Hyperspectral imaging combined with GA-SVM for maize variety identification.

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

A maize seed variety identification method based on improving deep residual convolutional network.

Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network's learning process and enabled more efficient information propagation across the network layers. Meanwhile, in the later residual blocks of the model, we introduced both the efficient channel attention (ECA) mechanism and depthwise separable (DS) convolution, which reduced the model's parameter cost and enabled the capturing of more precise and detailed features. Finally, a Swish-PReLU mixed activation function was introduced globally to improve the overall predictive power of the model. The results showed that our model achieved an impressive accuracy of 91.23% in corn seed classification, surpassing other related models. Compared with the original model, our model improved the accuracy by 7.07%, reduced the loss value by 0.19, and decreased the number of parameters by 40%. The research suggested that this method can efficiently classify corn seeds, holding significant value in seed variety identification.

One hundred single-copy nuclear sequence markers for olive variety identification: a case of fingerprinting database construction in China.

Olive is an ancient oil-producing tree, widely cultivated in Mediterranean countries, and now spread to other areas of the world, including China. Recently, several molecular databases were constructed in different countries and platforms for olive identification using simple sequence repeats (SSRs) or single-nucleotide polymorphisms (SNPs). However, comparing their results across laboratories was difficult. Herein, hundreds of polymorphic single-copy nuclear sequence markers were developed from the olive genome. Using the advantage of multiplex PCR amplification and high-throughput sequencing, a fingerprint database was constructed for the majority of olives cultivated in China. We used 100 high-quality sequence loci and estimated the genetic diversity and structure among all these varieties. We found that compared with that based on SSRs, the constructed fingerprint database based on these 100 sequences or a few of them, could provide a reliable olive variety identification platform in China, with high discrimination among different varieties using the principle of BLAST algorithm. An example of such identification platform based on this study was displayed on the web for the olive database in China (http://olivedb.cn/jianding). After resolving redundant genotypes, we identified 126 olive varieties with distinct genotypes in China. These varieties could be divided into two clusters, and it was revealed that the grouping of the varieties has a certain relationship with their origin. Herein, it is concluded that these single-copy orthologous nuclear sequences could be used to construct a universal fingerprint database of olives across different laboratories and platforms inexpensively. Based on such a database, variety identification can be performed easily by any laboratory, which would further facilitate olive breeding and variety exchange globally. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01434-9.

Genetic Relationships of 118 Castanea Specific Germplasms and Construction of Their Molecular ID Based on Morphological Characteristics and SSR Markers.

To understand the genetic relationships of Castanea species, 16 phenotypic traits were measured, simple sequence repeat (SSR) markers were analyzed, and molecular identity cards (IDs) were constructed for 118 Castanea materials using fluorescent capillary electrophoresis. The coefficient of variation values of the 16 morphological traits of the test materials ranged from 11.11% to 60.38%. A total of 58 alleles were detected using six pairs of SSR core primers, with an average number of 9.7 alleles per locus. The average number of valid alleles per locus was 3.9419 and the proportion of valid alleles was 40.78%. A total of 105 genotypes were detected, and the number of genotypic species that could be amplified per primer pair ranged from 8 to 26. The mean value of the observed heterozygosity was 0.4986. The variation in the He, H, and PIC values was similar; the size of I value was approximately 2.21 times larger, and its mean number of variations was 0.7390, 0.7359, 0.6985, and 1.6015, respectively. The classification of 118 Castanea species was performed using three analytical methods: structure analysis, neighbor-joining (NJ) cluster analysis, and principal coordinate analysis (PCoA), and the results of the three methods were in high agreement. Six pairs of SSR core primers with high polymorphism and strong discriminatory properties were used to identify 118 Castanea plants, and a unique molecular ID card was constructed for each material. These results provide insight into the genetic diversity and population structure of Castanea plants and a theoretical basis for improving the phenomenon of mixed varieties and substandard plants in the Castanea plant market.

SSR marker based analysis for identification and of genetic diversity of non-heading Chinese cabbage varieties.

