34 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.

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.

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 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 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 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 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 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 provides a pear picking robot binocular positioning method based on YOLO-CDS depth detection network and RAFT-Stereo cooperation, and aims to solve the problems that obstacles such as iron wires and ropes in a modern orchard affect pear recognition and a semi-global matching (SGM) algorithm in a binocular vision positioning system is insufficient in feature matching robustness in a complex scene. A YOLO-CDS model is introduced, and a C2f-EMBC module, a DySample and a Shape-IoU loss function are utilized, so that the feature extraction and bounding box regression precision is improved; rAFT-Stereo is adopted to replace an SGM algorithm, so that the robustness of parallax estimation is enhanced; and the depth value is optimized in combination with an IQR (quartile distance), so that the positioning precision is improved. Experiments show that the average accuracy of the method on various pear data sets is 97.5%, the average relative error of pear coordinate positioning is smaller than 3%, the positioning success rate in an orchard picking test is 100%, the picking success rate is 91.93%, powerful technical support is provided for pear picking robot application, and the method has important application value and popularization prospects.

The application discloses a pear internal quality prediction method based on an autoencoder and multi-task learning, and belongs to the technical field of nondestructive detection of agricultural products. The method comprises the following steps: obtaining sample-level and pixel-level visible / near-infrared hyperspectral data sets of pears; constructing a denoising autoencoder, and migrating the encoder part of the pre-trained denoising autoencoder to sample-level average spectrum modeling; and constructing a DAE_multi multi-task network, which comprises the encoder part of the denoising autoencoder and a multi-task prediction module, the multi-task prediction module comprising a shared feature layer, a soluble solids content prediction head and a hardness prediction head, the shared feature layer receiving deep spectral features output by the encoder, and the soluble solids content prediction head and the hardness prediction head receiving outputs of the shared feature layer respectively to output a soluble solids content prediction value and a hardness prediction value. The application provides a feasible modeling idea for nondestructive detection of the soluble solids content and hardness of pears under small sample conditions.

This invention provides a method for predicting pear sugar content based on interpretable multimodal feature fusion. It involves collecting spectral and image data of pear samples to construct a multimodal dataset, then building a multimodal feature-level fusion model based on a convolutional neural network architecture to achieve multimodal feature extraction and fusion, outputting a predicted sugar content value. Next, it calculates and analyzes the intermodal interaction strength to obtain feature combinations of image and spectral features with high interaction strength, and restores the spectral features back to the original input data to identify the key wavelength regions most important for sugar content prediction. This invention effectively integrates visible-near-infrared spectral data and fruit peel image data through a feature-level fusion strategy, overcoming the limitations of a single modality. It also introduces an interpretable method for calculating intermodal interaction strength, quantifying the feature interactions between modalities from a theoretical perspective, revealing the cross-modal correlation mechanism of the model prediction, and improving the accuracy, robustness, and transparency of non-destructive testing of pear sugar content.

The application discloses a kind of pear defect classification methods based on deep network under small sample set, as follows: step S1: the pear image of different defect types is collected, and the defect image database is established;Step S2: the defect pear image is divided into image block, and is classified according to defect;Image defect is made into label, forms mat file, as small sample dataset label;Step S3: the multiple features of each image block are extracted, and are normalized;Step S4: the multiple features of image block are merged into mat file in order, form feature vector;Step S5: repeat step S1 to step S4 to extract the feature vector of each image block, form feature matrix, and merge with label data into small sample dataset;According to the proportion of 8:2, the dataset is divided into training dataset and test dataset;Step S6: construct deep network to train dataset, realize feature fusion and selection;Step S7: the multiple features of image block are extracted, and the trained network is used to intelligently classify features.

The invention discloses a variety pear identifying and sorting device and method based on a deep transfer learning algorithm. The device comprises a conveying and rotating mechanism, a fusion sensing unit, a processing and control unit and a sorting executing mechanism. According to the method, a dynamic anti-interference optical coherence tomography sensor is used for collecting an original one-dimensional interference spectrum signal sequence in the pear movement process, and the signal originally fuses the surface appearance and the shallow internal characteristics; then, directly inputting the processed signal into a variety identification neural network model which is pre-trained and finely adjusted by adopting a deep transfer learning algorithm for end-to-end analysis, and outputting a variety identification result; and finally, the sorting action is controlled according to an identification result. According to the method, the multi-source data synchronization problem is radically solved through a single-sensor scheme, the small sample training bottleneck is overcome by utilizing transfer learning, and high-precision, high-robustness and low-cost automatic identification and sorting of pear varieties in a real production line high-speed dynamic environment are realized.

