27 results about "Phenotypic analysis" patented technology

The application discloses an Arabidopsis thaliana shade-avoidance regulation microprotein gene AtRAD-Like, an encoded protein and application thereof, relates to the technical field of genetic engineering, and the nucleotide sequence of the gene AtRAD-Like is shown in SEQ ID NO.1; and the amino acid sequence of the protein encoded by the gene AtRAD-Like is shown in SEQ ID NO.2. The application is characterized in that the Bar marker gene is used to impart herbicide resistance to a transformation receptor for screening and Western Blot is used to detect the expression of the AtRAD-Like-4MYC fusion protein, so that positive seedlings can be accurately and quickly detected, and phenotype analysis is further conducted on the AtRAD-Like gene overexpression transgenic line, it is found that the AtRAD-Like gene has a negative regulation effect on the hypocotyl length of plants in a shade environment, the finding provides a research basis for developing a new shade-tolerant plant, and has important significance.

This invention discloses a method and system for three-dimensional phenotypic analysis of potatoes based on computer vision. The method includes: first, constructing an acquisition system with multiple high-resolution RGB cameras in a triangular layout and a uniform light source, including a motion turntable; after distortion correction via checkerboard calibration, simultaneously acquiring multi-view images of potatoes; next, generating a dense depth map through sparse point cloud reconstruction combined with an improved multi-view stereo vision reconstruction mechanism including dynamic adaptive aggregation range and curvature-weighted matching cost calculation; registering and fusing to obtain three-dimensional point cloud data containing color and texture; then extracting basic parameters such as length, volume, and RGB features; and using an improved point cloud segmentation model including curvature-sensitive sampling, geometric attention modules, and a hybrid loss function to segment buds and extract parameters such as number and depth. This provides technical support for potato breeding and intelligent sorting.

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.

This application discloses a multi-scale spatiotemporal fusion method and system for phenological identification based on remote sensing images. It utilizes a multi-scale spatiotemporal fusion deep learning framework, constructing continuous time series based on high-frequency acquired baseline resolution images as time anchors. This is then spatially enhanced by combining these with sparse high-resolution images. By introducing a missing-aware gating fusion mechanism, multi-resolution features on unaligned time axes are dynamically fused. The resulting fused feature sequence is then input into a long short-term memory network to achieve phenological stage identification of crop germplasm resources. This application can stably identify the jointing-booting stage, heading-flowering stage, milk stage, waxy ripening stage, and maturity stage under conditions of sparse high-resolution images and asynchronous sampling times, balancing identification accuracy and operational efficiency. It is suitable for large-scale germplasm resource phenotypic monitoring, critical period early warning, and precision breeding applications, providing a flexible, scalable, and cost-effective solution for high-throughput phenotypic analysis.

A phenotyping device, method and system for measuring phenotypic traits of one or more plants in a target canopy, wherein the comprises a canopy spreader for spreading the target canopy, a camera unit, a means for controlling the angle and the distance of the camera unit in relation to the canopy spreader, an electronic control unit for controlling the camera unit. The phenotyping device, method and system enables recording the raw data from the target canopy during a data collection session comprising information of the plants that are not visible to a camera unit without spreading the canopy.

The application belongs to the technical field of corn genetic breeding, and specifically discloses a corn kernel development gene ZmCHR24 and application thereof. ZmCHR24 The nucleotide sequence of the gene is shown as SEQ ID NO. 1. The application obtains a mutant material with loss of gene function through EMS mutagenesis and Mu transposon insertion technology. ZmCHR24 The phenotype analysis result shows that, compared with the wild type, ZmCHR24 the kernel of the mutant with loss of gene function presents severe development defects, and specifically, the kernel is only pericarp with no embryo and endosperm. ZmCHR24 The above result shows that the gene is a key gene for regulating corn kernel development. The application provides an important theoretical basis and gene resource for genetic improvement and molecular design breeding of the yield trait of corn kernels.

The application discloses a detection system and method for CAR-T cell phenotype analysis and function quality, which comprises nucleic acid aptamer, including at least one of CD3 aptamer, CD25 aptamer and CD122 aptamer; nucleic acid nano framework; wherein the nucleic acid aptamer is combined with the nucleic acid nano framework, and can produce a fluorescent signal when the CAR-T cell is activated. The detection system combines nucleic acid aptamers for CD3, CD25 and CD122, and can quickly, without washing, sensitively analyze the phenotype of the CAR-T cell and control the function quality. The detection system utilizes the programmability of the nucleic acid nano structure, realizes real-time monitoring of the activation state and cytotoxicity of the CAR-T cell, has the characteristics of high specificity, wide dynamic range and strong serum stability, can distinguish the resting and activated T cells within 30 minutes, and can capture the phenotype and function difference of the CAR-T cell in a co-culture experiment.

