Lotus root parentage molecular identification numerical control intelligent system

Abstract

The application discloses a lotus root parentage molecular identification numerical control intelligent system, and belongs to the field of molecular biology and agricultural biotechnology engineering, which comprises a nucleic acid automatic extraction module, which is used for automatically extracting nucleic acid samples from lotus root samples to be detected; an amplification reaction module, which is used for carrying out amplification reaction with the nucleic acid samples as templates; a separation and detection module, which is used for separating and detecting amplification products and collecting detection images; and a numerical control intelligent analysis module, which automatically converts the detection images into standardized molecular marker maps through an artificial intelligence image recognition algorithm, and compares and analyzes the molecular marker maps with a lotus root variety reference database to obtain lotus root parentage identification results. The lotus root parentage identification is realized in a full-process automatic, intelligent and high-throughput manner through the organic combination of molecular marker technology, automatic hardware and an artificial intelligence image recognition algorithm. Compared with a traditional artificial identification method, the identification period is greatly shortened, the labor cost is significantly reduced, and the identification accuracy is significantly improved.

Description

Technical Field

[0001] This invention relates to the fields of molecular biology and agricultural biotechnology engineering, specifically a numerical control intelligent system for molecular identification of lotus root kinship. Background Technology

[0002] Lung lotus (Nelumbo nucifera Gaertn.) is an important aquatic economic crop in my country with a long history of cultivation and abundant germplasm resources. With the rapid development of the lotus root industry, the demand for variety identification, phylogenetic analysis, and seedling purity testing is increasingly urgent. Traditional lotus root variety identification mainly relies on morphological markers and agronomic trait observation. This method is time-consuming, labor-intensive, and easily affected by environmental factors and plant development stages, making it difficult to guarantee accuracy and reliability. In recent years, DNA molecular marker technology has been widely used in crop variety identification and phylogenetic analysis due to its advantages such as being unaffected by environmental conditions, high polymorphism, and accurate detection. SSR (Simple Sequence Repeat) markers and InDel (Insertion / Deletion) markers are two commonly used molecular marker types. Studies have shown that SSR markers can effectively distinguish lotus root varieties, with polymorphic band ratios reaching 100%; InDel markers also show good variety differentiation ability, distinguishing 15 lotus root varieties using 7 pairs of InDel primers.

[0003] However, existing molecular marker identification methods still have the following shortcomings: (1) The operation process is cumbersome, requiring manual switching between each step from DNA extraction and PCR amplification to electrophoresis detection, which is time-consuming and labor-intensive; (2) The interpretation of electrophoretic patterns depends on human experience, which is highly subjective, inefficient, and prone to errors; (3) There is a lack of standardized data analysis procedures and variety fingerprint databases, making it difficult to achieve high-throughput and standardized paternity identification. Although there have been studies involving the application of convolutional neural networks in electrophoretic pattern classification, there are no reports of integrating molecular marker technology, automated hardware, and artificial intelligence algorithm systems for lotus root paternity identification.

[0004] Therefore, developing a system that can automate and intelligently identify the parentage of lotus roots throughout the entire process has significant industrial application value. Summary of the Invention

[0005] The purpose of this invention is to provide a numerical control intelligent system for molecular identification of lotus root parentage, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A numerically controlled intelligent system for molecular identification of lotus root parentage includes:

[0008] An automated nucleic acid extraction module is used to automatically extract nucleic acid samples from lotus root samples to be tested.

[0009] The amplification reaction module is connected to the automated nucleic acid extraction module and has a built-in lotus root-specific molecular marker primer set, which is used to perform an amplification reaction using the nucleic acid sample as a template to obtain amplification products.

[0010] The separation and detection module is connected to the amplification reaction module and is used to separate and detect the amplification products and acquire detection images containing molecular marker feature information.

[0011] The numerical control intelligent analysis module is connected to the separation and detection module. It has a built-in lotus root variety reference database. It automatically converts the detected image into a standardized molecular marker spectrum through artificial intelligence image recognition algorithm, and compares and analyzes it with the lotus root variety reference database to obtain the lotus root kinship identification results.

