60 results about "PEAR" patented technology

The invention relates to the technical field of fruit and peach screening, in particular to a fruit and peach multi-stage screening and quality detection method which comprises the following steps: S1, fruit and peach variety judgment: collecting multi-angle images, extracting color, texture and contour features, and outputting variety labels and parameter sets in a classified manner; s2, growth feature mapping: extracting structure key points, matching the structure key points with a variety template, and generating a growth deviation vector; s3, area perception analysis: dividing fruit surface areas based on growth deviation, and independently identifying defects and maturity; s4, grade discrimination: fusing identification results, combining varieties and deviations, and dynamically evaluating a comprehensive quality label; and S5, screening instruction generation: converting the quality label into a control instruction, and driving the sorting device to complete output. According to the invention, integrated automatic processing of intelligent identification of fruit and peach varieties, accurate detection of regional defects and grade screening control is realized, and the accuracy and flexibility of fruit and peach screening are significantly improved.

The invention discloses a method for constructing a pear variety DNA fingerprint spectrum based on a KASP primer combination and application of the method, and belongs to the technical field of molecular marker development. Whole genome re-sequencing is carried out based on 495 parts of different pear germplasm resources of Institute of Fruit Research, Chinese Academy of Agricultural Sciences, hundreds of thousands of SNP variation site data are obtained, and 25 SNP markers and matched KASP primer combinations are further analyzed, identified and screened by using a KASP technology platform. The invention provides a complete set of SNP site detection primers for pear variety research, and can be used for pear variety SNP detection kit development, pear variety DNA fingerprint construction, pear variety genetic typing, pear variety authenticity detection and the like.

The invention discloses a quick pear detection method in a natural environment based on a lightweight Transform architecture. The quick pear detection method comprises the following steps: A, acquiring pear images in different environments; b, making a pear fruit data set; c, constructing a pome detection model; and D, inputting the to-be-identified image into the pome detection model to carry out pome rapid detection. The defects in the prior art can be overcome, the complexity of a detection model is reduced, and the detection speed and precision are improved.

The invention provides a metabonomics technology-based Beijing white pear producing area identification method, which comprises the following steps: collecting Beijing white pear samples from different producing areas, detecting metabolites of the Beijing white pear samples from different producing areas by using an ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry method, analyzing to obtain differential metabolites among the Beijing white pears from different producing areas, and determining the production area of the Beijing white pears from different producing areas. And establishing a producing area distinguishing model based on orthogonal partial least squares discriminant analysis (OPLS-DA), and judging the producing area of an unknown sample according to the model. The method has the advantages of high accuracy, high sensitivity and high flux, can accurately distinguish products from small-range adjacent production places, and provides a simple, convenient and accurate solution for solving the problem that geographical indication products and other local characteristic products are pretended by the adjacent production places; and technical support is provided for protection of local characteristic products such as landmark products.

The invention discloses a generative adversarial network-based pear flower target detection and segmentation method, which comprises the steps of 1, collecting and marking pear flower image data and then establishing a real image data set, 2, constructing a generative adversarial network model, 3, training the generative adversarial network model, and 4, extracting a real image data set. 4, extracting pear flower image features generated by the generative adversarial network model and establishing a general training set, 5, establishing and training a target detection model and a segmentation model, and 6, inputting an image to obtain a detection and segmentation result of a pear flower target. According to the method, the adversarial learning mechanism of the generative adversarial network is utilized, the pear flower image of the detection frame and the segmentation mask closer to the real label can be generated, and the learning ability of the model to the pear flower features is enhanced, so that the detection and segmentation precision of the pear flower image is improved, and the robustness is improved; the method can effectively cope with the influence of factors such as complex backgrounds, illumination changes and shielding, and provides a new technical means for agricultural automation and botany research.