This invention belongs to the field of gene breeding technology, specifically relating to the application of the cascade signal kinase OsMAPKKK5 in improving rice resistance to bacterial blight. This invention provides the protein OsMAPKKK5 or substances regulating the activity and / or content of said protein, and the application of related biological materials. This invention constructs... OsMAPKKK5 Transgenic lines overexpressing the pathogen were inoculated with bacterial blight pathogens in the field for resistance phenotypic analysis, and the results showed that... OsMAPKKK5 Compared to wild-type rice varieties, the overexpressed transgenic lines showed shorter lesions and exhibited a resistance phenotype against bacterial blight, thus demonstrating improved immunity. OsMAPKKK5 The expression level of this gene can promote the resistance to bacterial blight in rice. This invention provides important genetic resources and molecular theoretical basis for the development of new rice varieties resistant to bacterial blight.

The application discloses a plant phenotype-oriented three-dimensional reconstruction method and a collection system. The method first acquires multi-view image data of a plant through a 360-degree ring collection system integrated with an RGB camera and a depth camera; then generates an accurate foreground mask by using a foreground semantic segmentation model; then combines structure from motion (SfM) and depth point cloud, and performs fusion under the guidance of the foreground mask to obtain an initial point cloud; then performs mask-guided 3D Gaussian splashing (3DGS) optimization reconstruction based on the point cloud, and the optimization process improves the identification of small plant organs by using a mask weighted loss function and a semantic-guided density control strategy; and finally derives a color point cloud from the optimized Gaussian model, which can be directly used for extraction of phenotype parameters such as plant height and leaf area. The application solves the problems of large noise and detail loss in the prior art when reconstructing plants in a complex background, and realizes fast three-dimensional reconstruction which can be directly used for phenotype analysis.

This invention discloses a deep learning-based method for generating wide-field crop images, belonging to the field of smart agriculture technology. The method utilizes a mobile platform equipped with multiple high-resolution RGB cameras to simultaneously acquire top-view images of crops after calibration and distortion correction. A dataset is constructed and enhanced based on the acquired images, and a cascaded network of SuperPoint and SuperGlue is built. The former detects key points and generates descriptors, while the latter achieves feature matching through a graph neural network. Intrinsic points are selected using the RANSAC algorithm, the homography matrix is ​​calculated, the images are aligned through perspective transformation, and multi-band fusion is used to eliminate seams, gradually stitching together to generate a wide-field image. This invention solves the problems of limited field of view, severe distortion, and insufficient generalization ability of traditional techniques, providing high-resolution and accurate crop images and reliable data support for crop phenotypic analysis.

This invention discloses a method for domesticating artificial allotetraploid rice AABB based on gene editing, wherein the artificial allotetraploid rice AABB is cultivated rice. O.sativa With spotted wild rice O.punctata This was obtained by hybridization of the parent lines followed by chromosome doubling technology, the method including knocking out or inhibiting chromosomes derived from spotted wild rice O. punctata. SH4 , LABA1 and Bh4 Genes. This invention constructs a dual-target vector that simultaneously targets three genes, and obtains lines with simultaneous mutations in multiple genes through a single genetic transformation. Phenotypic analysis shows that... OsSH4 Knockout reduced the natural grain loss rate from 31% to below 5%; OsLABA1 Knocking out the awn shortened its length from 4.28cm to approximately 1.60cm. OsBh4 Knockout caused the glumes to change from dark brown to yellow. The simultaneous improvement of the above traits significantly enhanced the cultivation level of the artificial allotetraploid AABB while maintaining its original excellent traits such as high biomass and superior grain structure.

This invention relates to the fields of image data processing, plant phenotypic analysis, and agricultural decision support, and discloses a method, system, device, and medium for determining the timing of hydrangea bluening treatment based on image data processing and leaf target recognition. The method includes: acquiring a top-view image of a hydrangea plant and determining the target area for timing determination; preprocessing the top-view image and inputting the preprocessed image into a pre-trained leaf recognition model to obtain leaf detection results; based on the leaf detection results, determining the effective leaf targets within the target area according to the detection box, target position relationship, and corresponding confidence level, and obtaining leaf count results; comparing the leaf count results with a preset growth stage threshold to generate a hydrangea bluening treatment timing determination result; and outputting bluening treatment suggestion information or continued monitoring information based on the determination result. This invention also discloses a system, electronic device, and computer-readable storage medium for implementing the above method. This technical solution can be used to assist in determining the timing of hydrangea bluening treatment.