[0012] Furthermore, the lotus root-specific molecular marker primer set is one or more of the following: SSR marker primer set, InDel marker primer set, EST-SSR marker primer set, SNP marker primer set, and MNP marker primer set.

[0013] Furthermore, the SSR-labeled primer set includes primer pairs for microsatellite markers NL-28, NL-P8, NL-35, NL-1, NL-P9, and NL-47; the nucleotide sequences of the primer pairs for NL-28 are shown in SEQ ID NO:1 and SEQ ID NO:2, respectively; the nucleotide sequences of the primer pairs for NL-P8 are shown in SEQ ID NO:3 and SEQ ID NO:4, respectively; the nucleotide sequences of the primer pairs for NL-35 are shown in SEQ ID NO:5 and SEQ ID NO:6, respectively; the nucleotide sequences of the primer pairs for NL-1 are shown in SEQ ID NO:7 and SEQ ID NO:8, respectively; the nucleotide sequences of the primer pairs for NL-P9 are shown in SEQ ID NO:9 and SEQ ID NO:10, respectively; and the nucleotide sequences of the primer pairs for NL-47 are shown in SEQ ID NO:11 and SEQ ID NO:12, respectively.

[0014] Further, the InDel-labeled primer set includes primer pairs of InD38, InD61, InD84, InD92, InD105, InD117, and InD128; the nucleotide sequences of the primer pairs of InD38 are shown in SEQ ID NO:13 and SEQ ID NO:14, respectively; the nucleotide sequences of the primer pairs of InD61 are shown in SEQ ID NO:15 and SEQ ID NO:16, respectively; the nucleotide sequences of the primer pairs of InD84 are shown in SEQ ID NO:17 and SEQ ID NO:18, respectively; the nucleotide sequences of the primer pairs of InD92 are shown in SEQ ID NO:19 and SEQ ID NO:20, respectively; the nucleotide sequences of the primer pairs of InD105 are shown in SEQ ID NO:21 and SEQ ID NO:22, respectively; the nucleotide sequences of the primer pairs of InD117 are shown in SEQ ID NO:23 and SEQ ID NO:24, respectively; and the nucleotide sequences of the primer pairs of InD128 are shown in SEQ ID NO:25 and SEQ ID NO:26, respectively. As shown in ID NO:26.

[0015] Furthermore, the amplification reaction module employs any one of PCR amplification technology, LMTIA isothermal amplification technology, or multiplex PCR amplification technology for amplification.

[0016] Furthermore, the artificial intelligence image recognition algorithm includes a neural network model trained on a dataset of lotus root detection maps labeled with molecular marker features, used to automatically identify the position, intensity and / or size of the marker signal in the detection image and generate a standardized molecular marker map; the neural network model includes a convolutional neural network model.

[0017] Furthermore, the molecular marker map is a DNA fingerprint map; the separation and detection method is electrophoretic separation, the corresponding detection image is an electrophoretic map image, and the labeling signal is an electrophoretic band; the convolutional neural network model has an automatic recognition accuracy of ≥95% for the position, size, and intensity of the electrophoretic bands, and the normalization conversion error rate of the DNA fingerprint map is ≤2%.

[0018] Furthermore, the lotus root variety reference database contains variety identity information, molecular marker map characteristics, and phylogenetic data of lotus root germplasm resources; the lotus root variety reference database is a local database or a cloud database, supporting incremental updates and remote sharing.

[0019] Another objective of this invention is to provide a lotus root parentage molecular identification device, including a chassis body, a human-machine interface, and the aforementioned lotus root parentage molecular identification CNC intelligent system.

[0020] Another object of the present invention is to provide an application of the above-mentioned CNC intelligent system for lotus root parent molecular identification or the above-mentioned lotus root parent molecular identification equipment in lotus root parent molecular identification, wherein the method for lotus root parent molecular identification includes the following steps:

[0021] Nucleic acid samples were automatically extracted from lotus root samples to be tested using an automated nucleic acid extraction module.

[0022] Using the lotus root-specific molecular marker primer set built into the amplification reaction module, the nucleic acid sample is used as a template to perform the amplification reaction and obtain the amplification product.

