203 results about "Grain quality" patented technology

The invention relates to the technical field of grain sorting and cleaning, in particular to a multi-modal sensor fusion-based grain quality dynamic grading and stone removal cooperative control method, which comprises the following steps of: 1, synchronously collecting by a multi-modal sensor, and generating a synchronous pulse through a high-speed photoelectric trigger when grains fall, a linear array camera is triggered to collect grain surface morphology sequence images, a near-infrared spectrometer collects an absorbance curve and moisture content data, and a piezoelectric weighing platform collects impact pressure waveforms; step 2, dynamic grading decision making; step 3, generation of collaborative parameters; 4, executing and feedback optimization, and issuing the generated parameters to a vibrating screen, a fan and an inclination angle adjusting mechanism to execute control; and detecting the proportion of residual impurities at a stone removal outlet, and triggering the cascade decision-making model to retrain when the proportion exceeds the limit continuously. Through the design, the grain grading and stone removing efficiency and accuracy are remarkably improved, and high-quality product experience and value-added benefits are provided for users.

Provided is the use of a protein derived from wheat and a biomaterial related thereto in increasing the yield per plant and grain protein content of a crop. The protein is designated as SWEET11, and may be a protein having an amino acid sequence as shown in sequence 9, sequence 6 or sequence 3 in the sequence listing. The wheat SWEET11 protein (TaSWEET11) is over-expressed in the Zhengmai 7698 wheat variety (nationally approved), and it is found that the over-expression of TaSWEET11 can significantly improve the thousand-grain weight of wheat grains and increase the yield. In addition, the over-expression of TaSWEET11 also increases the protein content in the wheat grain, significantly improving the grain quality while increasing the yield. The TaSWEET11 protein can be used for breeding and quality improvement of crops.

The invention discloses a grain quality detection device and method, a control device and a storage medium, and the device comprises a grain sample separation device which is used for dividing a target grain sample into two paths and conveying the target grain sample to a first discharge port and a second discharge port; the appearance quality detection device is used for detecting an appearance detection result; a volume weight detection device for detecting a target volume weight; the internal quality detection device is used for detecting an internal quality detection result; the safety index detection device is used for detecting a safety index detection result; a recovery device; the conveying device is used for sequentially conveying the target grain sample among the first discharge port, the appearance quality detection device and the recovery device and among the second discharge port, the volume weight detection device, the internal quality detection device, the safety index detection device and the recovery device; and the control device is used for determining grade information of the target grain sample according to the appearance detection result, the target volume weight, the internal quality detection result and the safety index detection result. The detection efficiency and the accuracy of the detection result can be improved.

The invention relates to the technical field of agricultural data management, in particular to a grain data management method and system based on artificial intelligence, and the method comprises the following steps: based on a grain transportation container and a storage environment, extracting monitoring data of temperature and humidity monitoring equipment, a gas component detector and an illumination sensor, summarizing environment parameters, screening abnormal environment data, and storing the abnormal environment data; and obtaining an environment state record. According to the invention, through accurate analysis of temperature and humidity, gas components and illumination data, perception and alarm capabilities of sudden changes of a storage environment are enhanced, comprehensiveness of quality change monitoring is improved, mining capability of relevance of environmental factors and microbial pollution is enhanced, optimal storage conditions are matched, quality loss risks are reduced, pollution factors are accurately identified, and the method is suitable for popularization and application. Through combination of air quality data, pollution hidden danger screening and risk pre-judgment capabilities are improved, abnormal environment data are deeply analyzed, quality deterioration alarm is optimized, abnormal environment identification precision is improved, and a grain quality guarantee system is enhanced.

The invention discloses a grain defective product detection method combining fingerprint spectrum and finite element analysis, and particularly relates to the technical field of grain quality detection.The grain defective product detection method comprises the following steps that grain chemical and physical characteristics are extracted through a fingerprint spectrum and a finite element model, and key characteristic vectors are obtained through dimension reduction screening; on the basis of defective grain grade labels, mutual information is calculated, cross-modal analysis is carried out, and a strong dependency feature group is identified; feature sparsity and stability evaluation are combined, features are optimized by adopting a self-adaptive screening method, a food quality knowledge graph is finally constructed, and intelligent detection and quality evaluation of defective food are realized; according to the method, the accuracy of a detection model, namely a grain quality knowledge graph, is improved through feature sparsity and feature stability evaluation, an automatic grain defective product detection and recognition mechanism is constructed by utilizing a multi-modal information fusion method of fingerprint spectrum and finite element analysis and combining a mutual information analysis and dynamic feature screening algorithm, and the detection accuracy is improved. And the dependence on manual intervention and experience judgment is reduced.

