123 results about "Plant phenotyping" patented technology

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.

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 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.

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 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.

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.

The application 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 comprises the following steps: S1, acquiring an infrared image of a corn plant; S2, performing two-dimensional discrete wavelet transform to generate a boundary response graph; S3, generating a binary mask according to the characteristics of the leaf edge of the boundary response graph; S4, performing connected domain analysis to obtain candidate leaves and generate independent single-leaf masks; S5, removing small holes to obtain a mask and quantifying leaf curvature; S6, constructing a multi-dimensional feature vector containing the curling characteristics of the leaves; and S7, outputting the corresponding drought grade of the corn leaves through a multilayer perception machine model. The above method realizes automatic identification and early warning of the early water shortage state of corn.

The invention provides an automatic rail changing type plant phenotype measurement platform and a rail changing method, and the platform comprises a measurement rail group which comprises at least two measurement rails arranged in a first direction, and the measurement rails extend in a second direction; the rail changing rail is arranged on at least one side of the measuring rail set in the second direction, and the rail changing rail extends in the first direction; the rail changing carrier is arranged on the rail changing track, and a carrier track extending in the second direction is arranged on the rail changing carrier; the measuring platform main body comprises a main frame body and a collecting and measuring unit arranged on the main frame body; the measuring platform main body has a switchable measuring state and a switchable rail switching state; when the measuring platform main body is in a rail changing state, the main frame body is located on the carrier rail, and the rail changing carrier can drive the measuring platform main body to move along the rail changing rail. According to the automatic rail changing type plant phenotype measuring platform and the rail changing method, the manual participation degree in the rail changing process is reduced, and the rail changing efficiency is improved.

The application discloses a plant image segmentation method based on an attention mechanism and multi-scale feature fusion, which comprises the following steps: dividing a training set, a verification set and a test set, and performing data processing; extracting multi-scale features, including deep features and shallow features; adjusting the channel weight of the deep features, and performing up-sampling processing on the deep feature maps to obtain deep feature maps guided by up-sampling; adjusting the spatial distribution weight of the shallow features to obtain shallow feature maps with adjusted spatial distribution weight; performing multi-scale fusion on the deep feature maps guided by up-sampling and the shallow feature maps with adjusted spatial distribution weight to obtain feature maps after multi-scale fusion; model training to obtain a trained plant image segmentation model; and finally, verifying and testing the model. The application can better solve the plant image segmentation and recognition problem under a complex background, has strong robustness and high accuracy, and can provide visual support for plant phenotype extraction and growth potential prediction.

A corn tassel branch counting method based on digital images and semi-supervised learning belongs to the field of plant phenotype measurement. The method is: the construction of a corn tassel branch identification model YOLOv5-C3CA: a new network model YOLOv5-C3CA is constructed based on YOLOv5 for corn tassel branch identification; a corn tassel branch identification model based on YOLOv5-C3CA model and introduction of semi-supervised learning; corn tassel identification and branch number extraction: the YOLOv5-C3CA detection model based on semi-supervised learning is applied to identify the corn tassel; after identifying the tassel, the YOLOv5-C3CA training model based on semi-supervised learning is continuously applied to extract the number of corn tassel branches. The corn tassel identification and tassel branch identification of the application are relatively accurate, which shows that the corn tassel branch counting scheme based on semi-supervised learning is feasible, which not only ensures the detection performance of the model, but also saves the data labeling workload.

The invention relates to the technical field of plant phenotype analysis, in particular to an index analysis method for plant phenotype digital diversity. The problems that in an existing plant phenotype analysis method, the data dimension is single, discretization is limited, dynamic nature is insufficient, and a system maintenance scheme is lacked are effectively solved. According to the method, environment-phenotype dynamic data is acquired by constructing a heterogeneous monitoring network, discrete phenotype data is converted into a continuous probability space in combination with a three-dimensional ellipsoid domain model, and accurate evaluation and dynamic maintenance of plant phenotype diversity are realized through subdomain decomposition, dynamic correction, thermodynamic diagram generation, depth tracking and inverse mapping analysis. According to the method, genotype-environment interaction characteristics can be effectively captured, phenotypic response risk sites and lag units can be accurately positioned, a systematic solution is provided for plant variety-environment adaptation and diversity maintenance, and the dynamic nature, accuracy and practicability of plant phenotypic analysis are improved.