As a widely cultivated vegetable in China and Southeast Asia, the breeding of non-heading Chinese cabbage (Brassica campestris ssp. chinensis Makino) is widespread; more than 400 varieties have been granted new plant variety rights (PVRs) in China. Distinctness is one of the key requirements for the granting of PVRs, and molecular markers are widely used as a robust supplementary method for similar variety selection in the distinctness test. Although many genome-wide molecular markers have been developed, they have not all been well used in variety identification and tests of distinctness of non-heading Chinese cabbage. In this study, by using 423 non-heading Chinese cabbage varieties collected from different regions of China, 287 simple sequence repeat (SSR) markers were screened for polymorphisms, and 23 core markers were finally selected. The polymorphic information content (PIC) values of the 23 SSR markers ranged from 0.555 to 0.911, with an average of 0.693, and the average number of alleles per marker was 13.65. Using these 23 SSR markers, 418 out of 423 varieties could be distinguished, with a discrimination rate of 99.994%. Field tests indicated that those undistinguished varieties were very similar and could be further distinguished by a few morphological characteristics. According to the clustering results, the 423 varieties could be divided into three groups: pak-choi, caitai, and tacai. The similarity coefficient between the SSR markers and morphological characteristics was moderate (0.53), and the efficiency of variety identification was significantly improved by using a combination of SSR markers and morphological characteristics.

Investigation of microsatellite loci for the identification of registered varieties of Perilla frutescens and a discussion on the ancestor species of P. frutescens.

Techniques for identifying varieties of crops used as spices and food additives have important implications for the safety of food production, prevention of false labeling, protection of breeders' rights, and prevention of theft or outflow to other countries. Presently, there are 16 varieties of Perilla frutescens in the variety registration system of the Ministry of Agriculture, Forestry and Fishes in Japan (Ministry of Agriculture, Forestry and Fisheries. Variety registration data search. http://www.hinshu2.maff.go.jp/ . Accessed 03 Nov 2022). One such variety is "Shimoadachi," which contains citral as a main essential oil component and has a lemon-like smell. To our knowledge, no other cultivars with similar characteristics in P. frutescens have been identified. Additionally, the registered variety "per-001" contains high contents of perillaldehyde and rosmarinic acid, with practical applications for herbal medicines and functional foods. Therefore, the development of variety identification techniques is necessary for stable production and protection. In this study, we investigated microsatellite loci for the accurate identification of registered varieties of red perilla. These loci provide a basis for breeding superior varieties of medicinal plants.

Genetic diversity analysis and variety identification using SSR and SNP markers in melon.

Melon is an important horticultural crop with a pleasant aromatic flavor and abundance of health-promoting substances. Numerous melon varieties have been cultivated worldwide in recent years, but the high number of varieties and the high similarity between them poses a major challenge for variety evaluation, discrimination, as well as innovation in breeding. Recently, simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs), two robust molecular markers, have been utilized as a rapid and reliable method for variety identification. To elucidate the genetic structure and diversity of melon varieties, we screened out 136 perfect SSRs and 164 perfect SNPs from the resequencing data of 149 accessions, including the most representative lines worldwide. This study established the DNA fingerprint of 259 widely-cultivated melon varieties in China using Target-seq technology. All melon varieties were classified into five subgruops, including ssp. agrestis, ssp. melo, muskmelon and two subgroups of foreign individuals. Compared with ssp. melo, the ssp. agrestis varieties might be exposed to a high risk of genetic erosion due to their extremely narrow genetic background. Increasing the gene exchange between ssp. melo and ssp. agrestis is therefore necessary in the breeding procedure. In addition, analysis of the DNA fingerprints of the 259 melon varieties showed a good linear correlation (R2 = 0.9722) between the SSR genotyping and SNP genotyping methods in variety identification. The pedigree analysis based on the DNA fingerprint of 'Jingyu' and 'Jingmi' series melon varieties was consistent with their breeding history. Based on the SNP index analysis, ssp. agrestis had low gene exchange with ssp. melo in chromosome 4, 7, 10, 11and 12, two specific SNP loci were verified to distinguish ssp. agrestis and ssp. melon varieties. Finally, 23 SSRs and 40 SNPs were selected as the core sets of markers for application in variety identification, which could be efficiently applied to variety authentication, variety monitoring, as well as the protection of intellectual property rights in melon.