The invention discloses a grain crop grain classification detection method and system based on small samples, and relates to the technical field of grain quality detection.The method comprises the steps that hyperspectral images of grain crop grains are obtained, quality categories are marked, and then multi-modal features are extracted to construct a classification data set; the data set is used for training a multi-modal fusion classification model, a feature fusion network carries out dynamic weighted fusion on multi-modal features by means of an attention mechanism, a high-dimensional fusion feature vector is generated, and a classifier detects a quality category according to the high-dimensional fusion feature vector. During detection, the multi-modal features of the to-be-detected grains are input into the trained model, and then the quality category can be obtained. The method effectively solves the problems that a single modal method is difficult to deal with complex differences among grain crop grain types, high in hyperspectral data dimension, few in samples, easy to over-fit and the like under the condition of small samples, dynamic focusing of key information is realized by introducing an attention mechanism to optimize modal fusion weight distribution, and the method is suitable for popularization and application. And the classification accuracy and the model robustness are improved.

The invention discloses grain quality management and detection equipment for humidity detection for grain storage, and relates to the technical field of humidity detection, the grain quality management and detection equipment for humidity detection for grain storage comprises a machine body, and also comprises a material turning device, a dehumidification device and a moisture-proof device; wherein the material turning device comprises a detection device, a motor, a transmission shaft, a first rotating plate, a first rotating rod, a circular ring, a long rod, a supporting rod, a first sliding block, a reciprocating lead screw, a first belt, a support, a vertical rod and a second rotating plate, the detection device is fixedly connected to the top of the interior of the machine body, the motor is fixedly connected to the left side of the machine body, and the transmission shaft is fixedly connected to the output end of the motor; according to the grain quality management detection equipment for humidity detection in grain storage, when the first rotating plate rotates, the first rotating plate can turn over grains on the lower half portion in the machine body upwards, and the grains on the lower half portion of the machine body can be effectively prevented from being in a humid state for a long time.

The invention belongs to the technical field of grain loading supervision, and particularly relates to a grain transport vehicle grain loading state detection system and method based on visual identification, and the system comprises a visual collection module, an image processing and analysis module, a loading state evaluation module, an intelligent sampling auxiliary module and a cloud management and control module. Real-time pictures of grains in the vehicle are captured through the visual acquisition module, the image processing and analyzing module generates a three-dimensional distribution image of the grains based on the real-time pictures, the loading state evaluation module judges whether the grain loading state of the grain transporting vehicle is qualified or not according to the grain distribution image, the loading problem can be found and corrected in time, and the working efficiency is improved. The intelligent sampling auxiliary module automatically plans the sampling path and depth of the intelligent sampling machine according to the grain loading state information synchronized by the cloud management and control module, so that accurate and efficient automatic intelligent sampling is realized, and the accuracy of grain quality detection is improved.

The invention discloses a special slow-release compound fertilizer for corn and a preparation method thereof, the special slow-release compound fertilizer comprises the following raw materials by weight: 30-40 parts of urea, 15-20 parts of ammonium sulfate, 20-25 parts of potassium sulfate, 15-25 parts of monoammonium phosphate, 2-3 parts of a seaweed extract, 0.5-1.5 parts of polyaspartic acid, 0.5-1.5 parts of trace elements, and 0.5-1 part of a slow-release agent, the slow-release agent comprises DMPP, the trace elements comprise zinc-boron-titanium citrate, and the slow-release agent comprises potassium sulfate, potassium sulfate, potassium sulfate, monoammonium phosphate, polyaspartic acid, zinc-boron-titanium citrate. The seaweed extract comprises chitosan. DMMP is added into the formula, so that stable supply of nitrogen and synergistic yield increase benefits are achieved; the chitosan, the titanium zinc borate citrate and the polyaspartic acid are added, so that the absorption efficiency of the root system is further improved, the robust growth of corn plants is promoted together, and the acre yield and the grain quality of the corn are effectively improved.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent multifunctional grain threshing method and system. The method comprises the following steps that a conveying belt device is controlled to evenly convey grains to be threshed to an inlet of a threshing device, a photoelectric sensor is controlled to detect the number and speed of the conveyed grains, and grain conveying data are generated; generating threshing strength and speed parameters according to the grain conveying data, and respectively obtaining threshing strength parameter data and threshing speed parameter data; performing threshing operation of the thresher according to the threshing strength parameter data and the threshing speed parameter data, and monitoring the pressure of the cavity of the thresher to obtain threshing pressure data; and performing threshing effect evaluation according to the threshing pressure data to obtain threshing effect evaluation data, and performing optimization adjustment on the threshing strength parameter data and the threshing speed parameter data according to the threshing effect evaluation data to obtain real-time threshing optimization parameter data. The threshing efficiency can be improved, and the grain quality can be guaranteed.

