104 results about "Crop mapping" patented technology

The invention relates to a crop irrigation demand prediction method based on an intelligent integrated prefabricated pump station, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting a crop image in a prefabricated pump station irrigation scene, obtaining auxiliary data, calculating a daily accumulated reference evapotranspiration value, and obtaining a time-synchronized multi-modal data set; labeling and dividing the multi-modal data set; constructing an irrigation demand prediction model, wherein the irrigation demand prediction model comprises a spectral reflectivity enhancement module, a double-flow separation convolution module, a cross gating fusion module, a region sensitive pyramid module and a coupling prediction head module; inputting data in the divided training set into the model, and training the model through weighted total loss to obtain a trained model; inputting a to-be-detected image into the trained model to obtain a soil water content and transpiration prediction result; and performing irrigation decision based on the soil water content and transpiration prediction result. The accuracy of irrigation prediction can be improved.

The invention relates to the technical field of fertilization and irrigation regulation and control, and discloses a fertilization and irrigation two-way regulation and control system under water demand and fertilizer demand coupling modeling, and the system constructs the fertilization and irrigation two-way regulation and control system based on the water demand and fertilizer demand coupling modeling. The multi-source data acquisition module is used for acquiring soil, crop growth period and meteorological data through a layered soil sensor, crop image acquisition equipment and a meteorological acquisition point; the water and fertilizer scheme generation module calculates irrigation amount, fertilization type and dosage based on data, and adjusts intervals in combination with temperature; the operation execution module controls the operation of the irrigation and fertilization equipment and records the state; and the data correction module compares the sensor with the sample data to generate a correction scheme. The system realizes accurate regulation and control through a closed loop of data acquisition, scheme generation, execution and correction, simultaneously covers equipment state monitoring and early warning, and ensures efficient fertilization and irrigation to adapt to crop requirements.

The invention provides a multi-modal agricultural technology question and answer method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: extracting a text feature vector, an image feature vector, a time sequence feature vector and a spatio-temporal context feature vector according to a query text and a crop image; calculating an initial fusion feature according to the spatio-temporal context feature vector, the image feature vector and the time sequence feature vector; when the query text contains a semantic entity of a preset type, calculating to obtain a final fusion feature according to the text feature vector and the initial fusion feature; splicing the final fusion feature and the text feature vector, and mapping the spliced vector to a space-time knowledge graph for reasoning to obtain a causal reasoning path; and generating a question and answer result according to the causal reasoning path. According to the method, deep alignment of time and space and semantics is carried out on the multi-modal agricultural data, and causal reasoning is carried out in combination with the knowledge graph with time and space constraints, so that the accuracy of a question and answer result is remarkably improved.

The invention relates to an insect pest detection processing method, system and device, and belongs to the field of insect pest detection.The method comprises the steps that a crop image in a target area is collected, black spot recognition and malformation recognition are conducted on the crop image, and black spot parameters and malformation parameters are obtained; according to the black spot parameter and the malformation parameter, calculating to obtain a first insect pest parameter, and configuring an insect body detection range and an insect body detection scale; collecting a detection image set, and carrying out insect body recognition to obtain an insect body recognition result; and processing according to the insect body identification result to obtain a second insect pest parameter, and calculating to obtain an insect pest parameter as an insect pest detection processing result in combination with the first insect pest parameter. According to the method, the technical problems of inaccurate detection and low treatment efficiency caused by the fact that pest distribution characteristics are not considered in mangosteen pest detection in the prior art are solved, and the technical effects of analyzing the pest distribution characteristics through crop pest symptoms, optimizing a detection strategy and improving pest detection accuracy and treatment efficiency are achieved.

The invention provides an unmanned aerial vehicle pesticide spraying area recognition method and system combined with image recognition, and relates to the technical field of agricultural unmanned aerial vehicle operation, and the method comprises the steps: firstly obtaining an image set which is collected by an unmanned aerial vehicle and comprises farmland scene images at different time periods, and carrying out the region semantic association analysis to generate a farmland region semantic association graph; next, an image recognition model is called to perform growth state recognition on the crop image part, and a spraying area adaptation model is constructed in combination with the semantic association graph; then, based on the spraying area adaptation model, evaluating a spraying demand, dividing farmland sub-areas and generating a spraying area division scheme; determining pesticide spraying parameters of each sub-region according to the spraying region division scheme, and generating an unmanned aerial vehicle pesticide spraying instruction; finally, the instruction is sent to an unmanned aerial vehicle control system, the unmanned aerial vehicle is controlled to execute pesticide spraying operation, and therefore the pesticide spraying accuracy and uniformity can be improved.