[0023] The amplification products are separated and detected by the separation and detection module, and detection images containing molecular marker feature information are acquired.

[0024] The detection image is automatically converted into a standardized molecular marker spectrum by the artificial intelligence image recognition algorithm of the CNC intelligent analysis module, and compared with the lotus root variety reference database to obtain the lotus root kinship identification results.

[0025] The intelligent CNC system for lotus root parentage molecular identification provided by this invention organically combines molecular marker technology, automated hardware, and artificial intelligence image recognition algorithms. This achieves full-process automation, intelligence, and high throughput for lotus root parentage identification. Compared to traditional manual identification methods, the identification cycle is significantly shortened, labor costs are significantly reduced, and identification accuracy is significantly improved. This invention can be widely applied in fields such as genetic diversity analysis of lotus root germplasm resources, variety authenticity identification, variety verification, parentage tracing, offspring kinship identification, seedling purity testing, and new variety rights protection. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of the intelligent numerical control system for molecular identification of lotus root parentage provided in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the structure of the lotus root parentage molecular identification device provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0029] like Figure 1As shown, in one embodiment of the present invention, to address the technical problems of existing lotus root parentage identification techniques, such as cumbersome operation, low efficiency, and strong subjectivity, a numerical control intelligent system for lotus root parentage molecular identification is provided, specifically including:

[0030] The automated nucleic acid extraction module 10 is used to automatically extract nucleic acid samples (such as genomic DNA) from lotus root samples to be tested.

[0031] The amplification reaction module 20 is connected to the automated nucleic acid extraction module 10 and has a built-in lotus root-specific molecular marker primer set, which is used to perform an amplification reaction using the nucleic acid sample as a template to obtain amplification products.

[0032] The separation and detection module 30 is connected to the amplification reaction module 20 and is used to separate and detect the amplification products and acquire detection images containing molecular marker feature information.

[0033] The numerical control intelligent analysis module 40 is connected to the separation and detection module 30. It has a built-in lotus root variety reference database. It automatically converts the detected image into a standardized molecular marker spectrum through an artificial intelligence image recognition algorithm, and compares and analyzes it with the lotus root variety reference database to obtain the lotus root kinship identification results.

[0034] Among them, the lotus root-specific molecular marker primer set is one or more of the following: SSR marker primer set, InDel marker primer set, EST-SSR marker primer set, SNP marker primer set and MNP marker primer set.

[0035] In this embodiment of the invention, by organically combining molecular marker technologies such as SSR / InDel, automated CNC hardware, and artificial intelligence image recognition algorithms, the entire process of lotus root parentage identification has been automated, intelligent, and high-throughput for the first time. Compared with traditional manual identification methods, the identification cycle has been shortened from 3-5 days to 4-6 hours, labor costs have been reduced by more than 80%, and the identification accuracy has been increased to over 98%. This invention can be widely applied to fields such as lotus root germplasm resource identification, variety authenticity verification, parentage tracing, seedling purity testing, and new variety rights protection.

[0036] In a preferred embodiment of the present invention, the above-mentioned SSR marker primer set includes primer pairs for microsatellite markers NL-28, NL-P8, NL-35, NL-1, NL-P9, and NL-47; the nucleotide sequences of the primer pairs for NL-28 are shown in SEQ ID NO:1 and SEQ ID NO:2, respectively; the nucleotide sequences of the primer pairs for NL-P8 are shown in SEQ ID NO:3 and SEQ ID NO:4, respectively; the nucleotide sequences of the primer pairs for NL-35 are shown in SEQ ID NO:5 and SEQ ID NO:6, respectively; the nucleotide sequences of the primer pairs for NL-1 are shown in SEQ ID NO:7 and SEQ ID NO:8, respectively; the nucleotide sequences of the primer pairs for NL-P9 are shown in SEQ ID NO:9 and SEQ ID NO:10, respectively; and the nucleotide sequences of the primer pairs for NL-47 are shown in SEQ ID NO:11 and SEQ ID NO:12, respectively. Using the above 6 pairs of SSR marker primers, PCR amplification and polyacrylamide gel electrophoresis were performed on 10 lotus varieties. A total of 15 polymorphic bands were amplified, with a polymorphic band ratio of 100%, which can completely distinguish the 10 lotus varieties tested.

