21 results about "Plant phenotyping" patented technology

The application discloses a kind of artificial climate chamber for plant phenotype monitoring and climate automatic regulation, it is related to artificial climate chamber technical field, artificial climate chamber is built-in several cultivation frame, artificial climate chamber includes cabin, regulating device and air distribution device, imaging device is built-in cabin, cabin is equipped with heat preservation cavity and air inlet, air inlet and heat preservation cavity are communicated, air inlet inlet section is equipped with compensation groove, compensation groove and heat preservation cavity are communicated, regulating device includes temperature control assembly and compensation component, compensation component and temperature control assembly are electrically connected, compensation component includes temperature sensing piece, temperature sensing piece includes two sections of material of different thermal expansion coefficients, air distribution device includes fan, fan and air inlet inlet pipeline are communicated, temperature sensing piece one end is inserted into air inlet, and the other end is inserted into compensation groove.In the process of plant cultivation, the plant to be cultivated is placed on the cultivation frame, double-row cultivation frame can be used in the artificial climate chamber, and multiple layers are provided on each cultivation frame to ensure the cultivation efficiency.

This invention provides a handheld method and device for detecting vegetation index of crop leaves, relating to the field of plant phenotyping technology. The method includes: controlling a contact linear array image sensor to scan the leaf of the crop to be tested line by line in the shaded leaf channel to obtain multi-channel reflectance grayscale digital quantities corresponding to each band; sequentially performing radiation baseline drift suppression, fluorescence cross-channel crosstalk compensation, and crosstalk bias correction on the multi-channel reflectance grayscale digital quantities to obtain multi-channel response data; determining the vegetation index of the leaf of the crop to be tested based on the multi-channel response data, and extracting the leaf phenotypic parameters of the leaf of the crop to be tested; determining the growth status of the leaf of the crop to be tested based on the vegetation index and leaf phenotypic parameters. This method can suppress the influence of ambient light interference, fluorescence crosstalk, and multi-band crosstalk on the measurement results, improve the accuracy of the vegetation index and the reliability of the leaf phenotypic parameters, and is beneficial for high-frequency, continuous, single-person handheld operation under field conditions.

The application belongs to the technical field of agricultural monitoring, and discloses a plant phenotype high-throughput monitoring system based on matrix control, which comprises a matrix monitoring module, a matrix plant phenotype detection environment is constructed, and to-be-detected plant samples are arranged in N plant cultivation units; each plant cultivation unit is respectively arranged with a corresponding environment regulation and control device, and is mapped into an addressable matrix node based on a logical addressing rule, and the matrix nodes form an environment control matrix through a distributed parallel communication link; a multi-factor monitoring module is arranged, a multi-factor collaborative monitoring network is built, a distributed sensor array arranged in the matrix nodes is arranged, a preset matrix sampling time sequence table is used, and each matrix node is uniformly triggered to synchronously collect SPAC parameters based on the preset matrix sampling time sequence table; the application breaks through the limitations of traditional plant phenotype collection means in terms of processing scale, regulation accuracy and data space-time consistency, and realizes automatic and intelligent management of the whole process of plant phenotype monitoring.

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.

This invention relates to the technical field of plant phenotypic information acquisition equipment, and discloses a dual-purpose (rail and road) wheeled phenotypic information acquisition platform, including a long-distance platform frame. The long-distance platform frame is equipped with a spacing adjustment mechanism, enabling the acquisition of plant phenotypic information. During acquisition, it can capture images of the entire plant, performing rotating circular acquisition, resulting in a wide range of acquisition efficiency. This overcomes the problem of existing acquisition methods that cannot capture images of the entire height of the plant. It can move on railway tracks and highways, broadening its application range. Furthermore, it can simultaneously acquire images of multiple plants, resulting in high acquisition efficiency. The wheel spacing can be adjusted during acquisition to accommodate acquisition movements of different widths.

The application discloses a plant phenotype analysis intelligent agent system based on a large language model. The system adopts a star topological structure, takes a planning intelligent agent as a hub, and is provided with special intelligent agents for vision, model fine tuning, code generation, GWAS analysis and paper writing, so that an overall process from multi-scale plant image processing, zero sample plant image segmentation, plant three-dimensional reconstruction and point cloud segmentation, neural network model optimization, visual chart drawing, genotype-phenotype analysis to scientific research paper writing is realized, and a one-stop intelligent solution is provided for accelerating high-throughput phenomics and genomics correlation research.