The invention discloses an intelligent analysis method for a grain storage process, and relates to the technical field of intelligent storage, and the method comprises the steps: analyzing the influence degree of environment data on grain quality, recognizing key environment data and secondary environment data, carrying out the abnormal value detection of the key environment data based on a box plot method, whether abnormal value detection is carried out on the secondary environment data or not is judged according to the key environment data abnormal value detection result and the influence degree of the secondary environment data on the grain quality; correcting the abnormal data based on a particle swarm optimization radial basis function neural network algorithm to obtain an abnormal environment factor; the problems that the response to the change of the storage environment is not timely and the grain storage efficiency is low are solved; the grain storage efficiency is improved, the loss is reduced, and the grain quality is ensured.

The invention discloses a grain quality index intelligent detection method based on machine vision, and relates to the technical field of grain quality safety detection, and the method comprises the steps: collecting an upper surface image and a lower surface image of a grain sample through a high-resolution camera, carrying out the preprocessing, obtaining a preprocessed two-dimensional image, extracting two-dimensional features through a convolutional neural network, and carrying out the recognition of the two-dimensional features; the method comprises the following steps: extracting two-dimensional features by combining a convolutional neural network, identifying external defects of grains, analyzing internal defects of the grains by applying a physical model of a light scattering theory, and obtaining a preliminary quality evaluation result. The external defects of the grains are identified by combining the convolutional neural network to extract the two-dimensional features, and the internal defects of the grains are analyzed by applying the physical model of the light scattering theory; therefore, comprehensive assessment of the internal and external quality of the grain is realized. And the SVM classifier is adopted to carry out final quality state judgment on each grain, so that the user experience and the working efficiency are further improved.

The invention relates to a grain unmanned sampling quality inspection device and process, and belongs to the technical field of grain detection, and the grain unmanned sampling quality inspection device comprises a sampling mechanism used for sampling grains; the reserved sample packaging machine is used for packaging the reserved sample grains; the sample mixing and separating machine is respectively connected with the sampling mechanism and the reserved sample packaging machine and is used for mixing and separating the grains sampled by the sampling mechanism, and the reserved grains are conveyed to the reserved sample packaging machine to be packaged; an impurity unmanned inspection machine; an unmanned intelligent inspection machine; the inspection result inquiry machine is used for acquiring grain quality information; and the pneumatic conveying mechanism is used for conveying grains. Compared with a traditional manual or semi-automatic inspection mode, the inspection efficiency is greatly improved, manual intervention is reduced, and the labor intensity is reduced.

The invention discloses a wheat imperfect grain identification method based on a double-branch network and feature fusion, and the method comprises the following steps: S1, inputting a wheat imperfect grain image, and generating initial features through image segmentation and linear embedding; s2, performing multi-stage feature extraction by adopting a double-branch network architecture, specifically, improving a Swin Transform and a multi-scale dynamic convolution branch; s3, after each level of feature extraction, performing interactive fusion on the double-branch features through a cross attention fusion module; s4, aggregating multi-scale features through a four-stage feature fusion module; and S5, finally outputting an identification result through a pooling linear layer. According to the method, the accuracy rate in the wheat imperfect grain multi-classification task is remarkably improved and reaches 98.82%, a high-precision and low-cost automatic solution is provided for grain quality detection, and the problems that an existing method is insufficient in recognition precision and limited in feature expression ability are solved.

The utility model relates to an unloading plate structure and an anti-breaking unloader for grain quality inspection, and belongs to the technical field of unloaders. The air suction pipe is connected with the negative pressure air suction pipe and conveys grains through negative pressure; the grain conveying pipe is used for conveying grains; the discharging plate is arranged at an outlet of the discharger shell, and the discharging plate is movably connected with the discharger shell; and the driving part is used for driving the unloading plate to rotate. The unloading plate and the driving part are arranged in the device, and the driving part is matched with the negative pressure system, so that when grains are stacked to reach the preset weight, the gravity triggers the unloading plate to be opened stably, and the situation that the grains are broken due to instant impact, and grain detection data are influenced is avoided.