This invention discloses a high-throughput analysis method and system for seedling phenotypic analysis based on time-series growth images, specifically relating to the field of seedling cultivation and phenotypic analysis technology. It addresses the problems of distortion in growth curves and inaccurate evaluation and screening caused by transient disturbance noise introduced by moving imaging operations in existing high-throughput analysis methods. The method involves acquiring time-series images and extracting phenotypic parameters during the seedling growth cycle, constructing population and individual time series separately, performing time-frequency transformation to separate high-frequency disturbance components and generate energy proportion time series, identifying synchronous peak fluctuations in the population and individual time series to determine the timing of operational disturbances, and interpolating and correcting the contaminated phenotypic parameter values ​​at that moment. This invention effectively identifies and removes operational disturbance noise, improving the quality of seedling phenotypic data and the reliability of subsequent analysis.

This invention discloses a multi-dimensional feature fusion method for enhancing images of leafy vegetables in greenhouses, belonging to the field of image processing. It addresses problems such as uneven exposure, highlight spots, and color distortion in leafy vegetable images caused by changes in light intensity and leaf characteristics under greenhouse conditions, achieving high-quality image enhancement. The method includes image normalization, multi-branch multi-dimensional feature extraction, feature fusion and conditional diffusion network correction, as well as multi-scale decoding and output. By introducing complementary features of various types, such as edge, frequency domain, cross-scale context, and exposure prior, and utilizing a lightweight diffusion backbone network and dynamic weight loss optimization, the clarity and realism of the images are effectively improved. The enhanced leafy vegetable images are suitable for agricultural phenotypic analysis scenarios such as canopy coverage, vegetation index extraction, and yield prediction.

This invention provides a potato tuber formation-related transcription factor. StPDF2 Genes and their applications, through the construction StPDF2 Gene overexpression vectors and transformation of potatoes can significantly improve... StPDF2 Gene expression levels. Phenotypic analysis results indicate overexpression. StPDF2 The gene significantly promotes potato tuber formation, advances tuber setting time, and increases the number of tubers per plant; simultaneously, it has no significant adverse effects on the aboveground vegetative growth of the plant. The overexpressed material shows good application potential in improving tuber formation efficiency, precocity, and related agronomic traits, and can be used for the creation of early-tubering potato materials, screening of high-tuber-setting germplasm, and molecular design breeding. This invention provides a new technical approach and molecular tool for utilizing genes regulating potato tuber formation.

The present application relates to the technical field of computer-aided modeling and intelligent agricultural phenotype analysis, and particularly relates to a rhizomatous plant expansion quantification evaluation method, comprising: obtaining rhizome imaging data and environmental variable data of the same rhizomatous plant to be evaluated at at least two sampling time points; determining a set of expansion front nodes from a rhizome skeleton graph and constructing a rhizome digital twin model; establishing a set of front actions oriented to the set of expansion front nodes; constructing a consistency objective function; determining a confidence index based on the quality of observation data and the stability of registration, and adaptively determining a weight coefficient according to the confidence index; performing iterative calibration on a parameter vector based on the consistency objective function, and performing constraint projection on the updated parameter vector in each iteration to meet a constraint set; calculating an expansion index based on a predicted state trajectory and outputting an evaluation result. The present application realizes stable and quantitative evaluation of rhizome expansion.

This invention discloses a benzyl alcohol synthesis gene and its application, relating to the field of plant genetic engineering. Specifically, the benzyl alcohol synthesis gene is disclosed as OsCAD6, with the nucleotide sequence SEQ ID NO: 1; the nucleotide sequence encoding the protein region is SEQ ID NO: 2; and the amino acid sequence encoding the protein is SEQ ID NO: 3. By knocking out the OsCAD6 gene in the background of Zhonghua 11 rice using CRISPR / Cas9, a mutant oscad6 was obtained, reducing benzyl alcohol content in rice leaves to less than 30% of the wild type. Phenotypic analysis and metabolite determination were conducted, and the Ubipro-OsCAD6-pMDC32 overexpression vector was constructed to create OsCAD6 overexpression plants. This invention is based on the fact that OsCAD6 loss of function leads to a significant decrease in benzyl alcohol content in rice, while OsCAD6 overexpression leads to a highly significant increase in benzyl alcohol and SA content in rice.