Identification of wheat seedling varieties based on MssiapNet.

Introduction: In the actual planting of wheat, there are often shortages of seedlings and broken seedlings on long ridges in the field, thus affecting grain yield and indirectly causing economic losses. Variety identification of wheat seedlings using physical methods timeliness and is unsuitable for universal dissemination. Recognition of wheat seedling varieties using deep learning models has high timeliness and accuracy, but fewer researchers exist. Therefore, in this paper, a lightweight wheat seedling variety recognition model, MssiapNet, is proposed. Methods: The model is based on the MobileVit-XS and increases the model's sensitivity to subtle differences between different varieties by introducing the scSE attention mechanism in the MV2 module, so the recognition accuracy is improved. In addition, this paper proposes the IAP module to fuse the identified feature information. Subsequently, training was performed on a self-constructed real dataset, which included 29,020 photographs of wheat seedlings of 29 varieties. Results: The recognition accuracy of this model is 96.85%, which is higher than the other nine mainstream classification models. Although it is only 0.06 higher than the Resnet34 model, the number of parameters is only 1/3 of that. The number of parameters required for MssiapNet is 29.70MB, and the single image Execution time and the single image Delay time are 0.16s and 0.05s. The MssiapNet was visualized, and the heat map showed that the model was superior for wheat seedling variety identification compared with MobileVit-XS. Discussion: The proposed model has a good recognition effect on wheat seedling varieties and uses a few parameters with fast inference speed, which makes it easy to be subsequently deployed on mobile terminals for practical performance testing.

Nitrogen Allocation Tradeoffs Within-Leaf between Photosynthesis and High-Temperature Adaptation among Different Varieties of Pecan (Carya illinoinensis [Wangenh.] K. Koch).

Interpreting leaf nitrogen (N) allocation is essential to understanding leaf N cycling and the economy of plant adaptation to environmental fluctuations, yet the way these mechanisms shift in various varieties under high temperatures remains unclear. Here, eight varieties of pecan (Carya illinoinensis [Wangenh.] K. Koch), Mahan, YLC10, YLC12, YLC13, YLC29, YLC35, YLJ042, and YLJ5, were compared to investigate the effects of high temperatures on leaf N, photosynthesis, N allocation, osmolytes, and lipid peroxidation and their interrelations. Results showed that YLC35 had a higher maximum net photosynthetic rate (Pmax) and photosynthetic N-use efficiency (PNUE), while YLC29 had higher N content per area (Na) and lower PNUE. YLC35, with lower malondialdehyde (MDA), had the highest proportions of N allocation in rubisco (Pr), bioenergetics (Pb), and photosynthetic apparatus (Pp), while YLC29, with the highest MDA, had the lowest Pr, Pb, and Pp, implying more leaf N allocated to the photosynthetic apparatus for boosting PNUE or to non-photosynthetic apparatus for alleviating damage. Structural equation modeling (SEM) demonstrated that N allocation was affected negatively by leaf N and positively by photosynthesis, and their combination indirectly affected lipid peroxidation through the reverse regulation of N allocation. Our results indicate that different varieties of pecan employ different resource-utilization strategies and growth-defense tradeoffs for homeostatic balance under high temperatures.

Phenotypic variability and genetic diversity analysis of cultivated potatoes in China.

