143 results about "Seed sample" patented technology

A system, method, and computer program product for separating a seed aliquot from a bulk sample and preparing the seed aliquot for downstream research purposes is provided. The system embodiments may comprise a counter device, which is configured to receive at least a portion of the bulk sample and to separate the seed aliquot from the portion of the bulk sample, and a packaging device, which is configured to receive the separated seed aliquot and to dispense the aliquot into an aliquot container. Other embodiments may include a conveyor system for advancing the bulk sample and / or seed aliquot along a conveyance path that may be determined from a research protocol received from an input source. Various embodiments of the present invention may maintain an identity of the seed aliquot as it is advanced along the conveyance path and may selectively subject the seed aliquot to a variety of processing steps in accordance with the research protocol with little or no manual control or supervision.

The invention discloses a hyper-spectral reflection image collecting system and a corn seed purity nondestructive detection method based on the same. The method comprises the steps that an image of each wave band is extracted as a sub-image of a corresponding hyper-spectral reflection image based on hyper-spectral reflection images with the quantity of M of a collected corn seed sample under wave bands with the quantity of M; a corresponding corn seed outline image and shape characteristic parameters with the quantity of 12M under the M wave bands are extracted by utilizing an image segmentation method as characteristic parameters for judging an affiliated category of the corresponding corn seed sample, a preset detection model is inputted to perform the detection, and a detection result on the affiliated purity category of the corresponding corn seed sample is acquired. Due to the adoption of the hyper-spectral reflection image collecting system and the corn seed purity nondestructive detection method based on the same, defects in the prior art that parameters at the wave band with a loud noise are difficult to extract, the accuracy is poor, the reliability is low and the like can be overcome, and the advantages such as simplicity in operation, good noise resistance, good accuracy, high reliability and good real-time performance can be realized.

A system and method for treating a least a portion of an initial seed sample to create one or more treated seed samples is provided. In various embodiments, one or more seed treatment apparatus components are in communication with one or more controllers such that seed samples treated by a seed treater may be treated at least in part based on one or more instruction sets that may be associated with one or more research plans. As a result, the method and system are configured to accurately, consistently, and predictably treat seed samples, and to adapt and change the treatment of seed samples based on changing research plans.

A system and method for coordinating the production, processing, and utilization of seed is provided. In various embodiments, the method and system aggregate seed inventory data, identify seed requests, and query the seed requests against the seed supply information. If the query results in a match a seed source can be automatically identified. Further, identified seed can be automatically allocated to the identified seed request from the identified seed source.

The invention discloses a voice data processing method and a voice data processing device. The voice data processing method comprises the following steps: acquiring the I-Vector of each of a plurality of voice samples, and determining a target seed sample in the plurality of voice samples; respectively calculating the cosine distances between the I-Vector of the target seed sample and the I-Vectors of the target residual voice samples, wherein the target residual voice samples are the voice samples besides the target seed sample in the plurality of voice samples; and at least filtering from the plurality of voice samples or the target residual voice samples according to the cosine distances to obtain a target voice sample, wherein the cosine distance between the I-Vector of the target voice sample and the I-Vector of the target seed sample is higher than a first preset threshold value. With the adoption of the method and the device, the technical problem that in the relevant technologies, cleansing can not be carried out on voice data by adopting a manual annotation method, so that the voice data cleansing efficiency is low is solved.

An automated seed sampler includes a sampling station, a sampler for removing material from a seed in the sampling station; a seed conveyer for conveying the seed from the sampling station to a compartment in a seed tray, and a conveyor for conveying the material removed from the seed to a corresponding compartment in a sample tray. And a method for bulking up a quantity of plant seed having a desired characteristic generally includes testing each seed for the desired characteristic, cultivating plants only from seeds which tested positive for the desired characteristic, recovering seed from the cultivated plants, and repeating the processes of testing, cultivating, and recovering seed for one or multiple generations.

An apparatus and method for treating a least a portion of one or more initial seed samples to create one or more treated seed samples for use in research applications is provided. In various embodiments, the apparatus and method utilize two or more seed treaters operating in parallel to increase throughput for treating a portion of one or more initial seed samples. The apparatus and method may also include a metering station configured to distribute at least a portion of the one or more initial seed samples to the two or more seed treaters. As a result, the apparatus and method are configured to efficiently and accurately treat seed samples.