This invention discloses a method and system for classifying pear varieties based on a minimum feature set, belonging to the field of agricultural product classification technology. The method involves acquiring raw physicochemical index data of pear samples, standardizing the data to obtain a standardized dataset, and then performing principal component analysis (PCA) to verify the separability of the raw physicochemical index data. An orthogonal partial least squares discriminant analysis (OPLS-DA) model is used for pairwise comparisons across multiple groups. After screening and ranking, a subset of key difference components is obtained. This subset is then ranked by importance and sequentially input into a random forest model for training. After cross-validation, a minimum feature set is obtained, and the final random forest model is derived based on this minimum feature set for classifying pear varieties. This invention ensures the reliability of national standard testing methods while effectively reducing the number of testing indicators, achieving high-efficiency and low-cost accurate identification of agricultural products, effectively reducing testing costs, and promoting industry development.

The invention relates to the technical field of Korla bergamot pear detection, and discloses a Korla bergamot pear organic acid content nondestructive detection method, which comprises: collecting a bergamot pear sample with consistent maturity, no damage and complete surface layer; the surface is wiped with absolute ethyl alcohol and then naturally air-dried, only the surface layer is subjected to vacuum freeze drying and smashing, and the integrity of a main body is reserved; according to the method, ICP-MS, HT-IRMS and GC-MS / EMIS are combined with an HS-HPME extraction technology, multi-dimensional detection of mineral elements, isotope contents and ratios and volatile components is carried out, and an isotope and element characteristic database and a regional characteristic volatile substance fingerprint spectrum are constructed; analyzing and screening characteristic indexes through orthogonal partial least squares, and eliminating interference factors; and establishing a linear discrimination model, forming an organic acid content detection and origin identification integrated technical system, and optimizing detection parameters. According to the method, rapid and nondestructive testing and source area traceability are realized, and reliable technical support is provided for quality evaluation of the Korla bergamot pears.

The invention relates to the technical field of intelligent questions and answers, and discloses a pear field question and answer large model construction method and system, and the method comprises the following steps: obtaining text data of a plurality of sub-fields which cover the pear field and are divided according to knowledge attributes, and image data associated with the text data, and carrying out the standardization processing of the text data and the image data; constructing a sub-domain knowledge base set divided according to knowledge attributes, constructing a text index and an image index for each knowledge base, and establishing bidirectional association for the text index and the image index; constructing a multi-model cluster for analyzing the user questions, selecting the sub-field knowledge base with the highest comprehensive selection weight as a target retrieval library, then performing retrieval according to the types of the user questions, and generating an associated knowledge set related to the user questions; and finally, according to the questions of the user and the associated knowledge set, generating answers, so that the related questions in the pear field can be accurately answered.

Pear Blossom Greeting Card

The invention provides a YOLOv5-based Cuiguan pear surface defect detection method and system in the technical field of computer vision and agricultural intellectualization. The method comprises the following steps: S1, acquiring a large number of historical Cuiguan pear images to construct a data set; s2, creating a green-coronal pear defect detection model based on the improved YOLOv5s; s3, training the Cuiguan pear defect detection model through the data set, and performing deployment after training; s4, collecting a real-time Cuiguan pear image to be detected, and inputting the image into the Cuiguan pear defect detection model to obtain a Cuiguan pear defect detection result; and S5, recording a detection log which at least comprises the detection time, the real-time Cuiguan pear image, a Cuiguan pear defect detection result and detection error feedback in real time, and carrying out iterative optimization on the Cuiguan pear defect detection model based on the detection log. The method has the advantage that the accuracy and practicability of surface defect detection of the Cuiguan pears are greatly improved.

1. Name of the design product: picking robot. 2. Use of the design product: the design product is used for picking pears and other fruits. 3. Design points of the design product: in shape. 4. Picture or photo best indicating the design points: perspective view 1.

The present invention discloses a method for rapid detection of pear fruits in a natural environment based on a lightweight Transformer architecture, comprising the following steps: A. Collecting pear fruit images in different environments; B. Making a pear fruit data set; C. Constructing a pear fruit detection model; D. Inputting the image to be recognized into the pear fruit detection model for rapid detection of pear fruits. The present invention can improve the deficiencies of the prior art, reduce the complexity of the detection model, and improve the detection speed and accuracy.