The invention relates to the field of plant growth regulators, in particular to a conditioner group for improving the grain quality and the rice quality of direct seeding rice and a preparation and use method thereof.The preparation method of a conditioner in the initial heading stage comprises the following steps that 1, 6-benayl aminopurine is dissolved in a diluted hydrochloric acid solution in a assisted mode, and a 6-benayl aminopurine solution is obtained; step 2, adding choline chloride into the solution obtained in the step 1, stirring to obtain a mixed solution, transferring the mixed solution into a volumetric flask, and adjusting the pH value; 3, abscisic acid powder is dissolved with absolute ethyl alcohol, volume metering is carried out after dissolution to form a stock solution, and then PH adjustment is carried out. Two conditioners are included and sprayed in the initial heading stage and the full heading stage respectively, glumous flower degeneration can be reduced, the photosynthetic production capacity of the upper three leaves after flowering can be improved, assimilation substances are promoted to be transferred to grains, especially the filling state of vulnerable grains is improved, and the yield of the seedlings is increased. Finally, the grain number, the maturing rate, the grain weight and the yield are improved, meanwhile, the head rice rate of rice can be improved, and the protein content is reduced, so that the palatability of rice is improved.

The invention discloses a method for predicting the change trend of grain quality in a granary based on multi-modal data fusion, and relates to the technical field of granary supervision, and the method comprises the steps: judging a grain quality basic index and a grain quality change rate according to preprocessed multi-modal data; performing spatial-temporal feature fusion analysis based on the preprocessed multi-modal data, the grain quality basic index and the grain quality change rate, and constructing a grain quality prediction feature space; generating a grain quality change trend prediction model by using a preset grain quality evolution law and the grain quality prediction feature space; the preset grain quality evolution rule is obtained based on historical multi-modal data and historical granary environment parameters; obtaining current granary environment parameters, and inputting the current granary environment parameters into the grain quality change trend prediction model to output a grain quality change trend prediction result; the demand of intelligent warehouse management is met, and the accuracy and practicability of grain quality change trend prediction are improved.

The utility model relates to the field of grain processing, and discloses a drying device and a dryer, the drying device comprises a drying unit, the drying unit comprises a first drying layer, a middle tempering layer and a second drying layer which are sequentially arranged from top to bottom, the first drying layer communicates with a first air inlet pipe provided with a first air adjusting door, and the second drying layer communicates with a second air inlet pipe provided with a second air adjusting door; the second drying layer communicates with a second air inlet pipe provided with a second air adjusting door. The tempering unit is communicated with the second drying layer; the control unit is configured to adjust the opening degrees of cold air doors of the first air adjusting door and the second air adjusting door so that the air temperature of the first air inlet pipe can be larger than that of the second air inlet pipe when the grain moisture is larger than or equal to a preset moisture value; and when the grain moisture is smaller than the preset moisture value, the opening degrees of cold air doors of the first air adjusting door and the second air adjusting door are adjusted so that the air temperature of the first air inlet pipe can be close to or equal to that of the second air inlet pipe. According to the drying device and the dryer, the grain quality can be guaranteed, and the drying speed and the drying efficiency can be improved.

The invention discloses an intelligent processing system for agricultural grain harvesting, and the system comprises a plurality of acoustic emission sensing units which are distributed in an array manner, are coupled to a threshing part, and are used for monitoring the micro-damage of grains; and the integrated multi-mode sensing unit is arranged on the grain conveying part and comprises a transmission type sensor for acquiring grain internal structure data, a spectrum sensor for acquiring grain surface chemical component data, a volatile organic compound sensor and a body moisture sensor. The system also includes a central data processor configured to: calculate a micro-damage index; generating a grain quality map; generating corruption risk features; and generating a system control signal for the agricultural harvesting device based on at least one of the microdamage index, the grain quality map, and the spoilage risk feature. The problems that internal damage cannot be monitored in real time, internal and external defects cannot be distinguished and moisture interference cannot be overcome in a harvesting field are solved.