The invention discloses a crop seed variety authenticity identification method based on near infrared spectrum. The method comprises the following steps of 1, acquiring near infrared spectrum data of crop seed samples, and preprocessing the near infrared spectrum data which is taken as a training sample; 2, selecting the needed spectrum data from the preprocessed near infrared spectrum data, and establishing a qualitative analysis model of the crop seeds by a feature extraction method and a modeling method; and 3, carrying out variety authenticity identification on the spectrum of the crop seeds which are taken as test samples and about to be identified. The crop seed variety authenticity identification method is based on the near infrared spectrum, the general near infrared spectrum qualitative analysis model can be established by a series of operation such as spectrum preprocessing, feature extraction, modeling and identification, and the method is capable of rapidly and accurately identifying the crop seed variety authenticity.

The invention discloses an automatic seed sampling machine. The automatic seed sampling machine comprises an automatic seed arranger, a seed conveying device, a machine vision working position, a seed gripper group, a seed gripper group moving device, a laser cutting device, a sample disc moving device, a sample disc conveying and charging-discharging device and a seed disc conveying device, wherein the automatic seed arranger is arranged at the left side of the seed conveying device in a connecting manner; the machine vision working position is arranged at one end, close to the automatic seed arranger, of the seed conveying device; the seed gripper group moving device is arranged above the seed conveying device and is provided with the seed gripper group; the laser cutting device is arranged at one side of the seed conveying device in the moving range of the seed gripper group; and the sample disc moving device, the sample disc conveying and charging-discharging device and the seed disc conveying device are arranged at the downstream positions of the seed conveying device in sequence and in the connecting manner. The automatic seed sampling machine disclosed by the invention has the advantages of wide seed adaptability, slight damage to seeds and relatively high working efficiency.

The invention discloses a drying process for green bean seeds which dries green bean seeds by applying an enclosed steam compression heat pump drying equipment with an auxiliary condenser. The drying process for green bean seeds includes the following steps of inputting an initial moisture content and a target moisture content of the green beans; determining cycling times for drying according to values of the initial moisture content, the target moisture content and the differential value between the initial moisture content and the target moisture content; setting process conditions, such as moisture content, drying temperature, wind speed performance, continuous drying time and tempering drying time and the like; shutting down the compressor and entering into the tempering drying stage after the continuous drying stage, monitoring the moisture content of the green beans in real time through a seed sample weighing system inside a drying oven, adjusting the tempering drying time according to the differential value between the current green bean seed moisture content and the target moisture content, and stopping the tempering drying and ending the drying process of green beans when the seed moisture content reaches the target value. The drying process for green bean seeds has the advantages of environmental protection, low consumption, high degree of automation, wide scope of application and good drying quality.

The invention discloses a method for identifying transgenic rice and non-transgenic rice based on NIR (Near Infrared Spectrum), which comprises the following steps of: (1) transmitting the NIR to the rice seed samples, and collecting the diffuse reflection spectrum information of all the rice samples; (2) respectively preprocessing the diffuse reflection spectrum information for all the rice seed samples, extracting the spectrum information in characteristic spectrum regions after preprocessing through a principal component analysis method, selecting principal components, and acquiring the scores of the principal components; (3) building a model by the principal component scores corresponding to the rice seed sample spectrum information as an input and the rice seed type set values corresponding to the rice seed samples as an output; and (4) acquiring the scores of the principal components of the spectrum of rice seeds to be detected, taking the scores into the model in the step (3), and obtaining the types of the rice seeds to be detected. The invention is high in identification precision, simple to operate, low in cost, and is capable of realizing quickly and losslessly identifying the transgenic rice and the non-transgenic rice.

The invention provides a nondestructive testing method of the content of fatty acid in peony seeds. The method comprises the steps that first, ripe peony seeds are collected and are dried at 60 DEG C to serve as samples; second, the peony seed samples are respectively placed into an automatic rotating cup of a Fourier near-infrared scanner, and are scanned for 64 times within the range from 4000 cm<-1> to 10000 cm <-1>, each sample is repeatedly loaded for three times, and the average value is obtained; third, the content, namely the chemical value, of fatty acid in each sample is determined with a gas chromatography method, and then, a mathematical model is built according to spectral values and the chemical values; fourth, unknown peony seed samples are tested with the model. With the nondestructive testing method of the content of fatty acid in peony seeds, the content of palmitic acid, oleic acid, linoleic acid and linolenic acid in peony seeds can be rapidly tested in a nondestructive mode; operation is simple and convenient, and because model building is carried out on the basis of the chemical values and the spectral values of various peony seeds, the reliable mathematical model is obtained; a prediction result is high in accuracy, the accuracy of model prediction is close to the true value of the background technology.