The invention belongs to the technical field of breeding planning, and particularly relates to a large model construction method and system for multi-source data collaboration pear precision breeding planning, and the method comprises the steps: obtaining a multi-source collaboration data set, constructing a five-dimensional incidence matrix, and building a unified correlation basis of multi-dimensional data; constructing a constrained knowledge expression structure and training a constraint consistency reasoning model on the basis, analyzing a user breeding instruction in a constraint driving mode, and generating a combined constraint condition set containing a gene feasible region and a multi-phenotype collaborative target; calling germplasm resources under a cross-domain feature alignment and feature-level desensitization framework, executing phenotype-genotype collaborative screening, parent dynamic matching and multi-generation genetic evolution deduction, and generating breeding planning simulation data; and finally, outputting an optimal breeding scheme through a multi-dimensional evaluation system, and continuously updating the model by utilizing verification data. According to the invention, unification of multi-phenotype balanced synergistic improvement and stable gene transfer is realized, and the breeding efficiency and variety adaptability are remarkably improved.

The invention relates to the field of molecular breeding of Chinese pears, and discloses an SNP (Single Nucleotide Polymorphism) marker related to the fruit peel color of Chinese pears, the SNP marker is located at the No.8 chromosome 4163889 site of a 20-century pear PPYr1.0 genome, the fruit peel color of the Chinese pears is green when the genotype is T / T, and the fruit peel color of the Chinese pears is brown when the nucleotide at the SNP site is C / C or C / T. Compared with the prior art, the SNP molecular marker has the following advantages and effects that genome re-sequencing and whole genome association analysis (GWAS) are utilized to enrich in a gene interval related to the fruit peel color of the Chinese pears, the SNP molecular marker closely linked with the target character is developed, the SNP molecular marker can be effectively used for molecular marker-assisted selective breeding of the Chinese pears, the accuracy of character selection of the fruit peel color of the Chinese pears is improved, and the SNP molecular marker can be applied to the molecular marker-assisted selective breeding of the Chinese pears. The breeding process is accelerated.

The utility model discloses an internet-of-things real-time monitoring device for preventing insect pests of bergamot pears, which relates to the field of insect pest prevention and monitoring and comprises a base and a supporting plate. The lifting mechanism is arranged on the base and comprises a lifting plate movably connected with the supporting plate; the monitoring mechanism is arranged on the base and comprises a trapping assembly and a monitoring assembly which are installed on the two sides of the lifting plate, the trapping assembly comprises two trapping boxes arranged on the two sides of the lifting plate and two trapping lamps arranged on the two trapping boxes respectively, and the monitoring assembly comprises a fixing box installed on the trapping boxes and a camera arranged in the fixing box. According to the internet-of-things real-time monitoring device for bergamot pear insect pest prevention, insects in a bergamot pear orchard are trapped through the trapping lamp and enter the trapping box, the insects are shot through the camera, shooting results are transmitted to the storage device, and then the shooting results are transmitted to the monitoring terminal through the wireless transmitter.

The present application relates to the technical field of fruit screening, and specifically relates to a multi-stage screening and quality detection method for fruit, comprising the following steps: S1, fruit variety identification: collecting multi-angle images, extracting color, texture and contour features, and classifying and outputting variety labels and parameter sets; S2, growth feature mapping: extracting structural key points, matching with variety templates, and generating growth deviation vectors; S3, regional perception analysis: dividing fruit surface regions based on growth deviations, and independently identifying defects and maturity; S4, grade identification: fusing identification results, combining varieties and deviations, and dynamically evaluating comprehensive quality labels; S5, screening instruction generation: converting quality labels into control instructions to drive sorting devices to complete output. The present application realizes integrated and automated processing of intelligent fruit variety identification, accurate regional defect detection and grade screening control, and significantly improves the accuracy and flexibility of fruit screening.

