185 results about "Plant phenotyping" patented technology

The invention relates to the technical field of plant phenotype image processing, and particularly discloses a colloid culture plant root system three-dimensional characteristic quantification method and device, which are used for solving the problem that rapid and lossless three-dimensional observation and accurate analysis on a plant root system are difficult to realize by the existing traditional root system measurement tool and measurement method. The method comprises the following steps: culturing a plant root system under a colloid medium, acquiring a plant root system image, pre-processing the plant root system image, performing three-dimensional reconstruction on the plant root system based on SFM and NeRF algorithms, automatically extracting plant root system characteristics based on a three-dimensional reconstruction model, and integrating an optical calibration tank device into an imaging system. The optical refraction of the curved surface of the glass culture bottle can be effectively corrected, a three-dimensional point cloud is reconstructed in combination with a high-precision three-dimensional reconstruction algorithm, lossless in-situ measurement is realized, the dynamic growth process of a root system is captured, and the morphology and distribution characteristics of the root system are analyzed.

The invention relates to the technical field of crop monitoring, in particular to a crop phenotypic parameter automatic calculation and extraction method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic or semi-automatic segmentation on the images determined in the same coordinate system to obtain a multi-source crop remote sensing image cell segmentation map; extracting plant phenotypic parameters, physical parameters and chemical parameters from the multisource crop remote sensing image cell segmentation map by using an algorithm; the plant phenotype parameters comprise a vegetation index, a plant height, a surface area, a volume, a canopy coverage degree and a vegetation projection area; the physical parameters comprise a canopy average temperature value, a canopy temperature standard deviation and a canopy temperature variation coefficient; the chemical parameters comprise chemical elements such as nitrogen, phosphorus, potassium, calcium and magnesium in soil and vegetation. The method has the advantages that large-scale data processing and high-precision area prediction are realized, and the crop growth monitoring capability is enhanced.

The invention relates to the field of disease and insect pest monitoring, and discloses a garden plant disease and insect pest intelligent early warning system based on Internet of Things, comprising an environment monitoring module for collecting environment data related to disease and insect pest breeding based on a sensor network; the pest trapping and counting module is used for trapping pests by using an intelligent trapping device of a pest attractant and is provided with a counting sensor and a pest classification and identification unit to obtain the population density of different types of pests; the plant phenotype monitoring module captures plant phenotype changes by deploying high-definition multispectral camera equipment, and automatically diagnoses pest and disease damage types by using an image recognition algorithm; and the disease and pest early warning module analyzes by using a preset early warning model based on the data of the environment monitoring module, the pest trapping statistics module and the plant phenotype monitoring module to obtain disease and pest indexes, compares the disease and pest indexes with a preset threshold value, and immediately sends out early warning information once the disease and pest indexes exceed the preset threshold value.

The invention relates to the technical field of plant phenotype data processing, in particular to a plant phenotype automatic acquisition and feature processing method oriented to a multi-element environment. According to the method, the plant multi-modal phenotypic data are synchronously collected through the Internet of Things nodes, the coupling strength is calculated in combination with the environmental factors, the phenotypic parameters are divided into an environment sensitive type and a steady type, and the data classification accuracy is improved. A growth trend field is constructed by means of phenological phase decoupling analysis and geographic space superposition, abnormal segments are dynamically eliminated by combining physiological synergy degree and a mutation detection algorithm, and trend analysis stability is enhanced. A phenotype-environment response map is generated through standardized processing and an environment equivalent value model, a key environment sensitive window is recognized, a regional and staged cross-environment regulation scheme is output, intelligent and accurate management of the crop growth environment is achieved, and the method is suitable for agricultural yield increase and breeding optimization.

A method of calibrating hyperspectral images to account for diurnal changes is disclosed which includes receiving a plurality of hyperspectral images obtained over a plurality of days from a field, deriving a plurality of spectra from the received plurality of hyperspectral images, decomposing the derived plurality of spectra into a trend component representing a trend associated with the plurality of days, subtracting the trend component from the derived plurality of spectra to thereby generate diurnal spectra for one or more wavelengths, fitting one or more mathematical functions associating spectrum to time of day to the generated diurnal spectra for each of the one or more wavelengths, generating a mode from the fitted mathematical function for each of the one or more wavelengths, and applying the model to the derived spectra at a first time to generate a calibrated spectra at a second time.

