7results about How to "Improve identification accuracy" patented technology

This invention relates to a rapid identification method Lactobacillus plantarum The method for identifying strains, belonging to the field of species and strain identification, is based on the differences in three DNA barcode sequences. Lactobacillus plantarum CGMCC NO: 26171 strain from Lactobacillusplantarum This method allows for rapid identification from other strains of the same species. Compared to traditional morphological identification methods, the standard gene sequence obtained is beneficial for the molecular identification of *Lactobacillus plantarum* strains, effectively shortening the identification time. Three pairs of DNA barcoding primers enable specific amplification of the test strain, allowing for rapid identification. Lactobacillus plantarum strains.

The invention relates to a part identification method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring a first image comprising a to-be-identified part; inputting the first image into a first neural network to obtain the position of the to-be-identified part in the first image; segmenting the image according to the position of the to-be-identified part in the first image to obtain a second image including the to-be-identified part; inputting the second image into a second neural network to obtain a preliminarily identified part number; according to the preliminarily identified part number, judging whether the to-be-identified part has a similar part, and if the to-be-identified part has the similar part, inputting the second image into a third neural network to obtain a finally identified part number; and if the similar part does not exist, taking the preliminarily identified part number as the finally identified part number. Therefore, by adopting a multi-neural network collaborative identification strategy, the identification capability of small targets is enhanced, and the identification accuracy of similar parts is also remarkably improved.

This invention relates to the field of spectral analysis and identification of genuine and counterfeit Chinese medicinal materials, and discloses a method for identifying counterfeit Cordyceps sinensis based on multispectral fusion. This method first collects multispectral image data of samples under standard conditions; eliminates equipment and environmental noise through standardized preprocessing to establish a reliable data foundation; then, it fuses extracted spectral derivative features and spatial texture features to construct a lightweight neural network identification model. Its core innovation lies in introducing a physical interference stripping model, which can actively correct spectral distortion caused by moisture and mold, obtaining intrinsic spectra to improve model robustness; and through dynamic verification and incremental learning mechanisms, the model can continuously evolve, effectively identifying new counterfeiting methods. Finally, the entire model is integrated into a multispectral portable terminal, achieving rapid screening within seconds and outputting a complete report including authenticity determination, type inference, and confidence level. Ultimately, this solves the industry problems of low efficiency, reliance on experience, and inability to be applied on-site by traditional methods.

The application provides a face recognition system and method based on AI technology, and belongs to the field of image recognition.The system is suitable for security monitoring of smart communities or smart villages, and effectively improves the implementation efficiency and accuracy of security protection by realizing accurate face recognition and behavior analysis.The system mainly utilizes an incremental convolutional neural network and a sparse autoencoder.The whole system has the characteristics of stability, rapidity and accuracy, and can be widely used in the fields of security monitoring of smart communities and smart villages.The application solves some main problems in the application of face recognition technology, and provides a new method for effectively training on large-scale and dynamic data sets.

This invention discloses a TaqMan real-time fluorescent PCR detection primer and probe set, kit, and detection method for Cuscuta pentaphyllum. The primer and probe set includes: an upstream primer C. pan F, whose nucleotide sequence is shown in SEQ ID NO.1; a downstream primer C. pan R, whose nucleotide sequence is shown in SEQ ID NO.2; and a TaqMan probe C. pan P, whose nucleotide sequence is shown in SEQ ID NO.3. The primers and probe target specific fragments of the rbcL gene sequence of Cuscuta pentaphyllum, achieving specific recognition of Cuscuta pentaphyllum through sequence complementarity matching. This avoids false positive results caused by cross-reactions with closely related species and host plants at the molecular targeting level, effectively solving the problem of difficulty in distinguishing closely related species in traditional morphological identification. Compared with traditional morphological identification and conventional PCR, this invention also features high detection efficiency, short processing time, simple operation, and wide applicability. It is suitable for precise identification in professional laboratories and can also meet the practical applications in grassroots quarantine and field monitoring scenarios.

The application discloses a probe primer group, a kit and a detection method for detecting European dodder, wherein the probe primer group comprises an upstream primer Ce-rbcL-F, a downstream primer Ce-rbcL-R and a TaqMan probe Ce-rbcL-P; the nucleotide sequence of the upstream primer Ce-rbcL-F is shown as SEQ ID NO. 1; the nucleotide sequence of the downstream primer Ce-rbcL-R is shown as SEQ ID NO. 2; and the nucleotide sequence of the TaqMan probe Ce-rbcL-P is shown as SEQ ID NO. 3. The primer probe group is designed based on a specific region of the rbcL gene of European dodder, and specific identification of European dodder can be realized by combining an optimized real-time fluorescent PCR reaction system and conditions, namely, the application has the characteristics of rapidness, strong specificity, high identification accuracy, effectively solves the problem that near relatives are difficult to distinguish in traditional morphological identification, and avoids false positive misjudgment. Compared with traditional morphological identification and conventional PCR, the application also has the characteristics of high detection efficiency, short time consumption, simple operation and wide applicability, and can be applied to not only professional laboratory accurate identification, but also practical application of scenarios such as primary quarantine and field monitoring.

The invention discloses an automatic fritillaria identification system and method based on hierarchical deep learning, and relates to the technical field of intelligent detection of traditional Chinese medicinal materials. The system comprises a hardware integration and image acquisition module, an image processing and target positioning module and a grading identification algorithm module. Wherein ConvNeXt-is adopted for the category pairs of the Songbei and the Pingbei, which are relatively low in similarity; carrying out classification and identification through a Tiny network; the method comprises the following steps of: performing classification identification by adopting a double-branch ConvNeXt model aiming at furnace shell and illite type pairs with relatively high similarity, respectively extracting local microscopic texture and overall macroscopic morphological characteristics by adopting texture branches and morphological branches which are constructed in parallel, and performing optimization by adopting a joint loss function fusing tuple marginal penalty terms so as to enhance the inter-class discrimination capability. According to the invention, full-process automation from sample feeding to identification result output is realized, the identification precision is high, the speed is high, and the method is suitable for rapid nondestructive batch detection in the circulation link of traditional Chinese medicinal materials.