[0037] In a preferred embodiment of the present invention, the InDel-labeled primer set includes primer pairs of InD38, InD61, InD84, InD92, InD105, InD117, and InD128; the nucleotide sequences of the primer pairs of InD38 are shown in SEQ ID NO:13 and SEQ ID NO:14, respectively; the nucleotide sequences of the primer pairs of InD61 are shown in SEQ ID NO:15 and SEQ ID NO:16, respectively; the nucleotide sequences of the primer pairs of InD84 are shown in SEQ ID NO:17 and SEQ ID NO:18, respectively; the nucleotide sequences of the primer pairs of InD92 are shown in SEQ ID NO:19 and SEQ ID NO:20, respectively; the nucleotide sequences of the primer pairs of InD105 are shown in SEQ ID NO:21 and SEQ ID NO:22, respectively; the nucleotide sequences of the primer pairs of InD117 are shown in SEQ ID NO:23 and SEQ ID NO:24, respectively; and the nucleotide sequences of the primer pairs of InD128 are shown in SEQ ID NO:19 and SEQ ID NO:20, respectively. As shown in NO:25 and SEQ ID NO:26, the above 7 pairs of InDel-labeled primers were used to amplify and detect 15 lotus root varieties, and all 15 varieties could be completely distinguished.

[0038] In a preferred embodiment of the present invention, the amplification reaction module 20 can be amplified using any one of PCR amplification technology, LMTIA (Ladder-type Melting Temperature Isothermal Amplification) nucleic acid isothermal amplification technology, or multiplex PCR amplification technology. Among them, LMTIA technology has the advantages of fast detection speed, high specificity, and high sensitivity, which can further improve amplification efficiency.

[0039] In a preferred embodiment of the present invention, the above-mentioned artificial intelligence image recognition algorithm includes a neural network model trained on a dataset of lotus root detection spectral images labeled with molecular marker features, used to automatically identify the position, intensity and / or size of the marker signal in the detection image and generate a standardized molecular marker spectral; preferably, the neural network model is a convolutional neural network model, for example, using a multi-head convolutional neural network (MHCNN) combined with data augmentation technology, which can effectively improve the recognition accuracy of low intensity peaks in electrophoresis patterns.

[0040] Preferably, the molecular marker pattern is a DNA fingerprint pattern; the separation and detection method is electrophoresis separation, the corresponding detection image is an electrophoretic pattern image, and the labeling signal is an electrophoretic band; the convolutional neural network model has an automatic recognition accuracy of ≥95% for the position, size, and intensity of the electrophoretic bands, and the normalization conversion error rate of the DNA fingerprint pattern is ≤2%.

[0041] Furthermore, the lotus root variety reference database contains variety identity information, molecular marker map characteristics, and phylogenetic data of lotus root germplasm resources; the lotus root variety reference database is a local database or a cloud database, supporting incremental updates and remote sharing.

[0042] like Figure 2 As shown, in another embodiment of the present invention, a lotus root parentage molecular identification device is also provided, including a chassis body 1, a human-machine interface 2, and the above-mentioned system 3; in addition, it may also include a numerical control processor 4, which is electrically connected to each module in the above-mentioned system 3, and is used to control the operating parameters of each module according to a preset program.

[0043] The human-machine interface 2 is located on the surface of the chassis body 1 and is connected to the CNC processor 4 and the aforementioned system 3. It is used to input instructions and display the evaluation results.

[0044] In practical applications, the automated nucleic acid extraction module 10 can employ a fully automated nucleic acid extraction device based on magnetic bead extraction or centrifugal column extraction, with a single batch extraction throughput of 1-96 samples; the amplification reaction module 20 can include a precise temperature control device with a temperature control accuracy of ±0.1℃; the separation and detection module 30 can include a gel electrophoresis device and a CCD image acquisition device with an image acquisition resolution of ≥12 million pixels. The above equipment can be an integrated device or a combined device composed of independent functional modules, wherein the overall dimensions of the integrated device are ≤800mm×600mm×500mm.