The invention discloses a rapid breeding method of an early-maturing high-yield wheat variety. The rapid breeding method comprises the following specific steps: S1, establishing test fields in southern Xinjiang, wherein the test fields comprise a greenhouse test field and an outdoor test field; s2, screening high-quality wheat seeds: selecting a high-quality Xinwinter 20 # wheat seed as a female parent, taking Jinan 17 # and a derivative wheat seed Jimai 44 # thereof with good comprehensive performance as a prepared male parent, respectively planting the female parent and the prepared male parent in an outdoor test field located in southern Xinjiang, and screening the high-quality wheat seeds; s3, performing artificial hybridization by taking Xinwinter-20 as a female parent and Jimai No.44 as a male parent to obtain F3-generation single-plant seeds. The rapid breeding method of the early-maturing high-yield wheat variety disclosed by the invention has the remarkable effects of high yield, high quality and disease resistance, and the bred new product has good yield, disease resistance and stress resistance, good grain quality and outstanding yield performance; large-area popularization and planting can be realized, and the purposes of saving cost, increasing efficiency and promoting income increase of farmers can be achieved.

The invention discloses a grain storage quality detection system, and relates to the technical field of grain storage, and the system comprises a sample collection module which is used for obtaining samples from grain storage, and employs an automatic sampling device to carry out multi-point sampling according to the size, shape and grain storage distribution condition of a granary and a preset sampling rule; the index detection module is used for carrying out index detection on screened samples in grain storage; and the data processing module is used for receiving the index detection data of the index detection module. According to the invention, various index information of grains can be detected at the same time, grains with potential safety hazards can be effectively identified, defective grains can be processed in time, mildewed and insect-attacked grains are prevented from flowing into the market, the body health of consumers is guaranteed, the stability and order of the grain market are maintained, the food safety of the grains is ensured from the source, the detection period is greatly shortened, and the detection efficiency is improved. Grain quality information can be fed back in time, managers can grasp the best processing opportunity, and grain loss is reduced.

The invention relates to a grain impurity detection method based on a double-level deep learning algorithm, and belongs to the field of grain quality detection. Comprising the following steps: collecting grain sample images containing various impurity types on a grain collecting crawler belt, and establishing an original data set; carrying out feature extraction and classification training on the CNN model by using a first sample set obtained by preprocessing the original data set, and carrying out network structure and parameter tuning, so that the CNN model can carry out impurity type classification and impurity position labeling on an input sample image; establishing a second sample set for grain sample images without impurities after CNN model detection; training a YOLO model by using the second sample set, so that the YOLO model can perform category classification and position labeling on impurities which are not detected by the CNN model; the CNN model or the CNN model and the YOLO model are adopted to track the marked impurity positions after the continuously shot grain sample images are detected, so that the impurity position detection precision is improved. According to the invention, impurities in grains can be efficiently and accurately identified.

The invention discloses a grain supply chain privacy data sharing method based on distributed federated learning, and relates to the field of grain quality safety. According to the method, the private data sharing architecture of the grain supply chain is divided into a local layer, an edge layer and a global layer, a feature distillation framework is designed for data isomerism, and a model aggregation scheme and a hierarchical federated learning algorithm of each layer are optimized. And meanwhile, five encryption algorithms are introduced to realize hierarchical encryption, so that the data are ensured to be available but invisible, the sharing security and credibility are improved, and privacy protection and data sharing are balanced. Coupling of different types of data among links of the grain supply chain is increased, convenience and safety of data interaction among the links of the grain supply chain are improved, storage cost of the data of the supply chain and high delay of interaction are reduced, fine management of the data of the grain supply chain and personnel is achieved, and the management efficiency of the grain supply chain is improved. And the food quality safety of the grains is ensured.

The invention discloses a multi-index prediction management platform for grain quality analysis, and relates to the technical field of grain quality analysis, the multi-index prediction management platform comprises a grain detection module and a prediction evaluation module, the prediction evaluation module is connected with the grain detection module through signal transmission, and the prediction evaluation module is connected with the grain detection module through signal transmission. And the prediction and evaluation module performs analysis and prediction and evaluation by receiving the information collected by the grain detection module, and transmits a result to the database. By designing the prediction evaluation module, the function of accurately predicting the grain quality change trend is achieved, the problems that a single model is insufficient in prediction accuracy, poor in adaptability and insufficient in information utilization are solved, deviation and limitation existing in the single prediction model can be avoided, multiple factors influencing the grain quality are comprehensively considered, and the prediction accuracy is improved. The accuracy, stability and reliability of prediction are improved, and the utilization of computing resources and time cost is optimized.