The application discloses a kind of small RNA fusion multiomics plant phenotype prediction method, it is related to bioinformatics and agricultural biology field, including the following steps: obtaining and pre-processing genotype, transcript expression and small RNA expression data, obtain standardized feature matrix;The standardized feature matrix is screened based on the feature of pearson correlation, and the corresponding genotype, transcript expression, small RNA expression feature subset is obtained;Based on the pre-trained MirGP prediction model, by multi-branch feature extraction, multi-level efficient channel attention fusion and regression prediction, output plant phenotype prediction result.The method introduces multiple small RNA regulation characteristics and constructs hierarchical deep learning fusion architecture, expands the multiomics feature dimension, reduces data redundancy, realizes the effective integration and deep mining of cross-modal features.

The application relates to the technical field of three-dimensional reconstruction, and particularly discloses a semantic plant three-dimensional reconstruction and phenotype extraction method and system, which comprises the following steps: acquiring a multi-view image sequence of a target plant; performing organ-level semantic segmentation on the image of each view to obtain semantic segmentation masks of each organ; based on the principle of geometric consistency, performing cross-view organ instance ID matching on the multi-view semantic segmentation masks to generate unified instance identification of the same organ under different views; fusing the semantic information of the unified instance identification and performing a three-dimensional Gaussian splashing reconstruction process to generate a three-dimensional point cloud model with organ-level semantic labels; and extracting predetermined plant phenotype parameters based on the semantic three-dimensional point cloud model. The application solves the problems of three-dimensional reconstruction without semantics, a fragmented phenotype extraction process, and cross-view organ correlation confusion, realizes full-process automation and high-precision extraction from images to phenotype parameters, and significantly improves the efficiency and objectivity of plant phenotype analysis.

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 mobile phenotypic information acquisition platform and method based on the entire plant growth cycle, belonging to the field of intelligent plant phenotypic detection technology. It includes acquiring image sequences and depth point cloud data of multiple key growth stages of a target plant, and constructing a multi-dimensional phenotypic feature tensor representing the joint information of the plant's three-dimensional morphological structure and color texture through spatiotemporal registration and fusion. The tensor is input into a learnable phenotypic parsing network, and a dynamic phenotypic evolution map is generated by iteratively enhancing the plant organ feature response and suppressing the background feature response. Key phenotypic trait parameter sequences from budding to maturity are extracted, and the growth trend degree and developmental stability scores of each sequence are calculated, sorted, and integrated to form a hierarchical full-cycle phenotypic atlas. This method can achieve multi-dimensional phenotypic information fusion representation, weaken background interference, accurately depict the dynamic evolution process of phenotypic changes, and clearly present the correlation between phenotypic states at each growth stage.

This application relates to an intelligent environmental collaborative control method and system for plant cultivation in confined spaces. The method includes: collecting multidimensional environmental and plant phenotypic data to generate an input state vector containing the errors and rates of change of each parameter; inputting the input state vector in parallel into a fuzzy controller with a built-in coupled rule base, and outputting a preliminary decision vector through inference; using an improved particle swarm optimization algorithm with time multiplication by the integral of absolute error as the objective to optimize the quantization factor and scaling factor of the fuzzy controller, generating a control decision vector; addressing system feedback delay by connecting Smith predictors in parallel to each loop, utilizing delay-free predictive output to participate in feedback regulation in advance, eliminating lag effects, and obtaining a compensated final control command vector; monitoring photovoltaic power generation and battery status, running a variable-step MPPT algorithm to calculate available power, and if power supply is insufficient, initiating an energy consumption optimization strategy to adjust the load or regenerating and executing commands.

1. Name of the product in this design: Plant phenotyping analyzer. 2. Purpose of this design: This design is an instrument used for measuring and analyzing the appearance of plants. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application discloses a high-throughput plant phenotype data analysis platform based on artificial intelligence technology, which comprises an image acquisition unit, a deep feature extraction unit, a phenotype feature extraction unit, a quality prediction unit and a comprehensive evaluation unit; deep features of plant images are extracted through a multi-scale convolutional neural network, adaptive multi-scale feature fusion is carried out through an attention mechanism, plant physiological and biochemical indexes are predicted based on a deep regression network, and a comprehensive phenotype evaluation index is calculated by using an entropy weight method-hierarchical analysis method combined weighting method; the application realizes rapid, accurate, non-destructive and systematic analysis of plant phenotypes, and provides an efficient and intelligent technical tool for plant breeding, quality evaluation and precision agriculture.