The invention provides a dynamic multipoint grass variety identification and authentication method based on terahertz time domain spectroscopy. The method comprises the following steps: preparing a standard grass seed sample; respectively detecting the standard grass seed sample and dry nitrogen by using a terahertz time domain spectroscopy device to respectively obtain a terahertz pulse time domain waveform sum of the standard grass seed sample and the dry nitrogen, taking the terahertz pulse time domain waveform of the dry nitrogen as a reference signal, and taking the terahertz pulse time domain waveform of the standard grass seed sample as a sample signal; carrying out data processing on the terahertz pulse time domain waveform of the standard grass seed sample and the terahertz pulse time domain waveform of the dry nitrogen to construct a standard fingerprint spectrum library; and analyzing, calculating and establishing an identification database of unknown grass varieties according to optical parameters of the standard fingerprint spectrum library in combination with a mathematical statistical method, thereby carrying out qualitative analysis and recognition on unknown grass samples. By adoption of the method, the grass varieties can be effectively recognized and detected, the database can also be established for the unknown grass samples, and the unknown grass samples can be quickly distinguished and identified in batches.

The invention discloses an identification method for the authenticity of the varieties of seeds on the basis of near-infrared quantitative analysis. The method comprises the steps of acquiring near-infrared spectral data of a seed sample and determining a modeling set and testing sets; carrying out pretreatment, partial-least-square feature extraction and linear-identification feature extraction on the modeling set and the testing sets in sequence; selecting a spectral correction set from the modeling set and the testing sets, calculating a transformation matrix between the modeling set and the testing sets; applying the transformation matrix to the remaining testing sets; establishing a quantitative analysis model by adopting a support vector machine method; and carrying out identification on the authenticity of the varieties of the remaining testing sets by utilizing the quantitative analysis model. The identification method disclosed by the invention is established on the basis of near-infrared quantitative analysis and has the advantages that due to a series of operations of spectral pretreatment, feature extraction, correction of the testing sets, modeling and identification and the like, a more stable near-infrared spectral analysis model is established, and the problem of model applicability caused by long-time spectral shift of the same instrument can be solved, so that the identification result is accurate.

The invention provides a method and kit for detecting xanthomonas oryzae pv. oryzae by use of digital-PCR (polymerase chain reaction). The provided kit contains a pair of specific primer pairs and a probe with fluorescent marks at two ends, wherein the nucleotide sequence of the specific primer pairs is shown in SEQ ID NO.1-2, and the nucleotide sequence of the probe is shown in SEQ ID NO.3. The primers are designed on basis of rhs family genes of xanthomonas oryzae pv. oryzae, and the method for detecting the xanthomonas oryzae pv. oryzae is good in specificity, high in sensitivity and accuracy of detection results and suitable for detection of germs in rice seed samples and has important application value.

The invention discloses a method for identification of purity of a two-line hybrid rice seed. The method comprises the following steps of extracting a genomic DNA of a seed needing to be detected or a plant sample growing from the seed, designing primers according to functional single nucleotide polymorphic sites of a genic male-sterile gene and carrying out PCR amplification, carrying out characteristic analysis of the PCR amplification products to determine a sample genotype and calculating purity of the seed sample. The method utilizes the functional single nucleotide polymorphic sites of the functional gene to develop a molecular marker. Any parents subjected to cross combination necessarily have polymorphism and in hybrid seed purity identification, analysis of two-line cross combination adopting the two-line hybrid rice seed as a female parent can be directly carried out without research on parents. Through conventional indoor tests, hybrid rice seed purity can be simply, fast, economically and accurately determined.

The invention discloses a method for rapidly detecting the germination rate of vegetable seeds. Its steps are as follows: 1) Place the vegetable seed sample in an airtight container and let it stand for 30-60 minutes; 2) After the headspace gas reaches equilibrium, the headspace sample is introduced into the sensor array reaction chamber for sampling, and the sampling time is 60-90s , the sensor array reacts with the headspace gas to obtain a response signal; 3) Extract the 60s electronic nose signal as the original data, perform feature selection and feature value extraction on the feature matrix of the original data; use principal component analysis and linear discriminant analysis to evaluate the seeds Germination rate grade; 4) Using the original data and the above-mentioned processed data as eigenvalues, use BP neural network and support vector machine recognition mode to establish the correlation between the sensor response signal and the seed germination rate, and then evaluate the seed germination rate. The invention has the advantages of simple operation, greatly improved reliability and repeatability, and improved detection efficiency.

