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Seed processing is an important means of improving seed quality. However, the traditional seed processing process and parameter adjustment are highly empirically dependent. In this study, machine vision technology was used to develop a seed processing method based on the rapid extraction of seeds' material characteristics. Combined with the results of clarity analysis and the single seed germination test, the seed processing process and parameters were determined through data analysis. The results showed that several phenotypic features were significantly or highly significantly correlated with clarity, but fewer phenotypic features were correlated with viability. According to the probability density distribution of pure seeds and impurities in the features that were significantly correlated with seed clarity, the sorting parameters of length, width, R, G, and B were determined. When the combination of width (≥0.8 mm) + G (<75) was used for sorting, the recall of pure seeds was higher than 91%, and the precision was increased to 98.6%. Combined with the specific production reality, the preliminary determination of the Platycodon grandiflorum seed processing process was air separation—screen (round hole sieve)—color sorting. Then, four commercialized Platycodon grandiflorum seed lots were sorted by this process using corresponding parameters in the actual processing equipment. Subsequently, the seed clarity and germination percentage were significantly improved, and the seed quality qualification rate was increased from 25% to 75%. In summary, by using machine vision technology to quickly extract the material characteristics of the seeds, combined with correlation analysis, probability density distribution plots, single feature selection, and combination sorting comparisons, the appropriate processing process and corresponding sorting parameters for a specific seed lot can be determined, thus maximizing the seed quality. [ABSTRACT FROM AUTHOR]

It is crucial to identify and select high-quality seeds for improving Scutellaria baicalensis yield. In this study, we present a non-destructive and accurate method for predicting Scutellaria baicalensis seed viability that used seed phenotypic data with machine-learning algorithms to distinguish between vital and dead seeds. Meanwhile, the SMOTE was used to balance the dataset and make the established viability discrimination model more efficient by avoiding problems of overfitting or under-fitting. The results showed that hyperspectral imaging (HSI) combined with detrend (DT) preprocessing and a support vector machine (SVM) model could predict Scutellaria baicalensis seed viability with a 93.3% accuracy, and increased the germination percentage of the seed lot to 99.1%, while machine vision imaging provided the highest 87.9% accuracy and 87.0% germination percentage. This strategy is suitable for large-scale Scutellaria baicalensis seed viability discrimination operations for ensuring seed quality, expanding the cultivation and production scales of Scutellaria baicalensis, and accelerating the present solving of the problem of short supply. It can help to accelerate the breeding of quality Scutellaria baicalensis varieties. [ABSTRACT FROM AUTHOR]

Seed priming is a method for improving seed vigor, which can improve seed germinability, but the disappearance of positive priming effects and decrease of seed longevity during storage may limit its application. To determine the effect of priming on hot pepper seeds and the time during which priming effects can be maintained, it is necessary to monitor and study the variability of primed seeds during storage. In this study, several priming treatments with 3% KNO3, 20% PEG, or 3% KNO3 + 20% PEG and up to 8 months of storage of primed seeds were conducted on seeds of pepper strains No.63, No.73, and No.101. Germinability was improved by priming treatment. The germination percentage of primed seeds did not vary compared to non-primed ones, seeds germinated quickly and had strengthened emergence, especially for those primed with 3% KNO3 solution, which germinated significantly faster and the seedlings grew stronger. During the 8-month storage process, the beneficial effects of priming disappear after 6 months, beyond which the performance of primed seeds is worse than non-primed seeds. After 8 months of storage, the viability of primed pepper seeds dropped sharply. In general, preservation at −4 °C can retain the priming effects to the greatest degree and prevent the seed from losing viability for up to 6 months. Resistance to aging differed across varieties, with No.101 pepper seeds more resistant to aging than the other two varieties, regardless of priming treatment. [ABSTRACT FROM AUTHOR]

Wheat yield is not only affected by three components of yield, but also affected by plant height (PH). Identification and utilization of the quantitative trait loci (QTL) controlling these four traits is vitally important for breeding high-yielding wheat varieties. In this work, we conducted a QTL analysis using the recombinant inbred lines (RILs) derived from a cross between two winter wheat varieties of China, "Nongda981" (ND981) and "Nongda3097" (ND3097), exhibiting significant differences in spike number per unit area (SN), grain number per spike (GNS), thousand grain weight (TGW), and PH. A total of 11 environmentally stable QTL for these four traits were detected. Among them, four major and stable QTLs (QSn.cau-4B-1.1 , QGns.cau - 4B-1 , QTgw.cau-4B-1.1 , and QPh.cau-4B-1.2) explaining the highest phenotypic variance for SN, GNS, TGW, and PH, respectively, were mapped on the same genomic region of chromosome 4B and were considered a QTL cluster. The QTL cluster spanned a genetic distance of about 12.3 cM, corresponding to a physical distance of about 8.7 Mb. Then, the residual heterozygous line (RHL) was used for fine mapping of the QTL cluster. Finally, QSn.cau-4B-1.1 , QGns.cau - 4B-1 , and QPh.cau-4B-1.2 were colocated to the physical interval of about 1.4 Mb containing 31 annotated high confidence genes. QTgw.cau-4B-1.1 was divided into two linked QTL with opposite effects. The elite NILs of the QTL cluster increased SN and PH by 55.71–74.82% and 14.73–23.54%, respectively, and increased GNS and TGW by 29.72–37.26% and 5.81–11.24% in two environments. Collectively, the QTL cluster for SN, GNS, TGW, and PH provides a theoretical basis for improving wheat yield, and the fine-mapping result will be beneficial for marker-assisted selection and candidate genes cloning. [ABSTRACT FROM AUTHOR]

Chlorophyll fluorescence (CF) has been applied to measure the chlorophyll content of seeds, in order to determine seed maturity, but the high price of equipment limits its wider application. Astragalus seeds were used to explore the applicability of digital image analysis technology to the prediction of seed chlorophyll content and to supply a low cost and alternative method. Our research comprised scanning and extracting the characteristic features of Astragalus seeds, determining the chlorophyll content, and establishing a predictive model of chlorophyll content in Astragalus seeds based on characteristic features. The results showed that the R2 of the MLR prediction model established with multiple features was ≥0.947, and the R2 of the MLP model was ≥0.943. By sorting of two single features, the R and G values, the R2 reached 0.969 and 0.965, respectively. A germination result showed that the lower the chlorophyll content, the higher the quality of the seeds. Therefore, we draw a conclusion that digital image analysis technology can be used to predict effectively the chlorophyll content of Astragalus seeds, and provide a reference for the selection of mature and viable Astragalus seeds. [ABSTRACT FROM AUTHOR]

The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry., Competing Interests: Author HC was employed by Hengde Materia Medica Beijing Agricultural Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Wu, Chen, Zhang, Tu, Hao, Cao, Dong and Sun.)

Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected via hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Hou, Luo, Tu, Zhao and Sun.)