80 results about "Crop sample" patented technology

The invention discloses a non-destructive detection device and method of internal information of crops based on the spectrum technology. The method comprises the following steps: a knowledge database is established and the knowledge database related to all crop samples is established for a crop needing to be measured; image data and spectrum data of a crop canopy are acquired into a computer; the acquired image data is preprocessed by the computer and an image feature same as a training sample is extracted; the computer carries out waveband feature extraction same as the training sample on the acquired spectrum data; the computer carries out fusion on the extracted image feature, the waveband feature and the knowledge database, detection test is carried out by utilizing a test sample, the nitrogen and water contents of the current tested crop sample are given, and a detection result is displayed by the computer. According to the invention, which growth period the crop is in and what the nitrogen content is can be judged automatically by the spectrum information, and more convenience in use is brought to a user.

The invention discloses crop volatile matter detection by a metal oxide sensor array. The method comprises the following steps: 1) putting a crop sample in a sampling device, allowing the sample to stand for 30-60 minutes, when top air is balanced, introducing the headspace gas in the container into a sensor array reaction chamber by a sampling pump, allowing the sensor to react with the gas so as to obtain a corresponding response signal which is converted into digits and is inputted into a computer by an acquisition card; 2) performing feature selection and feature extraction of a feature matrix of electronic nose raw data; 3) establishing a mathematical model of the relationship between sensor response signals and different degrees of sample diseases by neural network with the raw dataand the treated data being used as the sensor response signals respectively. The invention has simple operation, greatly improved reliability and repeatability, and increased detection efficiency.

A grain quality sensor comprising a lens, a filter, a photosite array, an illumination source, and an electronics module, wherein the illumination source directs light containing a known set of wavelengths onto a crop sample, wherein the lens picks up light reflected by the crop sample and directs it into the filter, which allows light to pass into different parts of the photosite array such that certain locations on the photosite array only get certain frequencies of the reflected light, wherein the electronics module is electrically connected to the photosite array and capable of determining which parts of the photosite array received light and what frequency the light received was, wherein the electronics module can analyze the optical data received by the photosite array, and wherein the analysis of the optical data is used to determine the composition of different parts of the crop sample.

The invention relates to the intelligent decision, and discloses a crop yield prediction method, which comprises the following steps of obtaining th ecrop sample data, the crop sample data comprisingplanting characteristic data representing crops and actual yield values of the crops; according to the crop sample data, obtaining a yield prediction model through training; acquiring the target cropdata; and according to the yield prediction model, predicting the target crop data, and determining a predicted yield corresponding to the target crop data. The invention further provides a crop yieldprediction device and a computer readable storage medium. The crop yield of different lands in the same region can be accurately predicted based on the historical data, and quick compensation can berealized.

A crop quality sensor, comprising an illumination source, an imaging device, and a processor executing application software. The illumination source is shone onto a crop sample, and an image is taken with the imaging device of the illuminated crop sample. The software executing on the processor is used to analyze the image to identify the outlines of individual kernels and to identify which of those outlines contain a specular highlight, indicative that the kernel is whole and unbroken, while the absence of such a specular highlight is indicative of a broken kernel.

The invention discloses a positive plasmid molecule pBI121-Screening and application thereof and relates to the technical field of safety supervision and screening detection of transgenic crops in the transgenic safety field. The base sequence of the pBI121-Screening is as shown in SEQ NO.1. serving as both a positive contrast and a quality control sample, pBI121-Screening disclosed by the invention can be respectively used in general PCR and real-time fluorescence PCR screening detection of transgenic components of six transgenic crop samples, namely paddy rice, rapes, soybeans, corns, cotton and wheat. The pBI121-Screening disclosed by the invention can be used for solving the technical problem that the lack of a positive contrast or a standard sample in the screening detection of the transgenic crops, and prevents the problem that a plurality of positive contrasts are arranged for a plurality of detection targets in the screening detection, thus reducing the labor cost and economic cost for preparing the plurality of positive contrasts.

The invention relates to a method for comprehensively testing the fragrant components of crops, which belongs to the technical field of analysis and detection. The invention aims to solve a technical problem of providing a method for comprehensively testing the fragrant components of crops. Fragrant components in wheat, sorghum and maize can be comprehensively and accurately tested with the method. The method for comprehensively testing the fragrant components of crops comprises the following steps of: respectively pre-processing crop samples and capturing the fragrant components of crops with a headspace-solid phase micro-extraction method and a simultaneous distillation and extraction method, then respectively carrying out qualitative analysis through gas-phase color spectrum-mass spectrum and combining analyzed results to obtain the test results of the fragrant components of the crops. The crops refer to the wheat, the sorghum and the maize.