The invention relates to an electrospray extraction ionization-mass spectrum (EESI-MS) rapid detection method for alkaloid in lotus seeds, belongs to the technical field of analysis, and particularly relates to a rapid determination method for alkaloid in lotus seeds. The method comprises the following steps: processing a sample, processing a standard product, detecting by EESI-MS, drawing a standard curve, and calculating the concentration of alkaloid in a sample. The method provided by the invention is rapid and accurate without complex sample pretreatment, not only can be used for rapidly identifying the alkaloid in a complex matrix sample, but also can effectively differentiate lotus seed samples with different aging degrees by combining with chemometrics.

The invention discloses sedative and hypnotic active saponin components of a spina date seed, as well as separation and purification method and application of the sedative and hypnotic active saponin components, and belongs to the field of separation of natural products. The invention designs a new and relatively safe extracting route without introducing obviously toxic solvents and agents. The separation and purification method is characterized by comprising the following steps of: extracting a spina date seed sedative and hypnotic active saponin sample from a spina date seed sample by the alcohol extraction method and n-butanol extraction method; and then separating spina date seed sedative and hypnotic active saponin components I and II from the saponin sample by the column chromatography and thin-layer chromatography. The two spina date seed saponin components separated from the spina date seed show obvious hypnotic activity; and the natural sedative and hypnotic active components extracted and separated from traditional Chinese medicine can be used for solving the problems of multiple side effects and addiction of chemical synthetic drugs and is beneficial to human health and contributed to all the society.

The invention discloses a rapid lossless testing method based on a near infrared spectroscopy technology for a paddy rice seed germination percentage. The method includes: S1) collecting paddy rice seed samples and ageing the paddy rice seeds; S2) collecting near infrared spectroscopy data of the paddy rice seeds; S3) subjecting the paddy rice seeds to germination tests; S4) selecting wave bands of the near infrared spectroscopy data of the paddy rice seeds and pre-processing; and S5) building a paddy rice seed germination percentage model based on the near infrared spectroscopy technology, and examining. By collecting the near infrared spectroscopy data of the paddy rice seeds with different ageing time periods, selecting the wave bands of the near infrared spectroscopy data and pre-processing, the method builds the paddy rice seed germination percentage model based on the near infrared spectroscopy by adopting a partial least squares method. After testing, the model is accurate, reliable and practical. The method can achieve rapid, lossless and accurate testing of the paddy rice seed germination percentage.

The invention discloses a genetically modified cotton variety recognition method. The method comprises the steps that within the wavelength range of 150 micrometers to 3 millimeters, terahertz spectral information of a genetically modified cotton seed sample is collected; the terahertz spectral information of the genetically modified cotton seed sample is combined with dynamic self-adapting particle swarm optimization, the dynamic self-adapting particle swarm optimization is utilized for optimizing and training a support vector, and a recognition model of s support vector machine is built; then, the built recognition model is utilized for conducting category judgment on a sample to be tested to obtain a recognition result. Terahertz spectrum detection is adopted, and by means of the support vector machine based on the dynamic self-adapting particle swarm optimization, the recognition model of genetically modified cotton seed is built. The recognition rate of the model for the genetically modified cotton seed categories reaches 97.3%, a foundation is laid for application of a qualitative analysis model in actual sample detection, and the precise, rapid, simple, convenient and lossless method is provided for detecting genetically modified products.

The invention discloses a method for identifying different transgenic rice. The method comprises the following steps of (1) emitting near infrared spectrums to rice seed samples, and collecting diffuse reflection spectral information of all the rice seed samples; (2) respectively preprocessing diffuse reflection spectral information of all the rice seed samples, extracting spectral characteristic information of a characteristic spectrum section subjected to the preprocessing through a principal component analysis method, and selecting principal components to obtain scores of the principal components; (3) establishing a model with the scores of the principal components corresponding to the spectral information of all the rice seed samples serving as input and transgenic type set values corresponding to the rice seed samples as output; and (4) obtaining scores of the principal components of spectrums of rice seeds to be measured, and putting the scores of the principal components into the model obtained in the step (3) to obtain transgenic types of the rice seeds. Trans-protein-gene rice and trans-controlling-gene rice are utilized in the method. The method is simple in operation, high in identification accuracy degree and capable of achieving fast and nondestructive identification of different transgenic rice.

A method, apparatus, and system for coating, singulating, aligning, ablating, and indexing a number of seeds for testing is described. The apparatus has a carrier designed to hold seeds in a predetermined alignment for ablating a seed sample from a seed portion, the seed portions are indexed to the seed samples. The carrier is then located on the apparatus which delivers the seed samples from the carrier to a collector for laboratory testing.