The invention relates to the technical field of intelligent agricultural intelligent irrigation control, and discloses an intelligent irrigation analysis control system based on a crop growth period. The system comprises a crop image sequence, soil humidity profile data and a regional meteorological sequence. And the data analysis module is used for performing multi-stage growth state interpretation on the image sequence, generating a crop structure state profile in each growth period, performing vertical moisture exchange identification on the soil humidity data, and generating a root zone moisture dynamic map. And the habitat mapping and stress evaluation module synchronously executes habitat adaptability mapping and moisture stress risk mapping. And the irrigation decision module corrects the habitat mapping result according to the stress evaluation result and generates an irrigation decision profile. And the control instruction generation module generates a partition valve control instruction in combination with the decision profile and the alternate irrigation rule. According to the invention, deep fusion analysis of the crop growth state, the root zone moisture and the meteorological environment is realized, and the irrigation decision accuracy and the water resource utilization efficiency are improved.

The invention relates to the technical field of vegetable cultivation, and discloses an automatic light supplement control method and system for vegetable cultivation, and the method comprises the steps: determining the growth state of a vegetable crop based on crop image information and a crop growth model, and judging whether to execute a light supplement strategy or not according to a solar altitude, determining a backtracking light supplementing strategy or an initial light supplementing strategy according to the occurrence frequency of the growth state in the historical light supplementing database, determining target light supplementing data of the vegetable crops according to the number of the light supplementing data, and when the initial light supplementing strategy is determined, determining the target light supplementing data of the vegetable crops based on a recurrent neural network model, and analyzing the historical light supplement record to determine a light supplement swing factor of the light supplement lamp, adjusting the target light supplement data based on the light supplement swing factor, and performing light supplement on the vegetable crops according to the adjusted target light supplement data. According to the invention, the reliability of light supplement is ensured through the crop growth model and the light supplement swing factor.

The invention relates to the technical field of agricultural pest recognition, and provides an agricultural pest pattern recognition method and system based on computer vision, and the method comprises the steps: receiving a crop image of a to-be-diagnosed crop in a field, carrying out the preliminary diagnosis of the crop image, obtaining a preliminary diagnosis result, and recognizing key visual features supporting the preliminary diagnosis; generating a visually similar disease candidate set according to the preliminary diagnosis result; performing counter-example comparative analysis on each disease in the disease candidate set to obtain excluded similar diseases and generate exclusion reasons of the excluded similar diseases; providing and displaying a preliminary diagnosis result and typical case reference pictures of the eliminated similar diseases for agronomists; and integrating the preliminary diagnosis result, the key visual features, the elimination reason and the typical case reference picture, generating a structured diagnosis report, and outputting the structured diagnosis report to realize agricultural pest pattern recognition. The method has the effect of improving the accuracy and practicability of agricultural pest pattern recognition.

The invention relates to the technical field of agricultural automation equipment, and discloses a water and fertilizer integrated irrigation device and method based on the Internet of Things and an intelligent strategy, and the device comprises a crop and environment sensing unit which obtains crop image information and environment parameters; the intelligent decision-making unit is used for generating a target irrigation strategy and fertilization concentration; the source water management unit is used for analyzing a water source before irrigation and providing water quality background information; the water-fertilizer synthesis and control unit is used for dynamically synthesizing and outputting a nutrient solution in real time by combining the target concentration and the water quality background; and the cloud management and optimization unit is used for collecting data of each unit and is used for storage, remote monitoring and management. According to the invention, an on-line spectrum analysis technology and a high-frequency pulse microfluidic technology are coupled, a real-time closed-loop control system for directly measuring chemical components is constructed, and high consistency of a digital decision instruction and the chemical components of a physically output nutrient solution is achieved.