The present invention discloses a device and a method for measuring crop biomass. The crop biomass is measured by applying frequency sweep electric field to a to-be-measured crop sample in a parallel plate capacitor for measuring the dielectric property of the crop sample at a certain temperature and humidity, and by establishing a correlative model of dielectric constant, attenuation coefficient and the crop biomass. The device and the method provided in the invention can rapidly measure crop biomass per plant or of a plurality of plants without reaping the crop. The device is simple and portable, and is suitable for field application, and remarkably saves manpower, material resources and time, and furthermore, the device can be reformed into an on-line continuous monitoring system of the crop biomass for carrying out a continuous monitoring of field crop biomass.

A crop quality sensor, comprising an illumination source, an imaging device, and a processor executing application software. The illumination source is shone onto a crop sample, and an image is taken with the imaging device of the illuminated crop sample. The software executing on the processor is used to analyze the image to identify the outlines of individual kernels and to identify which of those outlines contain a specular highlight, indicative that the kernel is whole and unbroken, while the absence of such a specular highlight is indicative of a broken kernel.

The invention provides a high-throughput calculation method for crop plant height. The method comprises the following steps: acquiring a color image, an infrared image and a depth image of a crop sample group; and carrying out image registration on the color image and the depth image, and constructing a point cloud image with color information; extracting crop canopy three-dimensional point cloudaccording to the point cloud image with the color information; extracting a canopy image corresponding to each crop in the crop sample group according to the crop canopy three-dimensional point cloud,and calculating the plant height of each crop through a high-throughput calculation method. According to the embodiment of the invention, the plant height of each pot of crops in the group crops canbe quickly and accurately calculated; Compared with a traditional plant height measuring method which utilizes a ruler, a handheld laser range finder and other devices to manually measure the verticallength from the highest point of a canopy to the root of a plant, the high-throughput calculation of the plant height of the crop is realized, and the method is quicker, simpler and more convenient.Compared with a network camera remote phenotype measurement method, the accuracy of crop plant height measurement is improved.

The invention relates to an agricultural product pesticide residue detection device. The agricultural product pesticide residue detection device comprises a main body, and also comprises a feeding mechanism, a stirring mechanism, a grinding mechanism and a detection module, wherein the grinding mechanism comprises a first shell, a pressure block, a support, a reinforcing casing pipe, a lifting rod, a spring, a first driving assembly, a second driving assembly, a guiding wheel, a cutter blade, a first rotating shaft and a first gear, the feeding mechanism comprises a sleeve, a roller, a conveying hole, a third driving assembly, a rotating connector, a first conveying pipe and at least two feeding pipes. In the agricultural pesticide residue detection device, a crop sample can be minced by virtue of the grinding mechanism, so that the pesticide absorbed by the agricultural product can be effectively precipitated out, the accuracy for detecting the pesticide residual amount of the agricultural product can be improved, different extraction agents can be selected by virtue of the feeding mechanism, then the extraction agents are fed into the stirring mechanism, and the extraction agentscan be automatically fed, so that the detection efficiency of the detection device can be improved.

The invention discloses a quick non-destructive judging method for unsound grains of a single kernel crop. The method comprises the following steps: S1, collecting a single kernel crop sample, detecting unsound grain condition of each single kernel crop and establishing a class information matrix; S2, collecting a near infrared spectrum of single grains of the single kernel crop; S3, constructinga near infrared judging and analyzing model of unsound grains of the single kernel crop; and S4, differentiating normal single kernel crop and unsound grain single kernel crop by means of the established model. The method has the advantages of being objective and accurate in detection result, free of damage to the sample in the detection process, quick and simple, and can judge the unsound grainsof the single kernel crop efficiently in a through manner.

The invention discloses a cultivated crop type extraction method and system, a storage medium and electronic equipment. The invention discloses a cultivated land crop type extraction method. The method comprises the following steps: obtaining cultivated land parcel vector data of a target area; Constructing a radar time sequence image by using the obtained original radar data; Superposing the radar time sequence image and the cultivated land parcel vector to form radar time sequence characteristics; Obtaining sample data of a plurality of crops of the cultivated land parcel in the target area,marking the type of the crops, and generating a crop sample of the cultivated land parcel in the target area; Utilizing the deep learning network model to train the radar time sequence characteristics of the samples collected in the target area to obtain a crop type model; Inputting radar time sequence images and vector data of cultivated land parcels, and performing machine extraction on crop types of the input data by utilizing a crop type model; By combining the advantages of radar image all-weather monitoring and optical image ground object boundary clearness, the defect of single observation of the radar image all-weather monitoring and the optical image ground object boundary clearness is overcome, large-area crop type extraction in cloudy and rainy areas is achieved, and managementof various crops is facilitated.