The invention relates to a plant phenotype shape parameter extraction method, device and equipment and a medium, and the method comprises the steps: introducing a density factor, a curvature factor and a sampling distance into an original farthest point sampling algorithm in a first plant point cloud segmentation model, and introducing discrete wavelet transform in a feature extraction stage to enhance the multi-scale feature representation of point cloud data, integrating a kernel point convolution layer in the set abstraction layer, and working in parallel with a point convolution layer in the original plant point cloud segmentation model to construct a second plant point cloud segmentation model; and inputting the original point cloud data corresponding to the to-be-detected plant into the second plant point cloud segmentation model trained to the convergence state to determine the plant phenotype shape parameter corresponding to the to-be-detected plant so as to complete the extraction of the plant phenotype shape parameter. According to the method, extraction of local features is optimized, so that the precision of plant organ segmentation is improved.

A measurement system including: a. a sensor system that includes: i. a Light Detection And Ranging (LiDAR) module with a laser emitter configured to generate measurement data representing raw range measurements to measure heights of a crop, and ii. a computing module, including: at least one wireline / wired communications module configured to communicate with the LiDAR module for the computing module to acquire the measurement data from the LiDAR module; and at least one wireless communications module configured for the computing module to communicate using a wireless connection / link with a remote computing system that is configured receive the acquired measurement data and to determine / calculate / estimate phenotypic quantities of the crop based on the measured heights for the purpose of high-throughput plant phenotyping (HTPP); and b. a mobile / vehicle mount configured to hold / support the sensor system above the crop and to direct the laser emitter towards the crop.

The present invention discloses a rigid-flexible coupling mechanical arm type plant phenotypic information measurement platform and measurement method, including a crawler walking module, a flexible mechanical arm module and a soft mechanical arm module; the soft mechanical arm module includes a rotating motor and a soft mechanical arm component; the soft mechanical arm component includes an index finger component, a thumb component, a soft pad, etc.; the surface of the index finger end joint, the thumb end joint and the soft pad is arranged with a sensor 1 and a sensor 2; the flexible mechanical arm module of the present invention can accurately control the position and direction, and perform efficient and large-scale phenotypic detection. By designing the flexible joint of the mechanical arm, the vibration generated when the mechanical arm moves is reduced, and damage to the plant is avoided when the plant phenotypic information is manually collected, so as to achieve high-precision and high-efficiency measurement of the plant phenotypic information. The present invention realizes accurate, automatic and non-destructive measurement of the phenotypic parameters of plant organs and the phenotypic parameters of the whole plant through the coordination of the index finger and thumb joint of the flexible part and the telescopic arm of the rigid part.

The invention relates to the field of plant phenotype recognition and intelligent breeding, and discloses a multi-source data fused cotton phenotype characterization and breeding decision convolutional neural network platform, which comprises an image acquisition module, an environment sensing module, a molecular marker input interface, a data preprocessing module, a multi-modal feature extraction network, an attention fusion module and an intelligent decision engine. Image features are extracted through a convolutional neural network, environment response features are modeled through a gated loop network, a uniform phenotypic vector is generated in combination with molecular marker embedding, multi-source features are weighted and fused by adopting an attention mechanism, and high-dimensional phenotypic representation is constructed; and based on the weighted cosine similarity between the target character vector and the candidate individual vector, outputting a sorting result and mating combination recommendation. The method can realize multi-factor joint modeling of complex agronomic traits and target-oriented breeding path decision, has the advantages of high accuracy, high interpretability and high decision transparency, and is suitable for precise breeding and intelligent recommendation of large-scale cotton materials.