The invention relates to the field of agricultural plant protection machinery and plant phenotype research equipment, in particular to an intelligent spray tower system based on biomass recognition and a control method. The system comprises a modular shell, a pressure device, a pesticide applying device, a fresh weight measuring device, a physiological signal collecting unit, a waste liquid classifying and degrading assembly, an AI parameter self-adaption module, a remote monitoring module and a computer control panel. The fresh weight measuring device is additionally provided with a double-view-angle dynamic adjusting module, and image acquisition of plants in different forms is adapted through a laser distance sensor and a stepping motor. The physiological signal acquisition unit acquires leaf humidity and pore diameter in real time and feeds back optimized pesticide application parameters; the waste liquid classified degradation assembly realizes harmlessness of the waste liquid through classified filtration and microbial degradation; the AI module adaptively adjusts parameters based on a random forest algorithm, and the remote module supports data sharing and fault early warning. According to the invention, high-efficiency cooperation of accurate spraying and nondestructive measurement is realized, and the method is suitable for scenes such as plant protection efficacy tests and plant phenotype research.

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.

The invention discloses a multi-omics plant phenotype prediction method fused with small RNA, and relates to the field of bioinformatics and agricultural biology, and the method comprises the following steps: obtaining and preprocessing genotype, transcript expression and small RNA expression data to obtain a standardized feature matrix; performing feature screening based on Pearson correlation on the standardized feature matrix to obtain corresponding genotype, transcript expression and small RNA expression feature subsets; and based on a pre-trained MirGP prediction model, through multi-branch feature extraction, multistage efficient channel attention fusion and regression prediction, outputting a plant phenotype prediction result. According to the method, multiple types of small RNA regulation and control features are introduced and a hierarchical deep learning fusion architecture is constructed, so that multiple omics feature dimensions are expanded, data redundancy is reduced, and effective integration and deep mining of cross-modal features are realized.

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 invention relates to the technical field of plant phenotype information acquisition, and discloses a crawler-type plant phenotype information acquisition device and method.The crawler-type plant phenotype information acquisition device comprises a vehicle frame, a lifting adjusting mechanism is arranged on the vehicle frame, and the lifting adjusting mechanism is used for conducting lifting adjustment during information acquisition so as to adapt to plant phenotype information acquisition of different heights; the lifting adjusting mechanism is connected with a distance adjusting mechanism, the distance adjusting mechanism is used for adjusting the distance when information is collected, the whole machine is driven by an electric system, two sets of driving motors are used for landing on the tracks on the two sides, one-way orbital transfer and pivot steering are achieved, and a battery box body can move transversely; the cameras share one lead screw guide rail and can independently ascend and descend to meet the shooting requirements of crops at different angles under the same walking cross section, camera lead screws are installed on an integral installation frame, the installation frame can synchronously ascend and descend through electric cylinders on the two sides, and integral adjustment under the same camera arrangement state is achieved.

The present invention discloses is a rigid-flexible coupling robotic arm type measurement platform and method for plant phenotype information. The measurement platform includes a crawler walking module, a flexible robotic arm module and a soft mechanical hand module; the soft mechanical hand module includes a rotating motor and a soft mechanical hand assembly; the soft mechanical hand assembly includes an index finger assembly, a thumb assembly, a soft cushion and the like; and a first sensor and a second sensor are arranged on surfaces of an index finger end joint, a thumb end joint and the soft cushion. According to the present invention, the flexible robotic arm module can accurately control the position and direction to realize efficient and large-scale phenotype detection.