[0045] In another embodiment of the present invention, the application of the above-mentioned CNC intelligent system for lotus root parent molecular identification or the above-mentioned lotus root parent molecular identification equipment in lotus root parent molecular identification is also provided. Specifically, the method for lotus root parent molecular identification includes the following steps:

[0046] S1. Nucleic acid samples are automatically extracted from the lotus root samples to be tested using the automated nucleic acid extraction module 10;

[0047] S2. Using the lotus root-specific molecular marker primer set built into the amplification reaction module 20, the nucleic acid sample is used as a template to perform an amplification reaction to obtain the amplification product.

[0048] S3. The amplification product is separated and detected by the separation and detection module 30, and a detection image containing molecular marker feature information is acquired.

[0049] S4. The detection image is automatically converted into a standardized molecular marker spectrum by the artificial intelligence image recognition algorithm of the numerical control intelligent analysis module 40, and compared and analyzed with the lotus root variety reference database to obtain the lotus root kinship identification results.

[0050] Unless otherwise specified, all raw materials used in the following embodiments are commercially available products and can be purchased through commercial channels. The invention will be described in detail below through specific embodiments in practical applications.

[0051] Example 1: This example describes the screening of SSR marker primer sets and the construction of fingerprint patterns, as detailed below:

[0052] 1. Materials: The test materials consisted of 10 lotus cultivars, including Zhonglian No. 1, Zhonglian No. 2, Zhongxianlian No. 3, Zhongxianlian No. 4, Tianxianlian No. 5, Tianxianlian No. 6, Zhonglian No. 7, Zhonglian No. 8, Zhonglian No. 9, and Zhonglian No. 10, all of which were collected from the lotus germplasm resource nursery of Hubei Zhonglian Technology Co., Ltd.

[0053] 2. Genomic DNA extraction: Young lotus leaves were collected, and genomic DNA was extracted using a modified CTAB method. DNA quality was assessed by 1% agarose gel electrophoresis, and DNA concentration was measured by UV spectrophotometry. The DNA was then diluted to 50 ng / μL for later use.

[0054] 3. SSR marker primer set screening: 50 pairs of SSR primers were initially screened from the lotus root genome SSR library. After initial polymorphism screening, 6 pairs of core primers with high polymorphism, clear bands and good reproducibility were finally selected, including microsatellite markers NL-28, NL-P8, NL-35, NL-1, NL-P9 and NL-47. Their primer sequences are shown in SEQ ID NO.1 to SEQ ID NO.12 (5'→3').

[0055] 4. PCR amplification:

[0056] PCR reaction system (20 μL): 10×PCR Buffer 2 μL, MgCl2 (25 mM) 1.2 μL, dNTPs (2.5 mMeach) 1.6 μL, upstream primer (10 μM) 0.8 μL, downstream primer (10 μM) 0.8 μL, Taq DNA polymerase (5 U / μL) 0.2 μL, template DNA (50 ng / μL) 1 μL, ddH2O to 20 μL.

[0057] PCR amplification program: 94℃ pre-denaturation for 5 min; 94℃ denaturation for 30 s, annealing (temperature set according to the Tm value of each primer) for 30 s, 72℃ extension for 30 s, for a total of 35 cycles; final extension at 72℃ for 7 min.

[0058] 5. Electrophoresis detection: PCR amplification products were separated by 6% polyacrylamide gel electrophoresis, silver stained for color development, and photographed and recorded using a gel imaging system.

[0059] 6. Results: The six pairs of SSR marker primers amplified 15 polymorphic bands in 10 lotus varieties, with a polymorphic band rate of 100%. The DNA fingerprints of each variety were unique, and the variety differentiation rate reached 100%, indicating that the SSR marker primers can be used for accurate identification of lotus varieties.

[0060] Example 2: This example describes the screening of InDel-labeled primer sets and the construction of fingerprint patterns, as detailed below:

[0061] 1. Materials: The test materials consisted of 15 representative lotus root varieties, including the Zhonglian series, Zhongxianlian series, and Tianxianlian series, which were sourced from the lotus root germplasm resource nursery of Hubei Zhonglian Technology Co., Ltd.