Phenotypic evaluation and molecular biotechnology are both important in the identification and utilization of crop germplasm resources. In this study, the phenotypic variation and genetic diversity of 149 main potato cultivars in China were investigated with 12 phenotypic traits and 24 SSR markers. The coefficient of variation of 12 phenotypic traits ranged from 12.11% to 156.93%. The results of SSR markers exhibited a relatively high level of genetic variation (Na =5.458 ± 1.499, Ne =3.300 ± 1.087, I =1.397 ± 0.298, Ho =0.797 ± 0.178, He = 0.660 ± 0.117, and PIC=0.702 ± 0.087). Population structure and phylogenetic tree analysis divided the varieties into three subgroups. The results indicated that ninety percent of the molecular variance was attributed to within-group differences, and the remaining 10% was attributed to variation among groups. Consistent with previous report, alleles of the STI032 marker were significantly associated with tuber starch content and growth period traits in the population. The results of this study could facilitate the utilization of potato germplasm resources, molecular genetic breeding and improvement.

Selection and Validation of 48 KASP Markers for Variety Identification and Breeding Guidance in Conventional and Hybrid Rice (Oryza sativa L.).

BACKGROUND: Breeding of conventional and hybrid rice (Oryza sativa L.) have solved hunger problems and increased farmers' income in the world. Molecular markers have been widely used in marker-assisted breeding and identification of larger numbers of different bred varieties in the past decades. The recently developed SNP markers are applied for more stable and detectable compared with other markers. But the cost of genotyping lots SNPs is high. So, it is essential to select less representative SNPs and inexpensive detecting methods to lower the cost and accelerate variety identification and breeding process. KASP (Kompetitive Allele-Specific PCR) is a flexible method to detect the SNPs, and large number of KASP markers have been widely used in variety identification and breeding. However, the ability of less KASP markers on massive variety identification and breeding remains unknown. RESULTS: Here, 48 KASP markers were selected from 378 markers to classify and analyze 518 varieties including conventional and hybrid rice. Through analyzing the population structure, the 48 markers could almost represent the 378 markers. In terms of variety identification, the 48 KASP markers had a 100% discrimination rate in 53 conventional indica varieties and 193 hybrid varieties, while they could distinguish 89.1% conventional japonica rice from different breeding institutes. Two more markers added would increase the ratio from 68.38 to 77.94%. Additionally, the 48 markers could be used for classification of subpopulations in the bred variety. Also, 8 markers had almost completely different genotypes between japonica and indica, and 3 markers were found to be very important for japonica hybrid rice. In hybrid varieties, the heterozygosity of chromosomes 3, 6 and 11 was relatively higher than others. CONCLUSIONS: Our results showed that 48 KASP markers could be used to identify rice varieties, and the panel we tested could provide a database for breeders to identify new breeding lines. Also, the specific markers we found were useful for marker-assisted breeding in rice, including conventional and hybrid.

Development of SLAF-Sequence and Multiplex SNaPshot Panels for Population Genetic Diversity Analysis and Construction of DNA Fingerprints for Sugarcane.

A genetic diversity analysis and identification of plant germplasms and varieties are important and necessary for plant breeding. Deoxyribonucleotide (DNA) fingerprints based on genomic molecular markers play an important role in accurate germplasm identification. In this study, Specific-Locus Amplified Fragment Sequencing (SLAF-seq) was conducted for a sugarcane population with 103 cultivated and wild accessions. In total, 105,325 genomic single nucleotide polymorphisms (SNPs) were called successfully to analyze population components and genetic diversity. The genetic diversity of the population was complex and clustered into two major subpopulations. A principal component analysis (PCA) showed that these accessions could not be completely classified based on geographical origin. After filtration, screening, and comparison, 192 uniformly-distributed SNP loci were selected for the 32 chromosomes of sugarcane. An SNP complex genotyping detection system was established using the SNaPshot typing method and used for the precise genotyping and identification of 180 sugarcane germplasm samples. According to the stability and polymorphism of the SNPs, 32 high-quality SNP markers were obtained and successfully used to construct the first SNP fingerprinting and quick response codes (QR codes) for sugarcane. The results provide new insights for genotyping, classifying, and identifying germplasm and resources for sugarcane breeding.

RAD-seq as an effective strategy for heterogenous variety identification in plants-a case study in Italian Ryegrass (Lolium multiflorum).