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 discloses a crop disease and pest control pesticide spraying method and system based on image recognition, and the method comprises the steps: collecting crop images through carrying multiple cameras by an unmanned aerial vehicle, inputting the crop images into a double-branch deep learning model after preprocessing, and enabling the model to comprise an FCA frequency domain feature extraction network, a DAT-Transform spatial domain feature extraction network and an MSAF fusion module, precise identification of diseases and pests is realized; further integrating real-time environment parameters such as temperature and humidity, wind speed and crop growth stages, constructing a dynamic pesticide amount calculation model, and outputting personalized pesticide spraying amount; the pesticide spraying mechanism is driven by the controller to execute pesticide spraying, effect data is collected after a prevention and treatment period, an optimization recognition model and pesticide amount calculation parameters are fed back, and a recognition-pesticide spraying-feedback-iteration closed-loop prevention and treatment system is formed. The method solves the problems of low recognition precision, rigid pesticide amount regulation and control and lack of a continuous optimization mechanism in a complex environment in the prior art, improves the recognition precision, saves the pesticide amount, and improves the control effect.

The invention discloses an agricultural greenhouse automatic dimming image acquisition method based on visual task feedback, and relates to the field of agricultural data acquisition, and the method comprises the steps: collecting a crop image through a depth camera, carrying out the target detection, extracting an ROI region of a crop, and carrying out the target detection based on an RGB image and a depth image in a detection frame of the crop; the method comprises the steps of calculating a comprehensive image quality index used for representing the overall texture richness and the overall detection reliability of each crop, and then dynamically adjusting the driving current of an LED lamp panel based on the comprehensive image quality index so as to dynamically adjust light. Even if the illumination conditions of different areas of the agricultural greenhouse change and the crops have the phenomena of strong reflection, local shielding and the like, closed-loop dimming can be carried out based on the local image quality of the crops, so that the quality of the collected crop images is improved, and the accuracy and reliability of image analysis are improved.

This invention discloses a multi-sensor fusion adaptive control method and system for a micro-agricultural box. The method simultaneously collects three types of data: environmental, cultivation medium, and crop images. It utilizes an improved unscented Kalman filter algorithm for multi-source data fusion and multi-parameter coupling calculation, combined with a BP neural network-optimized adaptive PID controller to achieve closed-loop control. The system also includes an image recognition dynamic correction unit and a fault diagnosis and tolerance unit. This invention addresses the pain points of existing micro-planting equipment, such as low control precision, large parameter coupling interference, inability to adapt to the entire growth cycle, and poor reliability for unattended operation. This invention improves temperature and humidity control precision to ±0.5℃ / ±3%RH, reduces pest and disease incidence by more than 95%, and achieves pesticide-free, unmanned adaptive planting, suitable for home and commercial micro-agricultural scenarios.

The application discloses a dense planting farmland growth evaluation method and system based on image processing and relates to the field of agricultural information, including the following steps: S1, multi-source data acquisition and preprocessing; S2, improved image segmentation and crop feature extraction; S3, multi-dimensional growth evaluation.The application covers the field level and the plant level and the whole growth period through multi-source data acquisition, uses a generative adversarial network to repair a blocked plant image, restores complete morphological information, solves the segmentation problem in a dense planting scene, improves the accuracy of dense planting crop image analysis, realizes accurate registration of multi-modal data with the help of feature point matching, optimizes image segmentation with a graph neural network, effectively distinguishes overlapping leaves and stems, deeply fuses multi-modal features and dynamically selects a fusion strategy, improves feature distinguishability, constructs a dynamic adaptive evaluation model, improves generalization ability and evaluation accuracy, identifies abnormalities in real time and intervenes, and improves crop risk resistance and yield prediction accuracy.

The present application relates to water and fertilizer management technical field, specifically to a kind of agricultural water and fertilizer management method and system based on cloud platform, including the following steps, utilize sensor to collect crop image and extract chlorophyll and leaf area characteristics, analyze variation rate to determine crop growth process and time sensitivity, construct index type dynamic weight mapping relationship, task resource coupling index is calculated in combination with cloud node load and fertilization data fluctuation, based on index task sequencing and execution time delay deviation compensation, generate water and fertilizer management instruction.In the present application, by fusing multi-dimensional morphological features and environmental load data to construct a dynamic control model, a nonlinear correlation between crop demand and execution weight is established using an exponential function mapping mechanism, precise perception of crop growth demand and adaptive optimization of task execution timing are achieved, the task queue is flexibly adjusted according to the resource coupling state, resource competition conflicts are effectively avoided, and the efficiency of water and fertilizer collaborative management is improved.

