2a affects the mitochondrial function, and the gene can be used for inducing the mutation of the crk5 grain. The development of corn embryos and endosperm and the accumulation of grain storage substances are regulated and controlled. In crk5, ZmmtRF2a lacks 5th to 7th exons, the encoded truncated protein does not have GGQ and SPF motifs, the motifs are crucial to release activity of RF, mitochondrial binding and termination codon recognition, technical support is provided for utilization of the gene, and a new way is opened up.

The invention discloses a sugarcane live-action three-dimensional phenotype extraction method based on a 3D Gaussian splashing technology. The method comprises the following steps of data acquisition, wherein a 360-degree omnibearing shooting mode is executed around a single sugarcane plant for shooting or video recording to obtain complete image or video data of the sugarcane plant; preprocessing the data; 3D model construction comprises the following sub-steps: SfM sparse reconstruction: instance segmentation; the method comprises the following steps: carrying out 3D Gaussian Splicing guided by a mask; point cloud data processing includes the following sub-steps: point cloud preprocessing including voxel downsampling, statistical filtering and radius filtering; segmenting and identifying stalks / leaves; the segmentation comprises RANSAC plane segmentation, DBSCAN clustering and region growth; the phenotype analysis comprises grid reconstruction and area / volume / perimeter / plant height calculation. According to the invention, the problems of difficult data acquisition, large workload and incomplete information acquisition in the prior art are solved.

This invention discloses a rice starch synthesis-related gene, OsFLO17, its encoded protein, and its applications. Through phenotypic analysis of the rice endosperm mealy mutant flo17 and preliminary localization of the target gene, the starch synthesis-related protein OsFLO17 was ultimately cloned. This protein consists of the amino acid sequence shown in SEQ ID NO.3. The starch granule development-related protein of this invention affects the rice endosperm starch synthesis process. Introducing the encoding gene of this protein into plants with abnormal starch granules can yield transgenic plants with normal starch filling. Therefore, the protein and its encoded gene of this invention can be applied to plant genetic improvement.

The invention relates to the technical field of soybean seed testing, in particular to a soybean stalk seed testing device and portable seed testing equipment, which comprises a scanning bin, a chuck, a Y-axis movement unit, a 3D scanning unit, an electric turntable, an electric control unit and a display, the Y-axis movement unit can move in the height direction; the 3D scanning unit performs imaging scanning on the stalks; the electric rotary table drives the stalks to rotate by 360 degrees; the electric control unit controls scanning; according to the device, the electric control unit controls the electric rotary table and the Y-axis movement unit, the 3D scanning unit is matched, all-directional imaging scanning of soybean stalks is achieved, appearance and internal feature information is accurately obtained, and phenotype analysis accuracy is improved; according to the automatic seed testing process, manual intervention is reduced, seed testing time is shortened, efficiency is improved, and the problems that a traditional seed testing method is low in efficiency and prone to being interfered by subjective factors, and the requirements of precision agriculture for high throughput and multi-dimensional data cannot be met are solved.

The invention relates to the technical field of peanut phenotype intelligent analysis scheme design based on an unmanned aerial vehicle, in particular to a peanut phenotype intelligent analysis method and system based on the unmanned aerial vehicle. The method comprises the following steps: acquiring a multi-modal image through a visible light, multispectral and thermal infrared sensor carried by an unmanned aerial vehicle, separating a plant area through illumination normalization and soil mask treatment, aligning data in different growth stages by using a time sequence registration algorithm, and extracting morphological, spectral and thermal characteristics to construct a fusion matrix; inputting the matrix into a dynamic growth model fusing LSTM and a crop physiological model, and outputting the number of pods, biomass and disease resistance parameters; and generating fertilization and irrigation decision suggestions in combination with a preset threshold. The system comprises a multi-modal data acquisition module, an image processing module, a feature fusion module, a model calculation module and a decision generation module. According to the method, multi-source data collaborative analysis, time sequence growth precise modeling and field management dynamic optimization are realized, and the peanut phenotype analysis efficiency and decision scientificity are remarkably improved.

The invention discloses research of Pdh1 allelic variation in soybean breeding material distribution and pod cracking resistance, and pod cracking severely affects improvement of soybean productivity and is one of important indexes of soybean breeding selection. The invention discloses a high-quality anti-pod-cracking soybean breeding identification marker Pdh1-2 gene, the Pdh1-2 gene is a variation type of a Pdh1 gene, the base sequence of the Pdh1 gene is as shown in SEQ ID NO.2, and the base sequence of the Pdh1-2 gene is as shown in SEQ ID NO.3. The invention also discloses a preparation method of the high-quality anti-pod-cracking soybean breeding identification marker Pdh1-2 gene. The practicability of the Pdh1-2 variation type is further explored. 1118 breeding strains are randomly selected, the proportions of split pods and non-split pods of variation types are respectively counted, and the result shows that 975 parts of materials are Pdh1-1 and Pdh1-3 variation types, and 143 parts of materials are Pdh1-2 variation types. The combined phenotypic analysis shows that the proportion of the Pdh1-2 split pod material is the maximum and is 84.6%, the proportion of the Pdh1-1 is 81.0%, and the proportion of the Pdh1-3 is the minimum and is only 41.1%. The research provides a theoretical basis for breeding of southern anti-pod-cracking soybeans assisted by the molecular marker.