1. Name of the designed product: toy (not pear). 2. Use of the designed product: for playing. 3. Design points of the designed product: in shape. 4. Picture or photo that best shows the design points: perspective view. 5. Top view has been shown in the perspective view, and the top view is omitted.

The application discloses a kind of molecular markers for detecting pear chilling requirement character and its application, belong to plant molecular marker technical field.The molecular marker is the single nucleotide polymorphism C>A of DAM1 gene CDS region shown in SEQ ID NO.1 216th position, utilize the SNP as detection target point and be applied to detect pear chilling requirement character.The application first discloses that the SNP is significantly associated with pear low chilling requirement character, by detecting the single nucleotide polymorphism of this site, the chilling requirement character of different pear varieties can be effectively identified, which has important significance for low chilling requirement pear variety identification and new variety breeding, shortening breeding period and improving breeding efficiency.The application also provides a method for detecting low chilling requirement pear variety / strain using KASP technology, which can efficiently detect the single nucleotide polymorphism of specific gene site of test variety / hybrid offspring.The method is fast, simple, specific and can realize high-throughput detection.

The application discloses a pear variety identification method and application thereof, and comprises the following steps: S1, acquiring an original pear fruit image, and constructing an occlusion sample set and a non-occlusion sample set after data processing; S2, performing contour extraction on the pear fruit images in the occlusion sample set and the non-occlusion sample set, and constructing an occlusion identification data set based on the extracted contours; S3, constructing an occlusion identification model based on an SVM, and training the occlusion identification model through the occlusion identification data set; S4, constructing a pear variety identification model based on a convolutional neural network, and training the pear variety identification model through the non-occlusion sample set; S5, inputting a pear fruit image to be identified into the occlusion identification model after contour extraction, identifying whether the pear fruit image is non-occluded, and if yes, performing pear variety identification through the pear variety identification model, and if not, performing pear variety identification based on a sliding window and the pear variety identification model. The application can quickly and accurately identify different varieties of pears.

The present application belongs to the technical field of breeding planning, and particularly relates to a method and system for constructing a large model for precise pear breeding planning based on multi-source data collaboration, which obtains a multi-source collaborative data set and constructs a five-dimensional correlation matrix to establish a unified correlation basis for multi-dimensional data; based on this, a constraint type knowledge expression structure is constructed and a constraint consistency reasoning model is trained to analyze user breeding instructions in a constraint driven manner, generating a joint constraint condition set containing a gene feasible region and a multi-phenotype collaborative target; under a cross-domain feature alignment and feature-level desensitization framework, germplasm resources are called to perform phenotype-genotype collaborative screening, parent dynamic matching and multi-generation genetic evolution deduction to generate breeding planning simulation data; finally, the optimal breeding scheme is output through a multi-dimensional evaluation system, and the model is continuously updated using verification data. The present application realizes the unity of multi-phenotype balanced collaborative improvement and gene stable transmission, significantly improving breeding efficiency and variety adaptability.

The invention discloses a pear germplasm molecular identity card construction method developed based on a pear germplasm resource structure variation molecular marker, which comprises the following steps: S1, acquiring re-sequencing data of pear germplasm, screening to obtain a whole genome SV, and only retaining a variation site SV on a chromosome; s2, obtaining a specific SV site combination; s3, adding 300bp flanking sequences to the upstream and downstream of the obtained SV site, and designing primers for the SV sequences with the flanking sequences; s4, carrying out PCR (Polymerase Chain Reaction) amplification on each pair of designed SV upstream and downstream primers, and counting amplification and agarose gel electrophoresis results; and S5, obtaining a molecular identity card, and drawing a variety two-dimensional code by using a qrcode packet according to the molecular identity card code. The invention provides a complete set of SV site detection primers for pear variety research, can be used for pear planting sample identification and pear variety authenticity identification, and is simple and convenient to operate, low in cost and high in efficiency.

The invention discloses a pome internal quality detection device and method based on non-contact instantaneous air jet excitation and laser Doppler vibration measurement, and relates to the technical field of pome internal quality detection. Wherein the internal quality comprises but is not limited to hardness, SSC and internal defects; the invention discloses a pome internal quality detection device based on non-contact instantaneous air jet excitation and laser Doppler vibration measurement. The pome internal quality detection device comprises a pome conveying system, a pome placement module, an air jet impact excitation system, a laser Doppler vibration measurement system, an air jet impact excitation controller and air jet control and signal analysis software, air jet impacts and excites a pome sample, a laser Doppler vibration meter senses pome vibration response signals, the obtained signals are processed, and then a deep learning model is established for judgment. By means of the mode, the device can rapidly and accurately detect the internal quality of the pear fruits in a non-contact mode, and has great significance in improving the commodity rate and market competitiveness of the pear fruits and promoting rapid and healthy development of the pear fruit industry.