The application provides a kind of multimodal agricultural technology question and answer method and system, belong to artificial intelligence technical field, including: according to query text and crop image, extract text feature vector, image feature vector, time sequence feature vector and space-time context feature vector;According to space-time context feature vector, image feature vector and time sequence feature vector, initial fusion feature is calculated;When query text contains the semantic entity of preset type, according to text feature vector and initial fusion feature, final fusion feature is calculated;The final fusion feature is spliced with the text feature vector, and the spliced vector is mapped to the space-time knowledge graph for reasoning, to obtain causal reasoning path;According to causal reasoning path, generate question and answer result.The application carries out the depth alignment of space-time and semantics to multimodal agricultural data, and carries out causal reasoning in combination with knowledge graph with space-time constraint, so as to significantly improve the accuracy of question and answer result.

The invention provides a picking point positioning method and device based on image fusion, a medium and equipment, and relates to the field of image processing, and the method comprises the steps: converting a to-be-processed crop image into a to-be-processed crop gray-scale map; calculating the gradients of the to-be-processed crop grey-scale map in the detection vertical edge direction and the detection horizontal edge direction to obtain a vertical-direction grey-scale gradient map and a horizontal-direction grey-scale gradient map; performing weighted fusion to obtain a middle edge image; performing weighted summation on the to-be-processed crop image and the middle edge image to obtain a weighted fusion edge enhanced image; and inputting the to-be-processed crop image and the weighted fusion edge enhanced image into a picking point positioning model to obtain target crops in the to-be-processed crop image and picking point coordinates corresponding to each target crop. The accuracy requirement of maturity identification is met, the reliability of picking point positioning is guaranteed, and the method is more suitable for application of automatic picking equipment in a large-scale planting scene.

The application discloses a kind of environment data real-time monitoring and analysis method and system for planting field, it is related to agricultural planting technical field, it aims at solving the technical problems that existing technology cannot monitor environmental data implicit change, it is difficult to comprehensively analyze multi-source data, problem diagnosis and processing are not accurate, including the following steps: S1: the planting environment data of planting field is monitored in real time, and the water quality data, soil physicochemical data, soil microbial data, crop image data corresponding to different positions are obtained;S2: the above four kinds of data are arranged according to time sequence respectively, and water quality data change curve, soil physicochemical data change curve, soil microbial data change curve, crop image data change curve are generated;S3: the above four kinds of data change curve are scored according to corresponding parameter respectively, the present application can accurately judge the deterioration condition and position of environment by real-time acquisition of multi-source planting field environment data, solve the monitoring problem of prior art.

The invention provides a crop water demand judgment irrigation method and system based on image recognition, and relates to the technical field of agricultural irrigation. An area to be irrigated is divided into a plurality of subareas, a plurality of image acquisition points are set in each subarea, and an optimal cruise path is calculated according to geographic coordinates of the image acquisition points; an unmanned aerial vehicle or a track robot carrying an image acquisition terminal is used for acquiring crop image data at the image acquisition points of all the subareas along the optimal cruise path; the method comprises the following steps: standardizing crop image data, extracting a spectral feature value for judging the crop moisture condition, inputting a pre-trained water demand judgment model to obtain a water demand index at each image acquisition point, and finally generating a corresponding control instruction based on the water demand index at each image acquisition point, and the irrigation execution unit receives the control instruction and completes irrigation operation. According to the invention, mechanical, automatic and intelligent precise irrigation based on image recognition can be realized, and efficient utilization of water resources is realized.