The invention relates to the field of biology, and provides a phenotype analysis method, electronic equipment and a storage medium, and the method comprises the steps: obtaining first information and a plurality of pieces of second information; obtaining a first vector of the first information according to a preset knowledge graph; obtaining a second vector of multiple pieces of second information according to a preset knowledge graph; based on the trained graph neural network, obtaining a first feature vector of the first information and second feature vectors of the multiple pieces of second information according to the first vector of the first information and the second vectors of the multiple pieces of second information; calculating the similarity between the plurality of pieces of second information and the first information according to the first feature vector of the first information and the second feature vectors of the plurality of pieces of second information; and determining target second information from the multiple pieces of second information according to the similarity between the multiple pieces of second information and the first information. According to the method, the searching efficiency among the symptom phenotype description information, the gene information and the disease information is improved.

The invention relates to the technical field of rice phenotype extraction, and discloses a rice fine phenotype extraction method based on three-dimensional vision, a terminal and a storage medium. The method comprises the following steps: acquiring a multi-view image of a rice plant by using a depth camera, and performing camera attitude estimation on the multi-view enhanced image to obtain camera internal and external parameters corresponding to each view; constructing a neural radiation field network, training the neural radiation field network by taking the enhanced image, the contour mask and internal and external parameters of the camera as input, and learning color and density distribution of a scene by utilizing a volume rendering technology; extracting surface points based on a mixed threshold strategy by using the trained neural radiation field network, and generating a rice three-dimensional point cloud; inputting the rice three-dimensional point cloud into a point cloud instance segmentation network, and outputting an instance mask containing each organ of the rice and a segmentation result of a three-dimensional bounding box; and counting phenotypic parameters of the rice according to a segmentation result. According to the method, the fine instance segmentation of the high-density point cloud is realized while the reconstruction precision is ensured, and the phenotypic analysis of the spike fraction is supported.

The invention discloses a method and a system for assisting in inputting clinical information of rare diseases and analyzing gene mutation based on phenotypes. The method comprises the following steps: firstly, acquiring clinical information in voice, text and image forms of a patient through a multi-source data acquisition module, converting the clinical information into characters, and performing entity recognition and standardization processing to generate structured medical record data; secondly, extracting clinical phenotypes from the structured data; furthermore, a candidate gene list is obtained according to the gene-disease relationship, comprehensive scoring and sorting are carried out, and a concerned gene list is output. According to the method, efficient structured input and standardization of clinical information are realized, the accuracy and automation level of phenotype-gene matching are remarkably improved, the gene variation interpretation period is effectively shortened, and intelligent support is provided for precise diagnosis of genetic diseases.

The invention relates to the technical field of plant genetic engineering, in particular to application of an algae NySAT2 gene in regulation and control of alfalfa crude protein and detergent fiber. According to the invention, a sequence after codon optimization of an NySAT2 gene coding region is obtained by using gene synthesis and codon optimization technologies, an algae NySAT2 gene overexpression vector is constructed, a target gene is introduced into an alfalfa genome by using an agrobacterium rhizogenes mediated genetic transformation method, and a transgenic plant is obtained by using a genetic transformation system. Molecular identification and phenotypic analysis show that on the premise of keeping normal growth vigor (plant height and biomass have no significant difference from those of a wild type), the crude protein content of a medicago sativa plant with the overexpressed NySAT2 gene is remarkably increased compared with that of the wild type medicago sativa plant, and meanwhile, the contents of acid detergent fibers and neutral detergent fibers are remarkably reduced, so that the content of the crude protein in the medicago sativa plant with the overexpressed NySAT2 gene is remarkably increased. The forage quality and the feeding value of the medicago sativa are effectively improved.

The invention discloses a method for assisting hawthorn breeding based on phenotypic omics, and relates to the technical field of phenotypic omics assisted hawthorn breeding. Comprising the following steps: S1, acquiring phenotypic data of normally growing hawthorns by using a high-throughput imaging system and a phenotypic analysis system, and detecting and analyzing various characters of the hawthorns; s2, establishing a hawthorn multi-dimensional phenotypic group database, and obtaining hawthorn breeding key character quantitative indexes; s3, constructing a phenotype-genotype correlation model according to the obtained hawthorn breeding key character quantitative indexes, and mining new functional genes to realize early prediction of breeding; and S4, creating a phenotype exponential weighting algorithm for parent matching. The method for assisting hawthorn breeding based on phenotypic omics is established, the problems of long hawthorn breeding period and high cost are solved, and the hawthorn breeding efficiency is effectively improved.