The invention provides a track type high-flux crop phenotype platform digital twinborn map construction method and device, and the method comprises the steps: collecting multi-source phenotype data of a planting land parcel region according to a constructed track phenotype platform, and constructing a track phenotype platform twinborn body in a digital twinborn space; preprocessing the multi-source phenotype data to obtain three-dimensional point cloud data and plant phenotype preprocessing data of the planting plot area; according to the plant phenotype preprocessing data, constructing to obtain a plant twinborn body; and constructing a regional environment twinborn body of the planting plot region, and integrating the track phenotype platform twinborn body, the plant twinborn body and the regional environment twinborn body in the digital twinborn space to obtain a digital twinborn map. By means of the plant phenotype digital twinning construction method and device, the defects that during plant phenotype digital twinning construction, digital twinning construction of a plant body is lacked, and fusion application of multi-modal data collected on a phenotype platform in a unified twinning space is lacked are overcome.

The invention provides a plant phenotype multi-precision information joint prediction method, and relates to the technical field of plant phenotype prediction, and the method comprises the steps: data acquisition and preprocessing; constructing and training a model; through interpretability analysis, the method can be applied to different species and various plant traits and can be expanded to other biological fields; new omics data and analysis functions can be flexibly supplemented, and the future requirements of large-scale multi-omics research are met; compared with a traditional linear or general machine learning method, the deep learning model is better in performance in a complex gene interaction scene, and stable prediction performance is kept.

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 provides a plant phenotype platform three-dimensional imaging method and device based on multi-sensor fusion, and the method comprises the steps: collecting the original three-dimensional point cloud data of a plant through a plurality of heterogeneous imaging sensors carried on a plant phenotype platform, and carrying out the time synchronization processing and space registration processing. And dynamically calculating the confidence coefficient weight of each data point according to the environment illumination condition, the measurement attribute of each data point in the registration point cloud set and the characteristics of the imaging sensor to which the data point belongs, and obtaining a point cloud set with a point-level confidence coefficient weight. Performing adaptive weighted fusion processing according to the spatial distribution of the point cloud set to generate a preliminary fusion point cloud model; and according to a quality index of the model and a real-time environment illumination condition, dynamically adjusting and calculating a weight parameter of a confidence coefficient weight, and finally outputting an optimized three-dimensional point cloud model after iterative optimization. The defect that a fusion result of an existing plant three-dimensional phenotype accurate acquisition scheme is insufficient in stability and precision in a complex and changeable field environment is overcome.

The invention relates to a controlled environment agriculture for plant cultivation using artificial lights. The artificial lights comprise an array of light emitting diodes fabricated using gallium nitride, each gallium nitride operable over a wavelength of 380 nm to 900 nm. The array of light emitting diodes include at least one integrated drive controller and at least sensor. The controlled environment agriculture includes at least an imaging device and a control module. The control module comprises a machine learning module and an aggregator module configured connected to at least one sensor and at least one imaging device to aggregate various parameters including environmental data, and plant phenotyping data associated with the plants. Based on the aggregated The control module determines the spectral requirements of the plants for optimal growth based on at least one parameter aggregated from at least one sensor, at least one imaging device and a database to adjust the spectral light in the array of light emitting diodes in real time. The control module is associated with a power module for regulating the electrical power required by the array of light emitting diodes to optimize power consumption. In at least one implementation, the control module and the power module are dynamically controlled in real time by implementing machine learning algorithms to automate the controlled environment for agriculture to optimize and control the spectral and power requirements of the array of light emitting diodes.

The invention discloses an infrared corn drought identification method based on wavelet boundary enhancement, and belongs to the field of agricultural informatization and plant phenotype identification, and the method comprises the following steps: S1, obtaining an infrared image of a corn plant; s2, executing two-dimensional discrete wavelet transform to generate a boundary response diagram; s3, generating a binary mask according to the characteristics of the boundary response diagram to the blade edge; s4, connected domain analysis is executed, candidate leaves are obtained, and independent single-leaf masks are generated; s5, the small holes are removed, a mask is obtained, and the blade curvature is quantified; s6, constructing a multi-dimensional feature vector reflecting the curling characteristics of the blade; and S7, outputting the drought grade corresponding to the corn leaf through the multi-layer perceptron model. By adopting the method, automatic identification and early warning of the early-stage water shortage state of the corn are realized.