The application discloses a kind of unmanned aerial vehicle-independent tuber crop ridge line high-precision determination method, comprising: obtaining reference object position data;Different tuber crops are photographed based on the reference object position data, and a plurality of tuber crop images are obtained;The tuber crops in the plurality of tuber crop images are framed, and a plurality of tuber crop points are obtained;The plurality of tuber crop points are fitted, and a fitting line is obtained, based on the fitting line and the reference object position data, a plurality of intersection points are obtained, the plurality of intersection points are connected, and an intersection point line is obtained, and the intersection point line is fitted, and a tuber crop ridge line is obtained.The application can be used for automatic driving unmanned agricultural machine to harvest tuber crops, and is effective and convenient.

The invention discloses a crop phenotype in-situ analysis method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a field crop image acquired by a multi-view depth camera carried by an unmanned vehicle, performing semantic segmentation on the crop image to obtain a plant region and a background region, and dividing to obtain a plurality of grids; determining a dynamic response threshold value of the grid by adopting a gradient magnitude histogram mode, screening feature points from the grid according to the dynamic response threshold value, calculating ORB descriptors, and combining to form an ORB feature point set of the crop image; three-dimensional point cloud reconstruction is carried out based on IMU data and the ORB feature point set of the multi-view depth camera, and a global dense three-dimensional point cloud map is generated; identifying the global dense three-dimensional point cloud, removing ground points, reserving a crop vegetation point cloud, and carrying out single plant instance segmentation on the crop vegetation point cloud to obtain a point cloud cluster of a single crop plant; and performing phenotypic parameter calculation on the point cloud cluster of the single crop plant to obtain phenotypic parameters.

Systems and methods are provided for determining a leaf-to-stem ratio of a crop automatically from an image of the crop. An agricultural vehicle may include a crop analysis system having a computing device and an imaging device for capturing an image of a crop. The computing device analyzes images acquired by the imaging determine to determine a leaf-to-stem ratio of the crop.

The invention discloses a monitoring system for standardized management of farmland, which relates to the technical field of farmland management, and constructs a farmland multi-source information model by acquiring multi-dimensional monitoring parameters including meteorology, soil, water quality and crop images, and compares the farmland multi-source information model with a preset standardized management rule base to obtain a standardized management result. Generating a difference value to identify an abnormal monitoring point and an abnormal type thereof; associating the abnormal information with a geographic layer, generating a visual management map, synchronizing the visual management map to a remote terminal, and supporting graphical display and control operation; meanwhile, the model is dynamically updated based on historical monitoring data and a current difference value, the abnormal response capability and the self-adaption level are improved, and intelligentization, standardization and closed-loop optimization of farmland management are achieved; the method is suitable for multi-crop and multi-plot digital agricultural scenes.

The invention discloses crop growth monitoring equipment based on multispectral perception and machine vision, which comprises a spectrum module, and is characterized in that the spectrum module is internally provided with a spectrum sensor, and the spectrum sensor is used for collecting spectrum data; the image acquisition module is internally provided with an image acquisition camera, and the image acquisition camera is used for acquiring crop graphic information; and the automatic calibration module is arranged below the spectrum module. The spectrum sensor and the image acquisition module are combined, and multiple crop growth parameters can be accurately measured. The design of the system has high controllability and accuracy, crop growth data can be rapidly obtained, and effective data interaction with a user can be carried out. The integrated monitoring system not only improves the evaluation efficiency of the crop growth state, but also provides a scientific basis for agricultural management, thereby promoting the development of precision agriculture.

The invention provides a greenhouse environment control method and device. The method comprises the following steps: acquiring greenhouse environment data, crop image data and park environment data; preprocessing the greenhouse environment data, the crop image data and the park environment data to obtain preprocessed data; obtaining crop growth data according to the preprocessed data and a preset crop growth model; obtaining crop illumination data according to the preprocessed data and a preset crop illumination model; obtaining crop water and fertilizer supply and demand data according to the preprocessed data and a preset crop water and fertilizer supply and demand model; obtaining greenhouse environment control data according to the crop growth data, the crop illumination data, the crop water and fertilizer supply and demand data and a preset judgment threshold; and controlling the working state of load equipment according to the greenhouse environment control data. According to the invention, the accuracy of greenhouse environment control can be improved.