The invention provides a grain full-link quality tracing system and method and a medium, and belongs to the technical field of grain safety, and the grain full-link quality tracing system comprises a data collection module which is used for collecting full-link grain data of grains from raw material purchasing to storage, processing and circulation links in real time; the full-link grain data comprises environment data and equipment parameters; the block chain evidence storage module is used for carrying out Hash calculation on the full-link grain data acquired by the data acquisition module and the key business data input manually and carrying out chaining evidence storage; the intelligent analysis module is used for performing prediction and anomaly detection on the grain quality risk by using a neural network based on the full-link grain data of the upper chain evidence and the real-time full-link grain data; and the cloud service module is used for integrating multi-source data, providing a data interface and visual display, and supporting full-link traceability query and supervision audit. According to the technical scheme, the problems of data fault, tampering risk and risk early warning lag in the prior art can be solved.

1. Name of the product in this design: Grain Quality Analyzer. 2. Purpose of this design: For the grade testing of grain quality. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1. 5. The bottom surface of this product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted.

This application relates to the technical field of grain processing quality inspection, and in particular to a grain quality infrared image recognition equipment, which includes a housing with a turntable rotatably mounted inside the housing. A drive source is provided between the turntable and the housing, and multiple storage cylinders are arranged on the turntable. This utility model, through the annular layout design of the turntable carrying multiple storage cylinders, combined with the rotation control of the drive source, can realize multi-channel parallel detection of grain samples. When one storage cylinder is under the infrared detector for detection, other storage cylinders can simultaneously complete operations such as feeding, unloading, or vibrating and spreading, avoiding the "intermittent detection" drawback of traditional single-channel equipment. At the same time, the two discharge pipes, combined with the eccentric setting of the electric telescopic rod, can automatically switch the opening and closing state of the baffle according to the detection results, realizing dynamic sorting of qualified and unqualified grains without manual intervention, significantly improving the detection and sorting efficiency in large-scale grain processing scenarios.

The invention belongs to the technical field of grain quality detection, and particularly discloses a grain quality detection device which comprises a detection vehicle, an oblique insertion flow guide sampling barrel, an alternate lifting rolling bearing type dyeing mechanism, a quality highlighting device and a paving observation device. According to the difference between surface structures and internal pore structures of wizened grains and full grains, the difference can cause different adsorption capacities of the wizened grains and the full grains when the wizened grains and the full grains are in contact with dyes, and the difference can be presented in a visual color difference form by dyeing the grains, so that the quality of the grains can be conveniently distinguished; the method for distinguishing the grain quality based on the visual difference after dyeing has the advantage of being high in intuition, and the grain quality can be rapidly judged under the condition that complex instrument analysis is not needed.

The invention relates to the technical field of crop planting. The invention provides an accurate fertilization and harvesting method for increasing yield and improving quality of sweet corn by nitrate nitrogen and rhizosphere symbiotic AM fungi. According to the technical scheme, the nitrogen and phosphorus compound fertilizer with the optimized proportion and AM fungi are synergistically used, so that accumulation of biomass of the mycorrhizal sweet corn and accumulation of nitrogen elements, phosphorus elements, soluble sugar content and amino acid content in sweet corn kernels are effectively promoted, and then the yield of the sweet corn and the kernel quality are improved; the utilization rate of nitrogen and the utilization rate of phosphorus are increased by 29.54% and 19.43% respectively by the AM fungus mycorrhizal corn. In addition, the time from planting to harvesting of the sweet corn ranges from 8 weeks to 9 weeks, the protein nutrition of the sweet corn is relatively best in the harvesting period, and the grain yield is relatively highest.

This invention relates to the field of crop breeding technology, specifically to a highly efficient breeding method for early-maturing highland barley. The method includes the following steps: parent selection and matching, planting substrate preparation, application of growth regulators, planting management, multi-generation screening, and variety identification. This invention, by screening parents with early-maturing and disease-resistant characteristics, optimizing the planting substrate (adding microbial agents), precisely applying growth regulators, and dynamically controlling the planting environment and nutrient solution, combined with early maturity, disease resistance, and variety identification, efficiently breeds highland barley with a short growth period and strong resistance to stripe rust and powdery mildew. It optimizes plant type and yield structure, improves grain quality, achieves increased yield and genetic stability, and provides technical support for highland barley variety improvement and high-yield, high-quality cultivation.