An apparatus and method for treating a least a portion of one or more initial seed samples to create one or more treated seed samples for use in research applications is provided. In various embodiments, the apparatus and method utilize two or more seed treaters operating in parallel to increase throughput for treating a portion of one or more initial seed samples. The apparatus and method may also include a metering station configured to distribute at least a portion of the one or more initial seed samples to the two or more seed treaters. As a result, the apparatus and method are configured to efficiently and accurately treat seed samples.

The invention discloses a deep learning model-oriented dynamic test method. The method comprises the following steps: S1, obtaining a picture data set and a deep learning model; S2, dividing the picture data set into a training set and a test set, and training the deep learning model by using the training sample to obtain a classification model; S3, randomly selecting pictures from the test set astest seed samples; S4, inputting the test seed samples into a classification model, if a classification result output by the classification model is inconsistent with a label of the test seed sample,taking the test seed samples as test samples, and if not, entering step S5; S5, calculating a gradient based on the cross entropy loss and the neuron coverage rate of the test seed samples in the classification model; S6, modifying the test seed samples according to a gradient rising algorithm; S7, circularly executing steps S4 to S6 until all the test seed samples become test samples and are output; and S8, evaluating the classification performance of the model by using the test samples.

A method, apparatus, and system for coating, singulating, aligning, ablating, and indexing a number of seeds for testing is described. The apparatus has a carrier designed to hold seeds in a predetermined alignment for ablating a seed sample from a seed portion, the seed portions are indexed to the seed samples. The carrier is then located on the apparatus which delivers the seed samples from the carrier to a collector for laboratory testing.

A method for reducing resources for selecting seed to be produced in commercial quantities or for research is disclosed. Samples of seed which are candidates for selection are collected and given an identifier. Specific tissue or structure from candidate seed is removed. A test or analysis is performed on the candidate seed or the removed tissue or structure. Results of the test or analysis are recorded and correlated to the seed's identifier. The results are evaluated and a decision is made whether to select a candidate seed for commercial production or for research. Time, space, and labor associated with growing plants in an experimental plot or greenhouse and taking tissue samples from growing plants is saved.

The invention discloses a preparing method and an application of a sample for detecting cucumber green mottle mosaic viruses (CGMMV) carried by cucurbitaceae seeds. The preparing method includes the steps that starting from treating seed samples, shell-kernel separating treatment is carried out on the cucurbitaceae seeds, seed kernels with the low virus content are removed, seed shells with the high virus content are left to serve as to-be-tested samples, the seed samples obtained after treatment are sufficiently grinded through a full-automatic rapid sample grinding instrument, and the CGMMV in the large quantity of cucurbitaceae seeds can be rapidly detected through the sufficiently-grinded samples with a RT-PCR method and can also be rapidly detected through the sufficiently-grinded samples with a dot enzyme-linked immunosorbent assay (dot-ELISA) method. The preparing method and the application are suitable for virus detection of the common cucurbitaceae seeds such as calabash seeds, cucumber seeds, lagenaria sicerariae seeds, pumpkin seeds, long gourd seeds and watermelon seeds. A detecting method is high in sensitivity, good in specificity, short in consumed time and low in cost, technical supports are provided for large-scale detection and scientific prevention and control of the CGMMV carried by the cucurbitaceae seeds, and references are provided for detection of plant viruses transmitted by other seeds.

The invention relates to a combined-skewness-based waveband selection method for a hyperspectral image of a corn seed. According to the technical scheme, the method comprises: step a, a to-be-identified corn seed sample is placed in a hyperspectral image collection system and thus hyperspectral images of the corn seed are collected and obtained; step b, a contour curve of the corn seed is obtained by using threshold segmentation, spectrum mean features and entropy features of the corn seed under the contour are solved and obtained, feature combination is carried out and then the combined feature is used as a feature parameter X of the to-be-identified corn seed; step c, a combined skewness value JS (X) in a full-waveband mode is resolved and obtained by using the feature parameter, waveband selection is carried out by using a sequential backward selection method, and then an optimal wave combination phi is outputted; and step d, a prediction model is established and an evaluation result of the waveband selection method is obtained. According to the invention, waveband selection is carried out on the hyperspectral image of the corn seed, so that waveband selection can be realized on the multi-feature condition. Moreover, the method has advantages of simple operation, high rapidity and effectiveness, and high robustness.