The invention discloses a plant growth condition observation system based on AI image recognition, and belongs to the technical field of intelligent agriculture and plant phenotype monitoring. The system quantitatively defines an ideal health state of a plant by constructing a plant geometric baseline model and a spectral physiological baseline model, and in observation, the system compares real-time three-dimensional point cloud, real-time hyperspectral data and real-time texture image multi-modal data which are acquired in real time with the health baseline model to determine the health state of the plant. Generating quantitative stress characteristic data such as a geometric deformation index and a spectrum deviation index, performing fusion analysis on stress characteristics by utilizing a neural network, and outputting a comprehensive physiological stress index for quantifying stress severity and an attribution vector for determining stress reasons by combining a causal attribution model; according to the invention, an intelligent closed loop from passive monitoring to active intervention is realized, the environment can be automatically regulated and controlled according to a diagnosis result, self-optimization is carried out through a verification and learning mechanism, and the automation level and precision of plant growth regulation and control are greatly improved.

Transgene-free genome editing of plants in T0 generation is highly desirable but challenging, especially in perennials and vegetatively propagated plants. Here is provided a co-editing strategy for generating transgene-free, gene-edited plants via Agrobacterium-mediated transient expression of cytosine base editor (CBE) / gRNA-Cas 12a / crRNA-GFP in plants. Specifically, CBE / gRNA was used to base edit the AES gene to confer resistance to herbicide chlorsulfuron as a selection marker, which has no negative effects on plant phenotypes; Casl2a / crRNA was used for editing genes(s) of interest; GFP was used for selecting transgene-free transformants. Using this approach, transgene- free genome-edited plants were efficiently generated for various genes (either individual or multiplex) in tomato, tobacco, potato, and citrus in the T0 generation. The biallelic / homozygous transgene-free mutation rates for target genes among herbicide-resistant transformants ranged from 8% to 50%. Whole genome sequencing further confirmed transgene-free and absence of off- target mutations in the edited plants. The co-editing strategy is efficient for generating transgene- free, genome-edited plants in the T0 generation, thus being a potent tool for plant genetic improvement.

The invention discloses a crop three-dimensional point cloud branch and leaf separation method of a semantic prototype driven graph attention network, and relates to the technical field of plant high-throughput phenotypic analysis and three-dimensional computer vision, local geometric features are extracted by adopting random downsampling and an attention pooling mechanism, the point cloud scale is reduced by randomly generating a pooling index, and the point cloud branch and leaf separation efficiency is improved. And performing weighted aggregation on neighborhood features by using geometric topological coding, constructing a dynamic topological structure based on a K-nearest neighbor algorithm, aggregating node features in a multi-level manner through a graph attention network, establishing dual constraints of geometric difference perception and feature association, and realizing deep integration of local and global context information. The method comprises the following steps of: performing up-sampling on low-resolution features step by step by using K-neighbor interpolation based on attention weighting to reconstruct high-resolution features, performing weighted fusion on the high-resolution features corresponding to a coding stage through jump connection, improving a distribution structure of a category feature space through an inter-class separability discrimination optimization function, and ensuring the stability of feature distribution.

The invention provides a phenotype data measurement method and system in a semi-automatic field scene, and belongs to the field of phenotype data measurement in an outdoor field scene, and the method comprises the steps: collecting a crop image, and obtaining a target phenotype center coordinate point specified by a user according to the crop image; segmenting the crop image and a target phenotype center coordinate point specified by a user through a pre-trained SAM model to obtain a target phenotype mask image; the morphological characteristics of the mask image are obtained by judging the bending characteristics of the mask image, and the morphological characteristics comprise an upright form and a bending form. According to the plant phenotype data calculation method, data collection, calibration and model training are not needed, the method can be widely applied to various plants and scenes, and a large amount of cost caused by data annotation and model training can be avoided.