The primary approach for variety distinction in Italian ryegrass is currently the DUS (distinctness, uniformity and stability) test based on phenotypic traits. Considering the diverse genetic background within the population and the complexity of the environment, however, it is challenging to accurately distinguish varieties based on DUS criteria alone. In this study, we proposed the application of high-throughput RAD-seq to distinguish 11 Italian ryegrass varieties with three bulks of 50 individuals per variety. Our findings revealed significant differences among the 11 tested varieties. The PCA, DAPC and STRUCTURE analysis indicated a heterogeneous genetic background for all of them, and the AMOVA analysis also showed large genetic variance among these varieties (ΦST = 0.373), which were clearly distinguished based on phylogenetic analysis. Further nucleotide diversity (Pi) analysis showed that the variety 'Changjiang No.2' had the best intra-variety consistency among 11 tested varieties. Our findings suggest that the RAD-seq could be an effectively alternative method for the variety distinction of Italian ryegrass, as well as a potential tool for open-pollinated varieties (OPVs) of other allogamous species.

Comparative Evaluation of Different Targeted and Untargeted Analytical Approaches to Assess Greek Extra Virgin Olive Oil Quality and Authentication.

Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet, with several health benefits derived from its consumption. Moreover, due to its eminent market position, EVOO has been thoroughly studied over the last several years, aiming at its authentication, but also to reveal the chemical profile inherent to its beneficial properties. In the present work, a comparative study was conducted to assess Greek EVOOs' quality and authentication utilizing different analytical approaches, both targeted and untargeted. 173 monovarietal EVOOs from three emblematic Greek cultivars (Koroneiki, Kolovi and Adramytiani), obtained during the harvesting years of 2018-2020, were analyzed and quantified as per their fatty acids methyl esters (FAMEs) composition via the official method (EEC) No 2568/91, as well as their bioactive content through liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) methodology. In addition to FAMEs analysis, EVOO samples were also analyzed via HRMS-untargeted metabolomics and optical spectroscopy techniques (visible absorption, fluorescence and Raman). The data retrieved from all applied techniques were analyzed with Machine Learning methods for the authentication of the EVOOs' variety. The models' predictive performance was calculated through test samples, while for further evaluation 30 commercially available EVOO samples were also examined in terms of variety. To the best of our knowledge, this is the first study where different techniques from the fields of standard analysis, spectrometry and optical spectroscopy are applied to the same EVOO samples, providing strong insight into EVOOs chemical profile and a comparative evaluation through the different platforms.

Evaluation of microsatellite allelic patterns to DNA fingerprinting in rubber tree clones

The rubber tree (Hevea brasiliensis) is native to the Amazon region, and it is widely exploited due to natural rubber produced from latex. There are many clonal varieties, without certification tests. In order to determine a genetic certification, 15 clones were genotyped to identify their genetic pattern. Ten microsatellites were used to determine a subset of alleles exclusive for each genetic profile. The genetic estimates obtained were: number of alleles per locus (N), expected (HE) and observed (HO) heterozygosity, Polymorphic Information Content (PIC) and Discriminatory Power (DP). The number of alleles (N) ranged from five to 14, with an average of 9.2. The HE mean (0.80) was higher than HO (0.60), indicating a selection for homozygotes. The locus informativeness was verified with PIC (0.77) and DP (0.90) means showing high polymorphism. The dendrogram represented the formation of three groups related to geographical origin. Clone MDF 180 presented the highest genetic divergence. Two genic pools represented the genetic composition of genotypes. Based on allelic profiles, a set of two microsatellites (A2365 and A2368) was able to distinguish all examined clones. The genetic certification using microsatellite fingerprinting proved to be an alternative to morphological traits.

Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice.