The present invention provides a collaborative prediction method for the sugar content of multiple varieties of pears based on characteristic band selection, which belongs to the field of spectral analysis. The present invention collects spectral data of multiple varieties of pears as training samples, uses the training samples to train a 1D Grad‑CAM model, obtains the Grad‑CAM value L of each spectral band, extracts the common characteristic bands of pears with the same skin color according to the Grad‑CAM value L, and constructs a sugar content prediction model for multiple varieties of pears, which reduces the amount of data and the amount of calculation, improves the versatility, robustness and prediction accuracy of multi-variety prediction, saves resources and time to a great extent, and has broad application prospects. The present invention solves the problem that it is impossible to construct the same sugar content prediction model for different varieties of pears under the influence of internal and external characteristic changes, spectral reflection intensity and characteristic peak changes between different varieties of pears, reduces resource and time consumption, and improves the versatility of the model.

The embodiment of the present application discloses a kind of pear black heart disease nondestructive testing method and equipment, the embodiment of the present application adopts the micro sampling cover of inner cavity volume not more than 30ml, in not more than 8 seconds sealing time, fast collection pear fruit head space gas, obtain the short time dynamic release characteristics of carbon dioxide and ethylene, and combine near infrared spectrum estimated multi-source information such as soluble solids, color and acoustic-mechanical texture, input fusion model is calculated to obtain a black heart early warning index, according to which pear fruit is divided into low, medium, high three risk levels, and linkage sorting mechanism is sorted.The matching equipment includes the micro sampling cover, multi-source sensing head, data fusion and sorting execution unit.The present application overcomes the defects that existing technology cannot obtain fruit physiological dynamic information under high-speed production rhythm, realizes the rapid, accurate, nondestructive early warning and sorting of pear black heart disease risk, effectively reduces storage loss.

The invention discloses a Korla pear recessive damage detection method, and belongs to the technical field of agricultural product detection. According to the method, multi-source detection equipment is adopted to collect appearance, internal structure, mechanical property and physiological index multi-dimensional data of bergamot pears, visual, structural, physical and metabolic signals related to recessive damage are comprehensively captured, a composite detection model fusing deep learning and traditional machine learning is constructed, features are extracted through division of labor of multiple sub-models, and weighted fusion is carried out, so that the detection accuracy of the bergamot pears is improved. The feature fusion layer integrates the output of each sub-model, strengthens the synergistic effect of key damage information, and is matched with weight determination and threshold determination to quickly and accurately recognize the hidden damage of the Korla pear, and further analyzes the damage type, position and degree on the basis of damage recognition, so as to improve the recognition accuracy of the Korla pear hidden damage. A detailed detection report with a tracing function is generated, the basic detection limitation of only judging whether the bergamot pears are damaged is broken through, targeted guidance is provided for follow-up treatment of the bergamot pears, and quality grading and storage scheme optimization are assisted.

1. Name of this design product: Pacifier Storage Bag (Pear). 2. Purpose of this design: For storing pacifiers. 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.

1. Name of the designed product: packing box (pear flower tape). 2. Use of the designed product: packing box. 3. Design points of the designed product: combination of shape and pattern. 4. Picture or photo best indicating the design points: front view.

The invention discloses a pear maturity nondestructive testing method and system based on mixed spectrum multiple tasks, and belongs to the crossing field of nondestructive testing and spectrum analysis technologies. The prediction method comprises the following steps: acquiring spectral reflectivity data of a target pear fruit, and performing three-view preprocessing to generate an original spectrum, a first-order derivative spectrum and a standard normal variable standardized spectrum; inputting the processed spectral data into a pre-trained mainstream pear variety global model, and synchronously outputting predicted values of hardness and soluble solids; and finally, obtaining a corresponding specific judgment threshold according to the variety of the target fruit, and comprehensively judging the maturity by comparing a predicted value with the threshold. According to the method, the advantages of traditional machine learning and deep learning are fused, the mainstream pear variety global model with the cross-variety generalization ability is constructed, the variety specificity threshold value is combined, rapid, lossless and accurate prediction of the maturity of the multi-variety pears is achieved, and the method is suitable for multi-scene application such as orchard harvesting, storage grading and market circulation.