This invention provides a growth information analysis device that selects traits that can be measured without harvesting the plant (such as initial growth, internode length, and leaf area), and predicts growth by determining the relationship between these indicators and biomass. [Solution] The growth information analysis device 100 comprises a temperature analysis unit 110 that analyzes temperature from a thermal image, a spectral range analysis unit 120 that analyzes the spectral range from a hyperspectral image, a crop recognition unit 130 that separates and recognizes crops from the background in an RGB image, a video synthesis unit 140 that generates a multi-layered composite image using a merged image obtained by combining the thermal image, hyperspectral image, and RGB image, a data storage unit 150 that stores the multi-layered composite image and merged image synthesized by the video synthesis unit, and an artificial intelligence analysis unit 160 that uses the stored images to classify the merged images stored in the data storage unit according to the phenotype of the crop and construct a database.

The application provides a multispectral plant physiology-environment coupling monitoring wearable sensor and method, and belongs to the technical field of agricultural information sensing and plant phenotype monitoring. The multispectral plant physiology-environment coupling monitoring wearable sensor comprises an upper clamping arm and a lower clamping arm. The upper clamping arm comprises an upper base, an excitation light source arranged on the upper base and facing the upper epidermis of a leaf, two first multispectral detectors symmetrically arranged on both sides of the excitation light source, and an upper light guide layer covering the upper epidermis of the leaf. The excitation light source is located at the geometric center of the upper base. The lower clamping arm comprises a lower base, a second multispectral detector arranged on the lower base, and a lower light guide layer covering the lower epidermis of the leaf. The second multispectral detector is located on the optical axis of the excitation light source. The three multispectral detectors are arranged to obtain the reflection, transmission and absorption spectra of the leaf, and the physiological index is inferred by coupling with environmental factors, so that the plant health state can be monitored in real time in the scenes of precision agriculture, ecological monitoring and the like.

This invention relates to the field of smart agriculture and plant phenotypic monitoring technology, specifically to a method and system for early diagnosis of agricultural pests and diseases based on multimodal bioelectrical signals. A high-sensitivity sensor array deployed at designated parts of the crop collects multimodal bioelectrical signals, including surface potential, transmembrane current, and damage-induced local potential. Microenvironmental parameters are collected using near-ground environmental sensors. A dedicated processing chip extracts time-domain and frequency-domain bioelectrical features from the bioelectrical signals in real time. The bioelectrical features and microenvironmental parameters are input into a multimodal fusion model for joint analysis. This model uses a spatiotemporal attention and adaptive gating fusion mechanism to distinguish between pest and disease stress and environmental interference. Based on the analysis results and a confidence assessment, early warning information is generated. This invention overcomes the limitations of traditional morphological detection, achieving ultra-early and highly reliable early warning by analyzing the crop's own "electrophysiological distress signals" before lesions are visible to the naked eye.

The application provides a plant dense 3D point cloud reconstruction method and system, and applies to the technical field of plant 3D phenotype research, and comprises the following steps: performing image exposure compensation preprocessing based on a multi-view image sequence to obtain an image sequence after exposure compensation; the image exposure compensation preprocessing adopts a hierarchical exposure control strategy, and exposure states of the multi-view image sequence are standardized to a unified target interval; performing sparse point cloud reconstruction based on the image sequence after exposure compensation to obtain a sparse point cloud and a camera trajectory; performing 3D Gaussian sputtering rendering based on the sparse point cloud and the camera trajectory, and obtaining an optimized 3D Gaussian model; and performing adaptive densification sampling on the optimized 3D Gaussian model to generate a plant dense 3D point cloud. Through the application, multi-view video / image data acquired by a consumer-level image / video acquisition device can be used to complete plant dense 3D point cloud reconstruction, and the quality of the reconstructed point cloud is significantly improved.

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 present application relates to the field of plant phenotype analysis, and provides a crop ear three-dimensional kernel grain phenotype analysis method and system based on weakly supervised neural radiance field. The method comprises: kernel grain surface labeling on an ear image sequence to obtain a labeled data set; inputting the labeled data set into a semantic segmentation model for training to obtain a kernel grain surface segmentation model, so as to infer a multi-view image sequence to obtain a kernel grain surface binary mask set of multiple views; taking the binary mask set as a weakly supervised signal to perform segmentation perception fine-tuning on a neural radiance field model to obtain a kernel grain surface three-dimensional volume representation; extracting a high-density three-dimensional point cloud from the kernel grain surface three-dimensional volume representation, and performing hierarchical clustering segmentation on the high-density three-dimensional point cloud to obtain a kernel grain instance point cloud set; and calculating a total number of kernel grains and a kernel grain row number. The present application realizes sparse labeling driven three-dimensional reconstruction, and realizes automatic counting of kernel grains and analysis of the row number of kernel grains without causing damage.

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.