The invention provides a crop classification method based on deep learning and a system thereof. The method comprises the steps of dividing a to-be-classified operation area into a plurality of sub operation areas, and acquiring a multi-temporal multi-characteristic data set of each sub operation area; according to the multi-temporal multi-characteristic data set and crop sample data in the growthperiod of the to-be-classified crops in the to-be-classified operation area, acquiring a multi-temporal multi-characteristic data sequence of each pixel in a random sub operation area; according to the multi-temporal multi-characteristic data sequence of each pixel, acquiring the growth characteristic graph of each pixel; identifying the growth characteristic graph of each pixel through a trainedneural network model, and acquiring the classification result of the to-be-classified crops. According to the method and the system of the invention, a crop classification problem is converted to a problem of identifying a time sequence growth characteristic graph; through a deep learning method, the time sequence which is irregular in a main grain crop main output area scale can be normally usedin a normal-state data environment, thereby improving classification precision.

The invention discloses a remote sensing measurement method for level of crops pollution stress, which belongs to the technical field of agricultural science. The method comprises: a) selecting a set amount of polluted-crops sample data, wherein each group of sample data comprises a set amount of input parameters and a set amount of output parameters comprising remote sensing parameters; b) obtaining a neural network structure comprising more than one fuzzy rule through fuzzy neural network analysis, judging whether to modify a current neural network structure at the same time according to set standards each time one group of sample data is input and modifying if so; and c) obtaining a corresponding output value through the input parameters of crops according to a final neural network structure and determining the level of pollution stress in a set association way according to the output value. The method can be used for the remote sensing measurement of the level of crops pollution stress, in particular the remote sensing monitoring of the level of heavy metal pollution stress.

A grain quality sensor comprising a photosite array, an illumination source, a filter, and an electronics module, wherein the illumination source directs light onto a crop sample, wherein the filter limits passage of light into different parts of the photosite array such that certain locations on the photosite array only receive certain wavelengths of light reflected or fluoresced by the crop sample, wherein an electronics module is electrically connected to the photosite array and capable of determining which parts of the photosite array received light and the wavelengths of the light received, wherein the electronics module can analyze the optical data received by the photosite array, and wherein the analysis of the optical data is used to determine the composition of the crop sample.

A system for presenting a crop sample to a crop property sensor is in particular suited for a harvesting machine and comprises a bypass line branching off from a crop feeding assembly, a conveyor for feeding the branched-off crop through the bypass line without damaging the crop, and a crop property sensor for sensing one or more properties of the crop in the bypass line. The bypass line is upwardly angled or extends vertical, such that the conveyor elevates the material from the crop guiding channel to the crop property sensor.

The invention discloses a cumulative environmental risk early warning method for soil heavy metal pollution, and the method comprises the following steps: setting a sample collection point, and collecting a soil sample and a crop sample; analyzing the content of heavy metal pollutants in the soil sample and the crop sample; calculating the heavy metal content in the soil sample, comparing the heavy metal content with a preset value, and judging the risk level of the heavy metal content; calculating the heavy metal content in the agricultural product sample, comparing the heavy metal content with a preset value, and judging the risk level of the heavy metal content; analyzing the heavy metal contents of the same sample collection point in different years, and calculating the soil heavy metal pollution accumulation rate; according to the heavy metal content in the soil sample, the heavy metal content in the agricultural product sample and the soil heavy metal pollution accumulation rate,calculating the accumulative environmental risk early warning level of the soil heavy metal pollution in the investigation area, and taking corresponding treatment measures to improve the damage of the heavy metal pollution to the environment.

The invention discloses a crop classification method based on time sequence NDVI and LST. The method comprises the following steps: 1) obtaining remote sensing image data containing a red light band,a near infrared band and a thermal infrared band, constructing a remote sensing image time sequence covering a crop growth period, and calculating to obtain an NDVI time sequence and an LST time sequence; (2) in order to enhance the difference between different crops, for each LST in step 1), firstly calculating LST mean vaue of LST, and then using the mean value to adjust (the formula is ALST(i,j)=LST(i,j)-LST mean, with i, j being the row number and column number of each pixel respectively, and finally supermisoing all the adjusted ALSTs in chronological order to form an ALST time series.; 3) obtaining crop sample data through a field survey or historical map, and 4) taking the NDVI time sequence, the ALST time sequence and the sample data as input, and classifying the crops in the research area by adopting a random forest classifier to form a crop classification result map.