The present application relates to the technical field of agricultural monitoring, in particular to a field crop investigation system and method based on AR glasses and edge computing. The system comprises an image acquisition unit, a main processor and a storage unit, an augmented reality display unit, a human-computer interaction unit, a sensor and positioning unit, a power management unit and a special investigation program software deployed on a mobile operating system. Multiple target recognition models are integrated in the AR glasses. The models are structured pruned, dynamically range quantified and hardware-aware accelerated by a deep collaborative optimization technology. The inference process is performed on a local processor for edge computing. The present application eliminates the delay of image data transmission to the cloud and feedback, saves bandwidth and protects privacy, and significantly improves inference efficiency. Multiple crop disease and pest models and phenotype analysis models are integrated. Disease and pest identification and classification are completed within 50-300 milliseconds, and the end-to-end delay is less than 500 milliseconds, fully meeting the needs of real-time field investigation.

The application discloses a rice tricyclazole-resistant gene screening method and system based on phenotype analysis, and relates to the technical field of plant gene screening; the method comprises the following steps: based on induced leaves, pre-induction leaves are subjected to induction treatment, and time-series phenotype data of the pre-induction leaves and post-induction leaves are collected to obtain induction data; based on baseline leaves, pre-baseline leaves are subjected to baseline treatment, and time-series phenotype data of the pre-baseline leaves and post-baseline leaves are collected to obtain baseline data; based on the induction data and the baseline data, disease course extraction is performed on the time-series phenotype data of the pre-baseline leaves to obtain basic resistance data; and based on material site data, post-treatment performance data, the basic resistance data and induction gain data, tricyclazole-resistant gene data is screened out; the application can distinguish the tricyclazole induction influence from the difference in the self resistance of rice materials, and realizes stable screening of tricyclazole-resistant related genes.

This invention discloses a method and system for fish pathological detection based on image segmentation and visual phenotypic analysis. The invention utilizes the SAM2 model as a high-precision contour extraction tool for real-time segmentation and recognition of each fish, solving the problem of limited pathological feature extraction caused by the inability to obtain accurate contours in existing technologies. Simultaneously, its zero-sample and few-sample segmentation capabilities allow the invention to adapt to different fish species without extensive annotation, addressing the pain points of weak model generalization ability and strong data dependence. Furthermore, this invention achieves real-time and highly efficient automated pathological monitoring and early warning, improving diagnostic accuracy, timeliness, and efficiency compared to traditional manual detection, while reducing monitoring costs. Moreover, this invention can independently assess the health status of each fish and provide immediate warnings for abnormal individuals, thus achieving fine-grained assessment at the individual level, making it highly suitable for large-scale application and promotion.

The present application relates to the field of plant genetic engineering technology, and particularly relates to the application of algal NySAT2 gene in regulating alfalfa crude protein and washing fiber. The present application uses gene synthesis and codon optimization technology to obtain the sequence of the coding region of the codon-optimized NySAT2 gene, constructs an algal NySAT2 gene overexpression vector, introduces the target gene into the alfalfa genome through the Agrobacterium rhizogenes-mediated genetic transformation method, and obtains the transgenic plant using the genetic transformation system. Through molecular identification and phenotype analysis, it is found that the alfalfa plants overexpressing the NySAT2 gene have a significantly increased crude protein content compared with the wild-type alfalfa plants, while the contents of acid detergent fiber and neutral detergent fiber are significantly reduced, effectively improving the forage quality and feeding value of alfalfa.

This invention discloses a high-throughput extraction, segmentation, and functional trait inversion method for multidimensional phenotypic analysis of plant leaves based on computer vision, belonging to the field of plant phenotypic feature extraction technology. It aims to solve the problem of efficient high-throughput analysis of multidimensional traits in plant leaves. The invention includes acquiring plant leaf sample images, performing high-throughput data scheduling and image decoding to obtain a decoded plant leaf sample image matrix; mapping to the HSV color space, then performing Gaussian convolution processing, adaptive threshold segmentation, and physical scale correction to obtain binary images of plant leaf samples. These binary images are then subjected to multi-objective topological separation and petiole segmentation to obtain clean binary leaf images. The method calculates multidimensional trait indicators of the leaves, including basic geometric traits, edge morphology and serration features, symmetry and leaf center point, and leaf insect damage ratio, and infers higher-order ecological functional traits of the leaves, including single leaf volume, leaf mass density, specific leaf area, and leaf dry matter content.