The invention discloses a pear internal quality detection method based on characteristic wavelength and sample screening, which can be applied to a visible / near infrared spectrum detection technology for detecting certain internal quality of pears. The difference value between the mean value of the normal fruit sample set and the mean value of the defective fruit sample set of the screened characteristic wavelength is as large as possible, and the variance of the normal fruit sample set and the defective fruit sample set is as small as possible; according to the method, the characteristic wavelength which contributes greatly to internal quality detection is selected, and samples which deviate greatly from the mean value of the normal fruit sample set and the mean value of the imperfect fruit sample set are screened and removed, so that the data volume of a spectrum is effectively reduced, and a reference thought is provided for related work of internal quality nondestructive detection in the pear sorting process.

The invention relates to the technical field of molecular breeding of Chinese pears, in particular to an SNP (Single Nucleotide Polymorphism) molecular marker related to the peel bottom color of Chinese pears and application of the SNP molecular marker. The SNP site is located at the 4160839th nucleotide of the No.8 chromosome of a Chinese pear genome, and the nucleotide is G or A. The nucleotide polymorphism marker site obtained by the invention can be used for identifying the ground color of the peel of the Chinese pears. The SNP molecular marker disclosed by the invention is an SNP molecular marker which is developed on the basis of whole genome association analysis and is closely linked with the ground color of the pericarp of the Chinese pear. When 74 parts of Chinese pear germplasm resources are subjected to peel ground color identification by using the SNP molecular marker, the result shows that the identification accuracy of the SNP molecular marker on Chinese pears with brown and green peel ground colors is 100%. The method does not need to identify the fruit mature period of the Chinese pears, can accurately and quickly identify the peel bottom color of the Chinese pears in the seedling stage, can improve the breeding efficiency and shorten the identification time, and is suitable for molecular design breeding of the Chinese pears.

The invention relates to the technical field of traditional Chinese medicinal materials, in particular to a detection method for a dual-wavelength fingerprint spectrum of dried pears and a control characteristic chromatogram of the dried pears, and the detection method comprises the following steps: determination of high performance liquid chromatography conditions: detection wavelengths are 280 nm (0-25 min) and 350 nm (26-90 min); preparing a mixed reference substance solution; preparing a dried pear test solution; establishing a dried pear control characteristic spectrum; detecting the quality of the dry pear sample to be detected; and analyzing the quality of the dried pears in combination with a chemometrics analysis method. The dual-wavelength fingerprint spectrum detection method provided by the invention can be used for comprehensively and effectively detecting main chemical components of the dried pears, a scientific evaluation method is provided for quality control of the dried pears, and the method can provide a scientific basis for subsequent development of dried pear medicinal and edible decoction pieces.

The invention provides a wild pear variety identification method and system based on a multi-scale residual network, and relates to the technical field of deep learning algorithms, and the method comprises the steps: obtaining a leaf enhanced image, mixing an original leaf image training set with a generated image after format conversion, carrying out the standardized preprocessing of the mixed image, and obtaining a leaf enhanced image; and inputting the data to the constructed multi-scale residual network for training, and outputting a wild pear variety probability vector. By constructing the combination of the multi-scale residual network and the generative adversarial network, the model can show higher robustness and accuracy when processing diversified leaf images, the deep learning model can automatically extract and learn leaf features, subjectivity brought by a traditional manual feature extraction method is avoided, and the method is suitable for large-scale application. Through the introduction of standardized preprocessing and a spatial attention mechanism, the model can more accurately capture the morphological characteristics of the leaves, thereby remarkably improving the recognition capability of different varieties of wild pears.