The invention belongs to the field of agricultural remote sensing, and relates to a provincial-level range plot scale rapid data assimilation yield prediction method based on set sampling, which specifically comprises the steps of obtaining a provincial-level crop spatial distribution map based on remote sensing data of a time sequence and crop sample points; obtaining a posterior sample set of the key parameters based on a site LAI and yield calibration WOFOST model, inputting the posterior sample set of the key parameters and the meteorological data of the whole growth period into the WOFOSTmodel, and generating an LAI trajectory set and a per unit yield set in the growth period corresponding to the site; inverting the reflectivity data into LAI based on a PROSAIL model, and obtaining an LAI track range in a growth period; and according to the LAI track range, carrying out inverse distance weighting on the unit yield corresponding to each obtained LAI track, wherein the yield obtained by weighted summation is the unit yield of rapid assimilation. According to the method, large-area crop unit yield prediction in a provincial-level range can be carried out at a high-resolution land parcel scale of 10 meters, the assimilation speed is high, and the efficiency is high.

The invention discloses a crop classification method based on a sentinel No.1 RVI time sequence. The method comprises the following steps: 1) obtaining VV-VH polarization data in an IW mode of a sentinel No.1 satellite, and constructing a remote sensing image time sequence covering the growth cycle of crops; 2) constructing an RVI index (the formula is RVI = sigma VH / sigma VV) based on the VV-VH polarization data of each period, and then performing integration to form an RVI time sequence; 3) obtaining crop sample data through field investigation or historical maps, and 4) classifying crops ina research area by taking the RVI time sequence and the sample data as input and adopting a random forest classifier to form a crop classification result map.

The invention discloses a farmland soil health evaluation method, which comprises the following steps: selecting a soil evaluation system; Screening soil health indicators in the soil evaluation system by a Delphi method to obtain screened soil health indicators; The original data of the selected soil health indicators were standardized, and the standardized data were analyzed by principal component analysis, and the indicators contained in the principal factors that meet the preset conditions were taken as the second soil health evaluation indicators. Performing stepwise regression analysis on the second soil health evaluation index to obtain a third soil health evaluation index, and constructing a soil health evaluation data set according to the third soil health evaluation index; obtaining soil samples and crop samples, and measuring the index value of the third soil health evaluation index; According to the index value of the third soil health evaluation index, the soil health index and the soil health grade of the soil sample are obtained by a preset scoring rule.

The invention discloses a crop identification method based on multi-spectral satellite images, comprising the following steps: S1, collecting crop samples; S2, acquiring multispectral satellite imagedata of the crop sample; S3, determining a pixel corresponding to the crop sample on the multi-spectral satellite image data through the collection position of the crop sample; S4, taking the temporalspectrum information of the pixel and the crop type of the crop sample as inputs to train a machine learning model; S5, classifying other sampling areas by the trained machine learning model. The invention takes the temporal spectrum information of the pixel as the input of the training machine learning model, Not only greatly expands the amount of crop spectral information, solves the problem ofinsufficient crop spectral information at a single time, but also identifies crops from the full cycle of crop growth spectral information, which is more accurate than the identification at a singletime, so as to improve the efficiency of crop identification.

The invention relates to a time sequence remote sensing image crop classification method combining a TWDTW algorithm and a fuzzy set. The method comprises the following steps: S1, obtaining time sequence remote sensing image data, plot data and crop sample data of a to-be-detected region; S2, preprocessing the time sequence remote sensing image data; S3, constructing an NDVI time sequence data set; S4, respectively constructing standard NDVI time sequence data of different crops and an NDVI time sequence data set of the plot units; S5, constructing a TWDTW algorithm of a non-equal-length time sequence, and obtaining a minimum cumulative distance feature matched with the similarities of different crops; S6, on the basis of the NDVI time sequence data set of the plot unit, phenological characteristics of different crop growth season lengths are calculated; and S7, on the basis of the minimum cumulative distance feature and the growth season length feature, constructing Gaussian membership functions of different crops, and on the basis of a fuzzy set classification rule, realizing refined crop classification on the plot scale. According to the invention, refined classification of crops on the plot scale is realized.

