Classification Of Plants Research Articles

Medicinal Plants Classification of Minangkabau and Mentawai; Structuralism Levi-Strauss Perspective

In 2012, research in medicinal plant species discovered around 297 different types in the Simanau and Maek villages of the Minangkabau region in West Sumatra, Indonesia. Sixty three of those were considered rare species, endangered with extinction. If it is studied deeply, it is possible that the types of medicinal plants in vilages of the Minangkabau region can amount to thousands of species. For this research, the structure of knowledge on practicing healer understood through ethnographic studies. This research result that the diversity and types of medicinal plants in Simanau village could be classified into four groups. This classification, known as ureh nan ampek, consisting of sitawa (antidote), sidingin (fresh taste), cikarau (defender) and cikumpai (destroyer). This suggests that the practicing healers have knowledge in recognizing the different types of plants that can be used for treatment, and have knowledge about how and what medicinal plants are used. This article attempts to understand the structure of knowledge of the practicing healers in Simanau village about the types and the classification of the medicinal plants.

Medicinal Plants Classification of Minangkabau and Mentawai; Structuralism Levi-Strauss Perspective

In 2012, research in medicinal plant species discovered around 297 different types in the Simanau and Maek villages of the Minangkabau region in West Sumatra, Indonesia. Sixty three of those were considered rare species, endangered with extinction. If it is studied deeply, it is possible that the types of medicinal plants in vilages of the Minangkabau region can amount to thousands of species. For this research, the structure of knowledge on practicing healer understood through ethnographic studies. This research result that the diversity and types of medicinal plants in Simanau village could be classified into four groups. This classification, known as ureh nan ampek, consisting of sitawa (antidote), sidingin (fresh taste), cikarau (defender) and cikumpai (destroyer). This suggests that the practicing healers have knowledge in recognizing the different types of plants that can be used for treatment, and have knowledge about how and what medicinal plants are used. This article attempts to understand the structure of knowledge of the practicing healers in Simanau village about the types and the classification of the medicinal plants.

Soybean iron deficiency chlorosis high-throughput phenotyping using an unmanned aircraft system

BackgroundIron deficiency chlorosis (IDC) is an abiotic stress in soybean [Glycine max (L.) Merr.] that causes significant yield reductions. Symptoms of IDC include interveinal chlorosis and stunting of the plant. While there are management practices that can overcome these drastic yield losses, the preferred way to manage IDC is growing tolerant soybean varieties. To develop varieties tolerant to IDC, breeders may easily phenotype up to thousands of candidate soybean lines every year for severity of symptoms related to IDC, a task traditionally done with a 1–5 visual rating scale. The visual rating scale is subjective and, because it is time consuming and laborious, can typically only be accomplished once or twice during a growing season.ResultsThe goal of this study was to use an unmanned aircraft system (UAS) to improve field screening for tolerance to soybean IDC. During the summer of 2017, 3386 plots were visually scored for IDC stress on two different dates. In addition, images were captured with a DJI Inspire 1 platform equipped with a modified dual camera system which simultaneously captures digital red, green, blue images as well as red, green, near infrared (NIR) images. A pipeline was created for image capture, orthomosaic generation, processing, and analysis. Plant and soil classification was achieved using unsupervised classification resulting in 95% overall classification accuracy. Within the plant classified canopy, the green, yellow, and brown plant pixels were classified and used as features for random forest and neural network models. Overall, the random forest and neural network models achieved similar misclassification rates and classification accuracy, which ranged from 68 to 77% across rating dates. All 36 trials in the field were analyzed using a linear model for both visual score and UAS image-based scores on both dates. In 32 of the 36 tests on date 1 and 33 of 36 trials on date 2, the LSD associated with UAS image-based scores was lower than the LSD associated with visual scores, indicating the image-based scores provided more precise measurements of IDC severity.ConclusionsOverall, the UAS was able to capture differences in IDC stress and may be used for evaluations of candidate breeding lines in a soybean breeding program. This system was both more efficient and precise than traditional scoring methods.