The invention provides a digital breeding-oriented Chinese cabbage leaf cell automatic segmentation and phenotype measurement system and method, and relates to the technical field of plant phenotype analysis. According to the system, joint coding of cell contours and tissue semantics is achieved through a feature extraction sub-network, scale alignment, global feature convergence and feature re-calibration, and pixel-level cell instance masks and tissue categories are output at the same time through mask decoding and category decoding. In combination with multi-tissue phenotypic parameter classification, in-tissue phenotypic index summarization and structured output, cell-level and tissue-level automatic quantitative analysis of upper epidermis, lower epidermis, fence tissues, sponge tissues, vascular bundles and the like is realized, and the efficiency and precision of multi-tissue cell screening and phenotypic statistics are remarkably improved. The method solves the problems that in the prior art, high-precision automatic segmentation and systematic phenotypic measurement for multi-tissue cells of the cross section of the Chinese cabbage leaf are insufficient, and digital breeding decision making is difficult to directly serve.

The invention relates to the technical field of plant phenotype acquisition equipment, and discloses an acquisition device and method for acquiring outdoor plant phenotype data, the acquisition device comprises a walking track, a bottom support is fixedly assembled on the outer edge of the walking track, and a height adjusting mechanism is fixedly assembled on the top of the bottom support. A connecting assembly is installed on the outer edge of the height adjusting mechanism, and a direction adjusting assembly is installed on the inner wall of the connecting assembly. Through the arrangement of the direction adjusting assembly, the direction adjusting assembly can enable the acquisition camera equipment to move leftwards and rightwards, so that the acquisition camera equipment not only can shoot and acquire plants on the left side of the crawler belt, but also can shoot and acquire plants on the right side of the crawler belt; the plant phenotype collecting device solves the problems that a collecting camera on an existing plant phenotype collecting device is located in the middle of the device and cannot move towards the left side and the right side, the collecting camera needs to turn around once after each column of plants are collected, and consequently a large amount of time needs to be consumed in the process, and the plant phenotype collecting efficiency is remarkably improved.

The utility model discloses an automatic whole plant phenotype imaging system, which comprises an imaging darkroom provided with a channel penetrating through the imaging darkroom, and two ends of the channel are respectively provided with a room door; the conveying mechanism is arranged in the channel; the loading box is arranged on the conveying mechanism and can be driven to move in the extending direction of the channel, a plurality of cultivation boxes are arranged in the loading box, and one side of each cultivation box is a transparent side wall; the moving mechanism is arranged in the imaging darkroom; the first phenotype imaging mechanism is arranged in the imaging darkroom and faces the detection position; the second phenotype imaging mechanism is arranged in the imaging darkroom and faces the detection position; and a control mechanism. According to the utility model, the phenotype of the whole plant can be automatically detected, and the data information of the root system and the plant is in one-to-one correspondence every time, so that the corresponding different forms of the root system and the plant in different periods can be fully reflected.

The invention provides a plant phenotype detection method and device for a vertical planting mode of a plant factory, and the method comprises the steps: scanning a target space environment, and obtaining an occupied grid map; the occupied grid map comprises area type information and multi-layer planting frame position information; determining to-be-detected sites and phenotype detection operation of to-be-detected planting pots on each layer of planting frame based on the position information of the multiple layers of planting frames; based on the region type information, controlling a phenotype detection terminal to move towards the to-be-detected site; under the condition that the phenotype detection terminal moves to the to-be-detected site, the phenotype detection terminal is controlled to execute phenotype detection operation on the to-be-detected planting pot, and a phenotype detection result is obtained. According to the invention, by positioning the spatial position of each planting pot on each layer of planting rack, the phenotype detection terminal is guided to move the to-be-detected planting pot to the detection environment, phenotype detection is carried out on the plant in the planting pot, and the accuracy of plant phenotype detection is improved.

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.

The utility model discloses an intelligent plant phenotype and pest monitoring device which comprises a support, a sliding groove is formed in the upper side of the support, a supporting plate is arranged on the outer side of the sliding groove, a moving assembly comprises a first motor arranged on the outer side of the supporting plate, a roller is arranged on one side of the first motor, and a first transmission belt is arranged on the inner side of the roller. A fixing plate is arranged on the outer side of the rotating wheel; a second motor is arranged on the outer side of the fixing plate; a second transmission belt is arranged on one side of the second motor; a driven wheel is arranged on one side of the second transmission belt; a first transmission belt on the outer side of the power output end of the first motor drives the roller to move in the sliding groove, a guide rope is fixed to a fixing frame on the lower side of the supporting plate, and the guide rope is wound or released through rotation of a rotating wheel arranged at the power output end of the second motor and a driven wheel driven by the second transmission belt on the outer side, so that the height of the monitor is changed. And diseases and insect pests at different heights can be found.

