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"Seeds Identification."
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DNA barcoding of Oryza: conventional, specific, and super barcodes
Key messageWe applied the phylogenomics to clarify the concept of rice species, aid in the identification and use of rice germplasms, and support rice biodiversity.Rice (genus Oryza) is one of the most important crops in the world, supporting half of the world’s population. Breeding of high-yielding and quality cultivars relies on genetic resources from both cultivated and wild species, which are collected and maintained in seed banks. Unfortunately, numerous seeds are mislabeled due to taxonomic issues or misidentifications. Here, we applied the phylogenomics of 58 complete chloroplast genomes and two hypervariable nuclear genes to determine species identity in rice seeds. Twenty-one Oryza species were identified. Conspecific relationships were determined between O. glaberrima and O. barthii, O. glumipatula and O. longistaminata, O. grandiglumis and O. alta, O. meyeriana and O. granulata, O. minuta and O. malampuzhaensis, O. nivara and O. sativa subsp. indica, and O. sativa subsp. japonica and O. rufipogon.D and L genome types were not found and the H genome type was extinct. Importantly, we evaluated the performance of four conventional plant DNA barcodes (matK, rbcL, psbA-trnH, and ITS), six rice-specific chloroplast DNA barcodes (psaJ-rpl33, trnC-rpoB, rps16-trnQ, rpl22-rps19, trnK-matK, and ndhC-trnV), two rice-specific nuclear DNA barcodes (NP78 and R22), and a chloroplast genome super DNA barcode. The latter was the most reliable marker. The six rice-specific chloroplast barcodes revealed that 17% of the 53 seed accessions from rice seed banks or field collections were mislabeled. These results are expected to clarify the concept of rice species, aid in the identification and use of rice germplasms, and support rice biodiversity.
Journal Article
A Manual for the Identification of Plant Seeds and Fruits
The taxonomic identification of individual seeds and fruits of wild and cultivated plants is not always straightforward. The specialist literature and botanical reference collections can be helpful, and knowing where to begin reading and comparing can save a considerable amount of time. We wrote A Manual for the Identification of Plant Seeds and Fruits to make your search easier. It describes the inflorescence(s) and infructescence(s) seen in each of a set of 19 plant families, as well as the morphology of its seeds and fruits (with special emphasis on fruit typology); the dispersal units (diaspores); and, if present, heterocarpism and seed dimorphism. The manual is richly illustrated with 460 colour photos of inflorescences, infructescences, seeds, and fruits. Each entry concludes with a concise seed atlas that depicts the variation in the individual plant family's seed and fruit forms. A Manual for the Identification of Plant Seeds and Fruits includes the following plant families: Amaranthaceae Apiaceae Asteraceae Boraginaceae Brassicaceae Caryophyllaceae Convolvulaceae Cyperaceae Fabaceae Geraniaceae Juncaceae Lamiaceae Malvaceae Plantaginaceae Poaceae Polygonaceae Ranunculaceae
eBook
Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.
Journal Article
Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network
Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.
Journal Article
Development of acorn discrimination model for warm-temperature evergreen oaks using hyperspectral analysis
We used hyperspectral analysis to distinguish between acorns of Japanese red oak (Quercus acuta Thunb.) and ring-cup oak (Quercus glauca Thunb.), two closely related species of the evergreen oaks. To accomplish this, 631 Japanese red oak acorns and 505 ring-cupped oak acorns were collected from the seed orchard in Jeju Island, Korea, and hyperspectral imaging was performed. Two types of hyperspectral devices, Corning and Korea Spectral Products (KSP), were used to calibrate images and extract regions of interest. Average spectra were obtained from the extracted regions of interest, and morphological variables were added to the Corning data to form a dataset. Partial least square (PLS) was used as the learning model, Standard normal variate, Multiplicative scatter correction, and Savitzky-Golay filtering were applied as preprocessing techniques, and competitive adaptive reweighted sampling and successive projection algorithm were applied as variable selection techniques ; and the combination of preprocessing method, the number of PLS components, and the number of selected variables were optimized.. The lightweight model was generated from the selected variables, and the performance was improved by combining the morphological variables. As a result, the lightweight model based on Corning dataset showed 45~85% accuracy, and the lightweight model based on the KSP dataset showed 75~90% accuracy. The model utilizing morphological variables in the Corning-based lightweight model showed a high accuracy of 98-100%, so we were able to discriminate the acorns of evergreen oaks between Q. acuta and Q. glauca. The results of this study are expected to serve as a basis for future model development for seed classification of hybrid oak acorns.
Journal Article
An Antique Microscope Slide Brings the Thrill of Discovery into a Contemporary Biology Classroom
The discovery of a Victorian-era microscope slide tided “Grouped Flower Seeds” began an investigation into the scientific and historical background of the antique slide to develop its usefulness as a multidisciplinary tool for PowerPoint presentations usable in contemporary biology classrooms, particularly large-enrollment sections. The resultant presentation was intended to engage students in discussing historical and contemporary biology education, as well as some of the intticacies of seed biology. Comparisons between the usefulness and scope of various seed identification resources, both online and in pant, were made.
Journal Article
A new technique for stain-marking of seeds with safranine to track seed dispersal and seed bank dynamics
Accurate tracking of seed dispersal is critical for understanding gene flow and seed bank dynamics, and for predicting population distributions and spread. Available seed-tracking techniques are limited due to environmental and safety issues or requirements for expensive and specialized equipment. Furthermore, few techniques can be applied to studies of water-dispersed seeds. Here we introduce a new seed-tracking method using safranine to stain seeds/fruits by immersing in ( ex situ ) or spraying with ( in situ ) staining solution. The hue difference value between pre- and post-stained seeds/fruits was compared using the HSV color model to assess the effect of staining. A total of 181 kinds of seeds/fruits out of 233 tested species of farmland weeds, invasive alien herbaceous plants and trees could be effectively stained magenta to red in hue (320–360°) from generally yellowish appearance (30–70°), in which the other 39 ineffectively-stained species were distinguishable by the naked eye from pre-stained seeds. The most effectively stained seeds/fruits were those with fluffy pericarps, episperm, or appendages. Safranine staining was not found to affect seed weight or germination ability regardless of whether seeds were stained ex situ or in situ . For 44 of 48 buried species, the magenta color of stained seeds clearly remained recognizable for more than 5 months after seeds were buried in soil. Tracking experiments using four species ( Beckmannia syzigachne , Oryza sativa f. spontanea, Solidago Canadensis , and Acer buergerianum ), representing two noxious agricultural weeds, an alien invasive plant, and a tree, respectively, showed that the safranine staining technique can be widely applied for studying plant seed dispersal. Identifying and counting the stained seeds/fruits can be executed by specially complied Python-based program, based on OpenCV library for image processing and Numpy for data handling. From the above results, we conclude that staining with safranine is a cheap, reliable, easily recognized, automatically counted, persistent, environmentally safe, and user–friendly tracking-seed method. This technique may be widely applied to staining most of the seed plant species and the study of seed dispersal in arable land and in disturbed and natural terrestrial or hydrophytic ecological systems.
Journal Article