[0062] 2. Genomic DNA extraction: Same as in Example 1.

[0063] 3. InDel-labeled primer set screening: Using the self-developed lotus root high-polymorphism InDel marker, 7 pairs of primer sets with high polymorphism and clear bands were obtained through preliminary screening, including InD38, InD61, InD84, InD92, InD105, InD117 and InD128, and their primer sequences are shown in SEQ ID NO.13 to SEQ ID NO.26 (5'→3').

[0064] 4. PCR amplification and electrophoresis detection: Same as in Example 1.

[0065] 5. Results: Seven pairs of InDel marker primers detected 28 allelic variations in 15 lotus root varieties, with an average of 4.0 allelic variations per locus. The polymorphism information content (PIC) values ​​ranged from 0.38 to 0.72, with an average PIC value of 0.56. Cluster analysis showed that the seven pairs of InDel marker primers could completely distinguish all 15 varieties, indicating that these InDel marker primers can be used for lotus root variety identification and phylogenetic analysis.

[0066] Example 3: This example is an experiment on the genetic diversity analysis of lotus root germplasm resources, as detailed below:

[0067] 1. Materials: The test materials consisted of 92 lotus germplasm resources, including 82 cultivated varieties (lotus root type, seed lotus type, and flower lotus type) and 10 wild lotus germplasm. The samples were planted at the Hubei Zhonglian Technology Company base in Shahu Town, Xiantao City, Hubei Province.

[0068] 2. Methods: Fifty pairs of polymorphic SSR markers selected in Example 1 were used for genomic DNA extraction, PCR amplification, and electrophoresis detection according to the method in Example 1.

[0069] 3. Data Analysis: Genetic diversity parameters such as the number of alleles (Na), effective number of alleles (Ne), polymorphism information content (PIC), and expected heterozygosity (He) were statistically analyzed for each SSR locus. UPGMA cluster analysis was performed to calculate the genetic similarity coefficient.

[0070] 4. Results: A total of 195 alleles were detected in 92 lotus root germplasms from 50 polymorphic SSR markers, with an average of 3.9 alleles per locus, an average PIC value of 0.43, and an average He value of 0.50. UPGMA cluster analysis showed that the genetic similarity coefficients among the 92 lotus root germplasms ranged from 0.58 to 0.96. The clustering results of cultivated varieties were significantly associated with their main agronomic traits (seed-type, rhizome-type, and flower-type) (correlation coefficient r = 0.72), but not with geographical origin.

[0071] Example 4: This example demonstrates the application of kinship identification, as detailed below:

[0072] 1. Materials: Five parent-offspring combinations of lotus plants with known kinship were selected (verified by hybridization breeding experiments), and five non-parental control samples were also set up. The samples were grown at the planting base of Hubei Zhonglian Technology Co., Ltd.

[0073] 2. Methods: Six pairs of SSR marker primers selected in Example 1 were used to perform PCR amplification and electrophoresis on all samples. The provided intelligent CNC molecular identification system for lotus root kinship was used to automatically generate a DNA fingerprint comparison report between the offspring and each candidate parent. The probability of kinship was calculated based on the number of shared alleles and the genetic similarity coefficient.

[0074] 3. Results: The identification results showed that the genetic similarity coefficient between the offspring and the true parents was 0.75-0.92, and the genetic similarity coefficient between the offspring and the non-parental control was 0.38-0.55. The accuracy rate of identifying the true parents was 100%, and the accuracy rate of excluding non-parental offspring was 100%.

[0075] Example 5: This example is a system overall performance verification experiment, as detailed below:

[0076] 1. Samples: 120 field samples of lotus roots to be tested, sourced from major lotus root producing areas in Hubei Province, and grown at the China Xianlian Science and Technology Park planting base in Shahu Town, Xiantao City, Hubei Province.

[0077] 2. Methods: The above-mentioned intelligent numerical control system for molecular identification of lotus root kinship was used, operating according to a fully automated process: nucleic acid extraction → PCR amplification → electrophoresis detection → AI image recognition → kinship analysis, with the total running time recorded. Simultaneously, a manual control experiment (traditional method) was conducted.