The application discloses a method for creating a double-tail phenotype by using a cytosine single-base editing system to inactivate a goldfish szla gene and application thereof; a target sequence corresponding to sgRNA in the cytosine single-base editing system is shown as SEQ ID NO:1, a guide sequence is shown as SEQ ID NO:4, and a skeleton sequence is shown as SEQ ID NO:5. The method comprises the following steps: (1) obtaining sgRNA of the cytosine single-base editing system; (2) introducing the cytosine single-base editing system; and (3) performing phenotype analysis. The application has the advantages of precise target selection, clear editing result, predictable mutation type, convenient molecular detection and high phenotype induction efficiency, and can be used for precise creation of double-tail goldfish, development function research of a goldfish tail fin and molecular design breeding of ornamental fish.

The application discloses a greenhouse tomato fruit phenotype analysis method based on edge computing and improved YOLOv11; the method comprises the following steps: firstly, an A-RepViT visual module is designed to enhance deep semantic representation without increasing inference burden, double-layer routing attention and efficient channel attention are introduced to optimize feature fusion, the perception ability of the model in a complex scene is improved, and an enhanced ECIoU loss function is proposed to improve the detection ability of small targets; secondly, an edge end phenotype analysis system is constructed by using an RGB-D depth camera and an NVIDIA Jetson Orin Nano, the length, the short diameter and the depth information of the fruit are calculated by fusing the detection frame and the depth information, the fruit is segmented by using HSV, and the volume is estimated by using a numerical integral formula; and the application can effectively overcome environmental interference and is expected to provide a high-precision and low-delay solution for mobile phenotype analysis in an intelligent greenhouse.

This invention discloses a system and method for standardizing plant chlorophyll fluorescence data, relating to the fields of plant phenotypic analysis and precision agriculture. The system includes: a chlorophyll fluorescence measurement unit configured to acquire raw fluorescence data from measurement points on the plant canopy; a three-dimensional structural imaging unit configured to acquire a three-dimensional structural model containing the measurement points; and a processing unit configured to: spatially register the coordinates of the measurement points in the measurement system coordinate system with the coordinates in the three-dimensional structural model; determine the incident vector and observation vector based on a pre-calibrated offline measurement system; calculate the normal vector of the leaf surface at the measurement point from the three-dimensional structural model; and construct a standardized model based on the incident vector, observation vector, normal vector, and fill factor; and calculate the raw fluorescence data to generate standardized fluorescence data. This system eliminates interference caused by geometric relationships, obtaining comparable data.

This invention discloses a method for quantifying three-dimensional plant architecture and evaluating light interception capacity in multi-genotype plants. The method includes the following steps: Step 1, multi-view image acquisition; Step 2, high-fidelity three-dimensional plant structure reconstruction; Step 3, three-dimensional phenotypic parameter extraction; Step 4, plant architecture classification based on unsupervised learning; Step 5, virtual plant population construction; Step 6, calculation of light interception for different plant architectures based on a radiative transfer model. This invention achieves quantitative acquisition and comparability of plant architecture structural parameters by constructing a high-fidelity three-dimensional plant structure model. Based on this, it completes automatic plant architecture classification and further simulates and evaluates the light interception process based on real plant architecture parameters, thus forming an integrated evaluation system of plant architecture and light energy utilization supported by three-dimensional structure. This system can be used for plant architecture phenotypic analysis, resource utilization efficiency evaluation, and breeding screening.

This invention discloses a method for crop phenotypic analysis and conditional image generation based on depth vision, involving the interdisciplinary field of agricultural artificial intelligence and crop breeding phenotypic analysis. The specific steps of this method are as follows: acquiring original RGB images of plants and preprocessing and labeling them to construct a semantic segmentation dataset; training a model to extract mask images; obtaining high-dimensional features through a deep feature encoding network; converting these features into low-dimensional deep phenotypic embedding vectors using an algorithm; constructing a conditional generation dataset to train the model; and finally, inputting the vectors to generate plant images with corresponding and continuous phenotypic changes. This invention achieves high-precision crop foreground segmentation through a semantic segmentation model, generating deep phenotypic embedding vectors supported by effective features; and constructing a conditional generation model, using this vector as a conditional constraint, through collaborative training and dynamic optimization, generating images that both conform to crop growth patterns and match preset parameters, providing strong support for agricultural research and production.