The invention discloses a plant phenotype image acquisition method based on a wheeled phenotype platform, which relates to the technical field of plant image acquisition, and comprises the following steps: deploying the wheeled phenotype platform and planning an acquisition route, acquiring crop image data and environment data in real time, and acquiring historical phenotype information data of crop growth at the same time; the method comprises the following steps: preprocessing collected image data and environment data, extracting phenotypic characteristics of crops for quantitative description and measurement, and carrying out statistical analysis on the extracted phenotypic characteristics to obtain an illumination interference factor and a shielding interference factor; according to the invention, quantitative analysis is carried out on the growth speed, morphological change, leaf color and the like of the plant through image processing and data analysis technologies, a scientific basis is provided for growth monitoring and early warning of plant diseases and insect pests, and in addition, the platform can combine the collected image and data with a plant growth model, so that the early warning of plant diseases and insect pests is realized. Growth trend prediction and yield estimation are carried out, and accurate decision support is provided for agricultural production.

The invention discloses an intelligent crop harvesting optimization system and method, and relates to the technical field of intelligent agriculture, and the system comprises a multi-source data collection module, a multi-source data processing module, an intelligent prediction scheduling module, an abnormality diagnosis module and a transfer learning adaptation module. According to the invention, a multi-source data acquisition module is used for acquiring soil environment, weather, crop images and agricultural machine state multi-modal data in real time and uploading the data to a cloud end; the multi-source data processing module performs space-time alignment, ETL processing and feature extraction on the acquired multi-modal data, and constructs a standardized data lake and a model training environment; the intelligent prediction scheduling module predicts the crop yield and maturity, generates a harvesting task plan in combination with a hybrid scheduling algorithm and pushes the harvesting task plan to the terminal for execution; the abnormity diagnosis module monitors abnormity of equipment, personnel and land parcels in the harvesting process in real time; and the transfer learning adaptation module realizes the harvesting optimization requirement that a small amount of data is quickly adapted to new crop varieties.

The present application relates to the technical field of smart farming. Disclosed are a big-data–based smart-farming intelligent monitoring system and method. The system comprises: a farming data monitoring module, a crop growth prediction module, and an agricultural-machinery harvesting assignment module, wherein the farming data monitoring module uses remote sensing technology to monitor image information of all farmland and crops planted within a supervised region, perform grid-based partitioning on the farmland, and analyzes and calculates crop maturity; the crop growth prediction module is used for constructing a crop growth state prediction model, and predicting an estimated crop maturity of farmland crops at a future time point; and the agricultural-machinery harvesting assignment module calculates the harvesting performance of agricultural machinery in respective harvesting regions assigned thereto, and performs analysis to acquire an agricultural-machinery assignment strategy that maximizes a total harvesting performance for all farmland within the supervised region.

The application discloses an agricultural pest AI early warning and unmanned aerial vehicle cluster precise prevention and treatment system and method, relates to the technical field of agricultural AI early warning, and collects crop pictures and weather data, identifies the types, distribution and diffusion of pests, predicts pest abnormal conditions and the development direction of the pests according to weather data of a future set time period, collects flight data of unmanned aerial vehicles in the development direction of the pests, predicts abnormal pesticide spraying conditions of the unmanned aerial vehicles according to the weather data and the flight data, analyzes pest prevention and treatment abnormal conditions according to the pest abnormal conditions and the abnormal pesticide spraying conditions, and schedules the number of unmanned aerial vehicles according to crop growth stages and the pest prevention and treatment abnormal conditions. The application combines pest abnormal prediction and abnormal unmanned aerial vehicle pesticide spraying in the development direction of the pests to determine the optimal number of unmanned aerial vehicles to be deployed according to the crop growth stages, is favorable for optimizing resource allocation, and realizes precise prevention and treatment of farmland.

The invention discloses a DNA (Deoxyribose Nucleic Acid) fingerprint spectrum for identifying a feeding oat variety and application thereof, relates to the technical field of biology, and provides 21 molecular markers capable of analyzing and identifying the feeding oat variety. A molecular probe combination, a gene chip and a kit prepared from the 21 SNP molecular markers can play an important role in the aspects of the blood relationship and genetic basis of oat crops, variety certification and determination, counterfeit and shoddy seed identification, management and detection of genetic breeding and crop maps and the like.