[0078] 3. Results: The fully automated process takes 4.2-5.8 hours, with a maximum throughput of 96 samples per batch. Compared to traditional manual identification methods (3-5 days), this represents an efficiency improvement of more than 10 times.

[0079] The convolutional neural network model in the artificial intelligence image recognition algorithm, trained on a dataset of lotus root electrophoresis images labeled with DNA fingerprint features, achieved an accuracy rate of 96.3% in automatically identifying the position, size, and intensity of electrophoretic bands, with an error rate of no more than 1.8% for fingerprint pattern standardization conversion. The overall consistency rate between the identification results and field morphological identification results was 97.5%, and the match rate with known pedigree records was 98.2%. The overall verification accuracy rate of the artificial control group was 89.6%, and the system provided by this invention improved the accuracy rate by approximately 8.6 percentage points compared to the artificial control.

[0080] In summary, the technical solutions provided by the embodiments of the present invention have the following significant advantages and beneficial effects:

[0081] 1. Fully Automated Process: This invention integrates nucleic acid extraction, gene amplification, electrophoresis detection, and data analysis into one system, achieving fully automated operation of lotus root paternity identification from sample to result. Compared to traditional manual operation, the identification cycle is shortened from 3-5 days to 4-6 hours, with a maximum throughput of 96 samples per batch.

[0082] 2. Accurate and reliable identification results: This invention uses the SSR / InDel core primer combination for amplification and detection, achieving a variety differentiation rate of 100%. The artificial intelligence image recognition algorithm achieves an accuracy rate of over 96.3% in automatically identifying the position, size, and intensity of electrophoretic bands, and the identification results match the variety pedigree records with a success rate of over 98%.

[0083] 3. Simple operation and low labor cost: This invention realizes automatic control of the operating parameters of each module through a numerical control processor and realizes one-click operation through a human-machine interface. Identification work can be completed without the need for personnel with professional molecular biology background, reducing labor costs by more than 80%.

[0084] 4. High degree of standardization: The system provided by this invention has a built-in lotus root variety reference database and a standardized data analysis process, which avoids the subjectivity and individual differences of manual interpretation, and the identification results are repeatable and comparable.

[0085] 5. High-throughput detection capability: This invention can process 96 samples in a single batch, meeting the high-throughput requirements of application scenarios such as large-scale germplasm resource identification and variety purity testing.

[0086] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification.

Claims

1. A numerically controlled intelligent system for molecular identification of lotus root parentage, characterized in that, include: An automated nucleic acid extraction module is used to automatically extract nucleic acid samples from lotus root samples to be tested. The amplification reaction module is connected to the automated nucleic acid extraction module and has a built-in lotus root-specific molecular marker primer set, which is used to perform an amplification reaction using the nucleic acid sample as a template to obtain amplification products. The separation and detection module is connected to the amplification reaction module and is used to separate and detect the amplification products and acquire detection images containing molecular marker feature information. The numerical control intelligent analysis module is connected to the separation and detection module. It has a built-in lotus root variety reference database. It automatically converts the detected image into a standardized molecular marker spectrum through artificial intelligence image recognition algorithm, and compares and analyzes it with the lotus root variety reference database to obtain the lotus root kinship identification results.

2. The intelligent numerical control system for molecular identification of lotus root parentage according to claim 1, characterized in that, The lotus root-specific molecular marker primer set is one or more of the following: SSR marker primer set, InDel marker primer set, EST-SSR marker primer set, SNP marker primer set, and MNP marker primer set.

3. The intelligent numerical control system for molecular identification of lotus root parentage according to claim 2, characterized in that, The SSR-labeled primer set includes primer pairs for microsatellite markers NL-28, NL-P8, NL-35, NL-1, NL-P9, and NL-47; the nucleotide sequences of the primer pairs for NL-28 are shown in SEQ ID NO:1 and SEQ ID NO:2, respectively; the nucleotide sequences of the primer pairs for NL-P8 are shown in SEQ ID NO:3 and SEQ ID NO:4, respectively; the nucleotide sequences of the primer pairs for NL-35 are shown in SEQ ID NO:5 and SEQ ID NO:6, respectively; the nucleotide sequences of the primer pairs for NL-1 are shown in SEQ ID NO:7 and SEQ ID NO:8, respectively; the nucleotide sequences of the primer pairs for NL-P9 are shown in SEQ ID NO:9 and SEQ ID NO:10, respectively; and the nucleotide sequences of the primer pairs for NL-47 are shown in SEQ ID NO:11 and SEQ ID NO:12, respectively.