The present invention discloses a plant gluten sorting-related protein OsGPA15, its encoding gene and applications. Through phenotypic analysis of rice gluten sorting mutants gpa15 and preliminary mapping of target genes, the gluten sorting-related protein OsGPA15 was finally cloned. The related protein is composed of the amino acid sequence shown in SEQ ID NO.1, and the nucleotide sequence of the gene is as shown in SEQ ID NO.2 or SEQ ID NO.3. The gluten sorting-related protein of the present invention affects the sorting process of gluten in rice endosperm. By introducing the encoding gene of the protein into plants with reduced mature gluten content, transgenic plants with normal mature gluten content can be obtained. Therefore, the protein and its encoding gene of the present invention can be applied to plant genetic improvement.

The invention discloses a rice photo-thermo-sensitive male sterility mutant Osgpat6.1, a corresponding gene locus OsGPAT6.1 of the rice photo-thermo-sensitive male sterility mutant Osgpat6.1 and application of a coding protein of the rice photo-thermo-sensitive male sterility mutant The phenotypic gene OsGPAT6.1 for controlling the photo-thermo-sensitive male sterility of the rice is obtained by cloning through phenotypic analysis of a mutant and preliminary positioning of a target gene, an amino acid sequence of the phenotypic gene OsGPAT6.1 is encoded as shown in SEQ ID NO.1 to form a protein, and the gene affects the development process of rice pollen by affecting tapetum degradation. The OsGPAT6.1 gene is subjected to gene editing, or a mutant allele of the OsGPAT6.1 gene is introduced into a conventional rice variety in a hybridization manner, so that a photo-thermo-sensitive male sterile material for a two-line sterile line can be obtained. The gene and the encoding protein thereof can be applied to creation of photo-thermo-sensitive sterile rice and germplasm genetic improvement of the photo-thermo-sensitive sterile rice.

The invention discloses an application of a PadPGR5a gene in fast-growing high-quality high-yield poplar breeding, belongs to the technical field of genetic engineering, and particularly relates to the application of the PadPGR5a gene in fast-growing high-quality high-yield poplar breeding, and the nucleotide sequence of the PadPGR5a gene is shown as SEQ ID NO.1. The invention further discloses an application of the PadPGR5a gene in fast-growing high-quality high-yield poplar breeding. Through phenotypic analysis, the plant height of the PadPGR5a gene overexpressed transgenic poplar is obviously increased, the net photosynthetic rate is obviously improved, and the oxidative stress resistance is obviously enhanced. It is shown that the PadPGR5a gene is an important forest fast-growing improved gene resource, and a new excellent transgenic variety can be provided for construction of a fast-growing high-quality high-yield poplar man-made forest by means of a genetic engineering technology.

The invention discloses application of a TaPP2CL-A gene in regulation and control of wheat tiller number, and belongs to the technical field of plant antiviral gene engineering. The invention discloses a novel gene TaPP2CL-A for regulating and controlling the tiller number of wheat. Wheat genetic transformation experiments and further phenotypic analysis find that compared with wild Fielder, the tiller number of a TaPP2CL-A overexpressed plant is reduced, and finally the yield of a single plant is reduced; the tiller number of a TaPP2CL-A silent plant is increased, so that the yield of a single plant is increased. The TaPP2CL-A gene provided by the invention can be used in the field of transgenic breeding, and can lay a theoretical foundation for wheat yield increase and character improvement.

This application is applicable to the field of cell analysis technology, and particularly relates to a method and device for cell mechanical phenotype analysis based on deep learning. The method includes: acquiring temporal images, mechanical response data, and culture environment parameters of target cells; based on the mechanical response data and culture environment parameters, performing regional segmentation on the temporal images to determine the mechanically sensitive regions on the surface of the target cells and the local deformation characteristics of subcellular structures, thereby achieving refined and targeted feature recognition; generating a mechanical phenotype descriptor of the target cells according to the mechanically sensitive regions, local deformation characteristics, and culture environment parameters to realize multi-modal data fusion; using a mechanical phenotype analysis model pre-trained with sample mechanical phenotype descriptors of sample cells to process the mechanical phenotype descriptor, and obtaining a heterogeneity prediction result of the target cells, automatically identifying the heterogeneity of cell mechanical properties under complex culture environments, and significantly improving the dynamics and generalization ability of heterogeneity classification.