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 invention discloses a plant three-dimensional data acquisition device and method fused with a multi-source sensor, and relates to the technical field of plant three-dimensional data acquisition. The carbon rod assembly is used for connecting the two groups of tripods, and the autorotation body can move in the length direction of the carbon rod assembly; the autorotation body can be connected with a three-dimensional laser scanner; the extension ladder is used for carrying a sensor, the extension ladder can be connected to the autorotation body, the extension ladder is provided with a plurality of equipment carrying positions, and the equipment carrying positions are used for carrying and connecting the sensor; through multi-source sensor fusion and lightweight design, the dependence of the prior art on an indoor environment and a single data source is broken through, and an efficient and reliable tool support is provided for field plant phenotype research, precision agricultural management and ecological system monitoring. And the method has remarkable technical advancement and application prospect.

The application discloses a kind of stem and leaf intelligent separation and high-precision measuring device and method, and it is related to plant phenotype analysis technical field.The device includes clamping module, weighing module, visual identification module, intelligent separation module, stem processing module, leaf processing module, chopping module, drying and weighing module and central control system.After clamping module fixes plant sample, weighing module obtains initial fresh weight;Visual identification module constructs stem and leaf point cloud model by three-dimensional scanning;Intelligent separation module plans cutting path based on model and executes separation;Stem processing module measures stem diameter and fresh stem weight;Leaf processing module flattens leaf and measures leaf area;After stem and leaf are chopped, send into drying and weighing module, and determine dry weight stable point by automatic weighing.The application integrates separation, measurement, drying function in one, realizes full-process automation, solves the problem of low efficiency, sample easy to damage, poor data consistency of traditional method, significantly improves the precision and efficiency of plant phenotype parameter acquisition.

The application discloses a soybean branch angle extraction method based on point cloud, comprising: layered clustering detection is carried out on pretreated soybean single plant point cloud data, and a branch position is obtained; based on the branch position, a layered cluster containing multiple clusters is selected, a median point of the cluster is calculated, and a bifurcation point is obtained; based on the bifurcation point, the branch position is optimized, and an optimized branch point is obtained; based on the bifurcation point and the optimized branch point, a branch angle is calculated, and the soybean branch angle extraction based on the point cloud is realized. On the basis of bifurcation point detection and branch point optimization, the branch angle can be calculated based on a space vector, and the high-throughput extraction efficiency of plant phenotypes is promoted; the method provided by the application can obtain branch angles at different heights of plants, and is used for crop plant type parameter evaluation, serves for excellent germplasm resource identification and breeding utilization, and through the provision of a new technical means of nondestructive, high-throughput and high-precision, it is beneficial to accelerate the breeding process and optimize the cultivation management measures.

The utility model discloses a hyper-spectral imaging analysis system for a RhizoTronn root phenotype, which relates to the technical field of plant phenotype and spectral imaging and comprises an X-axis, an imaging unit and an imaging light source are arranged on the X-axis, and the X-axis is equipped with a plant culture module. The technical problem that an existing plant root system phenotype imaging observation device cannot conduct in-situ imaging analysis on the side face of an overground plant and an underground root system at the same time is solved. The function of performing in-situ reflected light hyperspectral and UV-MCF biological fluorescence hyperspectral imaging analysis on the overground plant side surface and the underground root system of the plant at the same time through one set of imaging unit is realized; a root phenotype analysis method is expanded to comprehensive analysis of physicochemical properties, biochemical components and physiological states of plants and root samples from two hyperspectral dimensions of reflected light and fluorescence, synchronous imaging and coupling analysis of canopy phenotypes and root phenotypes are realized, phenotype analysis cost is reduced, phenotype imaging analysis efficiency is improved, and the method is suitable for popularization and application. And the space is also saved.