The embodiment of the invention discloses a nondestructive testing method and equipment for pear blackheart disease, which adopts a miniature sampling cover with the inner cavity volume of not more than 30ml to quickly collect gas in the head space of a pear within the sealing time of not more than 8 seconds to obtain the short-time dynamic release characteristics of carbon dioxide and ethylene, so as to realize the nondestructive testing of pear blackheart disease. And in combination with multi-source information such as soluble solids, color and acoustic-mechanical texture estimated by a near infrared spectrum, inputting the information into a fusion model to calculate a black core early warning index, dividing the pomes into low, medium and high risk levels, and performing sorting by linkage with a sorting mechanism. The matched equipment comprises the micro sampling cover, a multi-source sensing head and a data fusion and sorting execution unit. According to the method, the defect that the physiological dynamic information of the fruits cannot be obtained under the high-speed production takt in the prior art is overcome, rapid, accurate and lossless early warning and sorting of the pear blackheart disease risk are realized, and the storage loss is effectively reduced.

The present application provides a kind of based on conveyer belt type AI vision's shatian pear online sorting device, its structure includes: vertical column, shock-absorbing base, conveying end, substandard product collection box, good product collection structure, AI vision identification structure, mechanical arm sorter, vertical column is set to the outer edge of shock-absorbing base determines the position of conveying end and AI vision identification structure, mechanical arm sorter, conveying end one end is connected with good product collection structure;The present application is based on the ladder formed by the guide plate and the buffer plate provided by good product collection structure, combined with the further buffering of the rubber material of buffer plate and spliced deceleration block can effectively reduce the output speed of shatian pear and the distance with good product collection box, avoid the secondary damage caused by direct impact drop, combined with the cooperation of edge plate one and edge plate two can cover the edge position, prevent derailment, so as to improve the output safety protection effect and output accuracy of sorting device to shatian pear.

The invention relates to the field of agricultural product detection, and discloses an acoustic vibration method-based pome internal quality nondestructive detection device, which comprises a conveying unit, a detection unit, a sorting unit and a control unit, detected pomes are placed on a conveying tray, a conveyor drives the conveying tray and the pomes to move, and when the pomes move to the detection unit, the sorting unit is used for sorting the pomes; a detection excitation element of the detection unit is in contact with the pome, the detection excitation element generates mechanical vibration by utilizing an inverse piezoelectric effect and transmits vibration to the pome, a detection vibration measurement element is in contact with the other side of the pome, receives the vibration transmitted by the pome, and converts vibration response into an electric signal by utilizing the piezoelectric effect; the control unit receives a detection signal of the detection unit to judge the quality of the detected pears, the detected pears continue to be conveyed to the sorting unit, and the sorting unit picks up the pears through the sorting mechanical arm according to the detection result and conveys the pears into the sorting channel. The invention further provides a nondestructive testing method for the internal quality of the pomes.

The invention discloses an uninterrupted pear netting device, comprising a frame, a mobile frame, a net support, a net conveying mechanism, a storage plate, a lifting mechanism, a first claw mechanism, a cutting knife mechanism, a second claw mechanism and a net, wherein the mobile frame is slidably connected to the vertical slide bar of the frame, at least one lifting mechanism is installed between the mobile frame and the frame, the mobile frame is arranged as a square frame, a net support is installed at the middle position of the square frame, net conveying mechanisms are symmetrically installed on both sides of the net support, the gap between the net support and the net conveying mechanism is adapted to the thickness of the net, the storage plate is installed at the cross center of the frame and is located directly below the mobile frame, the upper and lower ends of the pears to be packaged on the storage plate correspond to the first claw mechanism and the second claw mechanism respectively, the first claw mechanism and the second claw mechanism are installed on the frame, and the cutting knife mechanism is installed on the frame and is located below the first claw mechanism. The invention automatically completes the uninterrupted set operation through reasonable configuration of the structure, does not damage the surface of the fruit, has a stable working state and high efficiency, and saves labor costs.