4. The intelligent numerical control system for molecular identification of lotus root parentage according to claim 2, characterized in that, The InDel-labeled primer set includes primer pairs for InD38, InD61, InD84, InD92, InD105, InD117, and InD128. The nucleotide sequences of the InD38 primer pairs are shown in SEQ ID NO:13 and SEQ ID NO:14, respectively; the nucleotide sequences of the InD61 primer pairs are shown in SEQ ID NO:15 and SEQ ID NO:16, respectively; the nucleotide sequences of the InD84 primer pairs are shown in SEQ ID NO:17 and SEQ ID NO:18, respectively; the nucleotide sequences of the InD92 primer pairs are shown in SEQ ID NO:19 and SEQ ID NO:20, respectively; the nucleotide sequences of the InD105 primer pairs are shown in SEQ ID NO:21 and SEQ ID NO:22, respectively; the nucleotide sequences of the InD117 primer pairs are shown in SEQ ID NO:23 and SEQ ID NO:24, respectively; and the nucleotide sequences of the InD128 primer pairs are shown in SEQ ID NO:25 and SEQ ID NO:26, respectively. NO:26 shown.

5. The intelligent numerical control system for molecular identification of lotus root parentage according to claim 1, characterized in that, The amplification reaction module uses any one of the following techniques for amplification: PCR amplification, LMTIA isothermal amplification, or multiplex PCR amplification.

6. The intelligent numerical control system for molecular identification of lotus root parentage according to claim 1, characterized in that, The artificial intelligence image recognition algorithm includes a neural network model trained on a dataset of lotus root detection maps labeled with molecular marker features. This model is used to automatically identify the position, intensity, and / or size of the marker signals in the detection images and generate standardized molecular marker maps. The neural network model includes a convolutional neural network model.

7. The intelligent numerical control system for molecular identification of lotus root parentage according to claim 6, characterized in that, The molecular marker map is a DNA fingerprint map; the separation and detection method is electrophoretic separation, the corresponding detection image is an electrophoretic map image, and the labeling signal is an electrophoretic band; the convolutional neural network model has an automatic recognition accuracy of ≥95% for the position, size, and intensity of the electrophoretic bands, and the normalization conversion error rate of the DNA fingerprint map is ≤2%.

8. The intelligent numerical control system for molecular identification of lotus root parentage according to claim 1, characterized in that, The lotus root variety reference database contains variety identity information, molecular marker map characteristics, and phylogenetic data of lotus root germplasm resources; the lotus root variety reference database is a local database or a cloud database, supporting incremental updates and remote sharing.

9. A molecular identification device for lotus root parentage, comprising a main body and a human-machine interface, characterized in that, It also includes the CNC intelligent system for molecular identification of lotus root parentage as described in any one of claims 1-8.

10. The application of a numerically controlled intelligent system for lotus root parentage molecular identification as described in any one of claims 1-8 or the lotus root parentage molecular identification equipment as described in claim 9 in lotus root parentage molecular identification, characterized in that, The method for molecular identification of lotus root kinship includes the following steps: Nucleic acid samples were automatically extracted from lotus root samples to be tested using an automated nucleic acid extraction module. Using the lotus root-specific molecular marker primer set built into the amplification reaction module, the nucleic acid sample is used as a template to perform the amplification reaction and obtain the amplification product. The amplification products are separated and detected by the separation and detection module, and detection images containing molecular marker feature information are acquired. The detection image is automatically converted into a standardized molecular marker spectrum by the artificial intelligence image recognition algorithm of the CNC intelligent analysis module, and compared with the lotus root variety reference database to obtain the lotus root kinship identification results.

Images