BACKGROUND: Plant variety identification is the one most important of agricultural systems. Development of DNA marker profiles of released varieties to compare with candidate variety or future variety is required. However, strictly speaking, scientists did not use most existing variety identification techniques for "identification" but for "distinction of a limited number of cultivars," of which generalization ability always not be well estimated. Because many varieties have similar genetic backgrounds, even some essentially derived varieties (EDVs) are involved, which brings difficulties for identification and breeding progress. A fast, accurate variety identification method, which also has good performance on EDV determination, needs to be developed. RESULTS: In this study, with the strategy of "Divide and Conquer," a variety identification method Conditional Random Selection (CRS) method based on SNP of the whole genome of 3024 rice varieties was developed and be applied in essentially derived variety (EDV) identification of rice. CRS is a fast, efficient, and automated variety identification method. Meanwhile, in practical, with the optimal threshold of identity score searched in this study, the set of SNP (including 390 SNPs) showed optimal performance on EDV and non-EDV identification in two independent testing datasets. CONCLUSION: This approach first selected a minimal set of SNPs to discriminate non-EDVs in the 3000 Rice Genome Project, then united several simplified SNP sets to improve its generalization ability for EDV and non-EDV identification in testing datasets. The results suggested that the CRS method outperformed traditional feature selection methods. Furthermore, it provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties and be useful for crop breeding.

Study on the identification of resistance of rice blast based on near infrared spectroscopy.

Rice Blast is the most devastating rice disease which poses a serious threat to the safe production of rice. The most effective way to prevent rice blast is to cultivate the rice varieties that have resistance to the disease, however, traditional resistance testing requires professional personnel, a tedious process, long determination time and high cost. In order to quickly identify different resistant rice seeds which are difficult to distinguish with the naked eye, a rapid non-destructive identification method based on Near-Infrared Spectroscopy (NIRS) was proposed. Four different types of resistant rice seeds (high resistance, high susceptibility, susceptibility and resistance) came from in HeiLongjiang province of China were selected as the research objects. A total of 240 spectral data (60 from each variety) were scanned by the NIR spectrometer. The BP neural network (BP), Support Vector Machines (SVM), Probabilistic Neural Network (PNN) models were established based on the original spectral data in the full-spectrum (11520-4000 cm-1). Among all, Raw-BP has the best identification accuracy which reaches 100% with an iteration time of 869 s. After extracting the feature wavelengths by successive projections algorithm (SPA) on the spectral data, Raw-SPA-BP, Raw-SPA-SVM and Raw-SPA-PNN models were established. The accuracy of these three models didn't improve. But the iteration time of the SPA-BP model was shortened to 791 s. Another group of BP, SVM, and PNN models were established after using different spectral pretreatment methods and the SPA feature extraction. After Multivariate Scatter Correction (MSC), the accuracy of the MSC-SPA-BP model was still 100% and the iteration time was shortened to 840 s, which is 1/30 of the time at which the original data model was formed. The accuracy of the MSC-SPA-PNN model increased from 60% to 90% and the accuracy of the MSC-SPA-SVM model increased from 60% to 85%. Based on the comparison analysis of the models mentioned above, a best neural network identification model of the MSC-SPA-BP with 513 inputs, 8 hidden layers and 4 outputs was established. Its classification accuracy reached 100% with an iteration time of 29 s, indicating that the MSC-SPA-BP model can completely achieve identification of four different resistant rice seeds. Therefore, the proposed method of the BP neural network identification model based on NIRS can be fully applied to the non-destructive rapid identification of rice seeds. Meanwhile, it provides a reference for the rapid identification of other crop seeds.

Development of SNP Markers for Original Analysis and Germplasm Identification in Camellia sinensis.