The invention provides a DELFIA-based pesticide residue detection device and method. The device comprises an analyzer having a centrifugal disk and a fluorescence detecting mechanism, and a microfluidic chip fixedly arranged on the centrifugal disk and acquiring a detection liquid by a centrifugal effect, wherein a sample hole, a filter hole, a first reaction hole, a second reaction hole and a waste liquid hole are successively arranged in the microfluidic chip from the inside to the outside in the radial direction of the centrifugal disk; the sample hole, the filter hole, the first reaction hole, the second reaction hole and the waste liquid hole communicate successively through micro-channels; sugar membranes of different thicknesses are disposed at the openings of the micro-channels, and open the micro-channels according to different centrifugal rotation speeds; the sample hole is used for containing a sample solution of a pesticide residue detection liquid, and the sample solutionis evenly stirred by centrifugal rotation. Thus, the harmful component content in a crop sample is detected. The device has few manual operation processes, is easy to operate, and can rapidly detect the pesticide residue sample solution.

The invention discloses a rapid atomic fluorescence spectroscopy heavy metal and trace element detection method based on laser ablation plume. The method comprises the following steps: cleaning a crop sample by using deionized water, and placing the cleaned sample on a sample table; forming 355nm laser by a first path of laser after passing through a three frequency generator, and after a light path is raised, focusing the laser at a position above the sample table to beat the surface of the sample to form the high-density plume; beating the high-density plume from the upward side of the sample by a second path of laser so as to enable atoms to be motivated; acquiring an atomic spectrum of the crop sample, and selecting four spectral lines which are self-absorptive, self-reversal and highest in strength from characteristic spectral lines according to to-be-tested heavy metal and trace elements; establishing a plurality of kinds of multiple regression models by using a sample reference value as output and the spectral line strength as input, selecting four optimal models from multiple regression models, and establishing a comprehensive model; and acquiring four spectral line strength values, inputting the four spectral line strength values into the comprehensive model aiming at the to-be-tested crop sample, and computing the contents of the heavy metal and trace elements of a crop.

The invention discloses a crop growth condition detection method, system and device and a storage medium. The method comprises the following steps of acquiring a first thermal imaging picture and growth environment data of a to-be-detected crop, determining growth stage information and first leaf surface temperature of the to-be-detected crop according to the first thermal imaging picture, determining a first water stress index of the to-be-detected crop according to the first leaf surface temperature and the growth environment data, and further determining the growth condition of the to-be-detected crop according to the growth stage information and the first water stress index. Crop samples do not need to be collected, crop leaves and other parts do not need to be contacted, related data can be rapidly obtained through an infrared thermal imaging technology and a sensor technology, and the efficiency of crop growth condition detection is improved; the growth condition of the crop is detected by determining the water stress index of the crop, so that the influence of environment and shooting conditions is avoided, and the detection accuracy of the growth condition of the crop is improved. The method can be widely applied to the technical field of agricultural condition monitoring.

The invention provides a multiple DPO-PCR primer combination for detecting transgenic maize MON810 and MIR604. Sequences of the primer combination are respectively shown in Seq ID NO. 1-4. The invention also provides a multiple DPO-PCR method for detecting transgenic maize MON810 and MIR604 on the basis of the primer combination. Qualitative detection of transgenic crop samples can be implemented. An experiment shows that the method is good in specificity and high in flux, and is speedy; the detecting efficiency is improved; and the method is an effective method for detecting the transgenic maize MON810 and MIR604.

The invention discloses an evaluating method for a passivation effect of a nanometer modifier to heavy metal in rhizosphere environment. The evaluating method comprises the following steps of preparing contaminated soil; preparing nanometer modifier remediated soil; evenly distributing and feeding the contaminated soil and the nanometer modifier remediated soil into all areas of root boxes to serve as a control group and an experiment group and sowing the same crop into root growth chambers of all the root boxes to be cultured into crop samples; sampling and detecting; destructively collecting crop samples and soil samples in the root boxes of the experiment group and the control group at certain intervals; judging whether the nanometer modifier has a passivation effect on heavy metal in the rhizosphere environment according to comparative results of the total heavy metal amount of all the samples. The evaluating method disclosed by the invention can limit crop roots in a certain area through the root boxes, achieves the purpose of more accurately separating soil samples with different distances to the root chambers, can detect the heavy metal content, accordingly accurately evaluates the passivation effect of the nanometer modifier to the heavy metal in the rhizosphere environment and provides theoretical guidance for actual production.