Tea plants are widely grown all over the world because they are an important economic crop. The purity and authenticity of tea varieties are frequent problems in the conservation and promotion of germplasm resources in recent years, which has brought considerable inconvenience and uncertainty to the selection of parental lines for breeding and the research and cultivation of superior varieties. However, the development of core SNP markers can quickly and accurately identify the germplasm, which plays an important role in germplasm identification and the genetic relationship analysis of tea plants. In this study, based on 179,970 SNP loci from the whole genome of the tea plant, all of 142 cultivars were clearly divided into three groups: Assam type (CSA), Chinese type (CSS), and transitional type. Most CSA cultivars are from Yunnan Province, which confirms that Yunnan Province is the primary center of CSA origin and domestication. Most CSS cultivars are distributed in east China; therefore, we deduced that east China (mainly Zhejiang and Fujian provinces) is most likely the area of origin and domestication of CSS. Moreover, 45 core markers were screened using strict criteria to 179,970 SNP loci, and we analyzed 117 well-Known tea cultivars in China with 45 core SNP markers. The results were as follows: (1) In total, 117 tea cultivars were distinguished by eight markers, which were selected to construct the DNA fingerprint, and the remaining markers were used as standby markers for germplasm identification. (2) Ten pairs of parent and offspring relationships were confirmed or identified, and among them, seven pairs were well-established pedigree relationships; the other three pairs were newly identified. In this study, the east of China (mainly Zhejiang and Fujian provinces) is most likely the area of origin and domestication of CSS. The 45 core SNP markers were developed, which provide a scientific basis at the molecular level to identify the superior tea germplasm, undertake genetic relationship analysis, and benefit subsequent breeding work.

Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network.

Soybean seed purity is a critical factor in agricultural products, standardization of seed quality, and food processing. In this study, laser-induced breakdown spectroscopy (LIBS) as an effective technology was successfully used to identify ten varieties of soybean seeds. We improved the traditional sample preparation scheme for LIBS. Instead of grinding and squashing, we propose a time-efficient method by pressing soybean seeds into rubber sand filled with culture plates through a ruler to ensure a relatively uniform surface height. In our experimental scheme, three LIBS spectra were finally collected for each soybean seed. A majority vote based on three spectra was applied as the final decision judging the attribution of a single soybean seed. The results showed that the support vector machine (SVM) obtained the optimal identification accuracy of 90% in the prediction set. In addition, PCA-ResNet (propagation coefficient adaptive ResNet) and PCSA-ResNet (propagation coefficient synchronous adaptive ResNet) were designed based on typical ResNet structure by changing the way of self-adaption of propagation coefficients. Combined with a new form of input data called spectral matrix, PCSA-ResNet obtained the optimal performance with the discriminate accuracy of 91.75% in the prediction set. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the clustering process of the extracted features by PCSA-ResNet. For the interpretation of the good performance of PCSA-ResNet coupled with the spectral matrix, saliency maps were further applied to visually show the pixel positions of the spectral matrix that had a significant influence on the discrimination results, indicating that the content and proportion of elements in soybean seeds could reflect the variety differences.

Construction of a SNP Fingerprinting Database and Population Genetic Analysis of Cigar Tobacco Germplasm Resources in China.

Cigar tobacco is an important economic crop that is widely grown around the world. In recent years, varietal identification has become a frequent problem in germplasm preservation collections, which causes considerable inconvenience and uncertainty in the cataloging and preservation of cigar germplasm resources, in the selection of parental lines for breeding, and in the promotion and use of high quality varieties. Therefore, the use of DNA fingerprints to achieve rapid and accurate identification of varieties can play an important role in germplasm identification and property rights disputes. In this study, we used genotyping-by-sequencing (GBS) on 113 cigar tobacco accessions to develop SNP markers. After filtering, 580,942 high-quality SNPs were obtained. We used the 580,942 SNPs to perform principal component analysis (PCA), population structure analysis, and neighbor joining (NJ) cluster analysis on the 113 cigar tobacco accessions. The results showed that the accessions were not completely classified based on their geographical origins, and the genetic backgrounds of these cigar resources are complex and diverse. We further selected from these high-quality SNPs to obtained 163 SNP sites, 133 of which were successfully converted into KASP markers. Finally, 47 core KASP markers and 24 candidate core markers were developed. Using the core markers, we performed variety identification and fingerprinting in 216 cigar germplasm accessions. The results of SNP fingerprinting, 2D barcoding, and genetic analysis of cigar tobacco germplasm in this study provide a scientific basis for screening and identifying high-quality cigar tobacco germplasm, mining important genes, and broadening the basis of cigar tobacco genetics and subsequent breeding work at the molecular level.