Documenting the collections of the Botanical Garden of the Saint Petersburg Forest Technical University for the purpose of international standardization of education

s. The Botanical Garden of the Saint Petersburg Forest Technical University was established in 1827. It is one of the oldest botanical gardens in Russia. It is also the first botanical garden in the country, which was created for educational purposes. The collection of woody plants includes over 1600 taxonomic units, about 4000 open-field flower crops, some 1300 tropical and subtropical plants. Cataloging of the collection since 2018 is based on modern classifications based on molecular studies (APG, PPG, etc.). This internationally recognized plant classification approach is necessary to organize collections, publications, and enhance standardization of the university education.

Medicinal Plants Segmentation using Thresholding and Edge based Techniques

Medicinal value is the integral essence and active ingredients in plants. Knowledge about medicinal plants is more essential to mankind. Manual identification of medicinal plant requires prior information and needs the assistance of botanist because all the plants have similar color, shape and texture characteristics and it is time consuming process. Due to this reason it is very much essential for Pharmacists, Botanist and Ayurveda Practitioners to know how to identify medicinal plants through computer technologies. Image processing technology is used for identification and classification of medicinal plants. The following three general phases of image processing techniques are applied to all types of images namely pre-processing, segmentation and feature extraction. Among all the stages of image processing, segmentation phase partition an image into number of regions and segment the object from background. It improve the representation of an image into more meaningful and easier to analyze the features. The medicinal plants segmentation is carried out in this proposed work. The common and famous segmentation techniques like edge detection and thresholding methods are applied on different shapes of flowers, leaves and fruits/seeds images. This paper explores the comparisons of above two methods and effects of filtering and enhancement techniques.

Automated crop plant detection based on the fusion of color and depth images for robotic weed control

AbstractRobotic weeding enables weed control near or within crop rows automatically, precisely and effectively. A computer‐vision system was developed for detecting crop plants at different growth stages for robotic weed control. Fusion of color images and depth images was investigated as a means of enhancing the detection accuracy of crop plants under conditions of high weed population. In‐field images of broccoli and lettuce were acquired 3–27 days after transplanting with a Kinect v2 sensor. The image processing pipeline included data preprocessing, vegetation pixel segmentation, plant extraction, feature extraction, feature‐based localization refinement, and crop plant classification. For the detection of broccoli and lettuce, the color‐depth fusion algorithm produced high true‐positive detection rates (91.7% and 90.8%, respectively) and low average false discovery rates (1.1% and 4.0%, respectively). Mean absolute localization errors of the crop plant stems were 26.8 and 7.4 mm for broccoli and lettuce, respectively. The fusion of color and depth was proved beneficial to the segmentation of crop plants from background, which improved the average segmentation success rates from 87.2% (depth‐based) and 76.4% (color‐based) to 96.6% for broccoli, and from 74.2% (depth‐based) and 81.2% (color‐based) to 92.4% for lettuce, respectively. The fusion‐based algorithm had reduced performance in detecting crop plants at early growth stages.

Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation

AbstractQuestionsHow to map floristic variation in a patterned fen in an ecologically meaningfully way? Can plant communities be delineated with species data generalized into plant functional types? What are the benefits and drawbacks of the two selected remote‐sensing approaches in mapping vegetation patterns, namely: (a) regression models of floristically defined fuzzy plant community clusters and (b) classification of predefined habitat types that combine vegetation and land cover information?LocationTreeless 0.4 km2 mesotrophic string–flark fen in Kaamanen, northern Finland.MethodsWe delineated plant community clusters with fuzzy c‐means clustering based on two different inventories of plant species and functional type distribution. We used multiple optical remote‐sensing data sets, digital elevation models and vegetation height models derived from drone, aerial and satellite platforms from ultra‐high to very high spatial resolution (0.05–3 m) in an object‐based approach. We mapped spatial patterns for fuzzy and crisp plant community clusters using boosted regression trees, and fuzzy and crisp habitat types using supervised random forest classification.ResultsClusters delineated with species‐specific data or plant functional type data produced comparable results. However, species‐specific data for graminoids and mosses improved the accuracy of clustering in the case of flarks and string margins. Mapping accuracy was higher for habitat types (overall accuracy 0.72) than for fuzzy plant community clusters (R2 values between 0.27 and 0.67).ConclusionsFor ecologically meaningful mapping of a patterned fen vegetation, plant functional types provide enough information. However, if the aim is to capture floristic variation in vegetation as realistically as possible, species‐specific data should be used. Maps of plant community clusters and habitat types complement each other. While fuzzy plant communities appear to be floristically most accurate, crisp habitat types are easiest to interpret and apply to different landscape and biogeochemical cycle analyses and modeling.

Species Richness of Pteridophyta in Mount Ungaran

MountUngaranisone areathathas thenaturalforestandhas a diverseofbiodiversity flora and fauna in Central Java. One of flora such as Pteridophyta can be found in Mount Ungaran. Pteridophyta has many beneficial to human, but knowledge of the localcommunityisstilllowassociatedwithit.Theobjectiveoftheresearchedwasknow the species richness of Pteridophyta in Mount Ungaran. This research was conducted on March – July 2018. Location of this research in Bukit Gentong, Mount Ungaran. The research used exploration method, and identification of Petridophyta using a guidebook such as Plant Systematics, Flora for Schools in Indonesia, Encyclopedia of Garden Ferns, and Pteridophyta. The data analysis using descriptive qualitative. The results showed a total of 21 species of Pteridophyta was found belonging to five classes,sixorderand11families.ThehighestofspeciesrichnesswerePteridaceae(six species), Polypodiaceae (five species) and the lowest species richness of Pteridophyta are Davalliaceae (two species) and also in eight other families (one species).

Indigenous Botanical Nomenclature Used by the Yi People in Liangshan Prefecture, Sichuan Province, China

With increasing outside cultural influences and economic development among the Yi people of Liangshan Prefecture, Sichuan, China, we sought to document traditional Yi language plant names and classification methods. Over 15 consecutive years (2003–2017), we used ethnobotanical surveys and interviews to investigate local knowledge of common plants in Liangshan. We collected Yi names of 322 plant taxa from 109 families and analyzed the Liangshan Yi’s indigenous botanical nomenclature. Many Yi language plant names include a suffix bbo, meaning “plant,” or syr meaning “tree.” Plants foundational to Yi culture, such as buckwheat, hemp, and bamboo, have monosyllabic names on which other plant names, compound words, and place names are built. The Liangshan Yi have a binomial nomenclature system used especially for crops, with “folk generics” based on plant life form or economic use, and “folk species/varieties” based on plant characteristics or habitat. Liangshan Yi generally name introduced plants by borrowing names from Mandarin Chinese in two ways: (1) borrowed sound (transliteration) or (2) borrowed sound combined with Yi morphemes (loan blends). With increased introduction of non-native plants into Liangshan, and greater market interactions between Han and Yi communities in Liangshan, traditional Yi plant naming methods are at risk of gradually being replaced by Han Chinese methods. Standardized education in Mandarin Chinese, increased Han migration to Liangshan, and changing lifestyles also contribute to decrease in knowledge and usage of Liangshan Yi plant names.

Optimal solutions for an automatic control system for medium capacity power supply facilities using renewable energy sources

Introduction: extensive logistics costs (including long distance transportation and high fuel prices) and a high cost of electric energy, generated by diesel power plants, are the main problem of power supply to consumers by off-grid power systems. The author unlocks the potential of hybrid power systems using renewable energy sources and saving expensive fuel. The author offers the analysis of automatic control systems capable of improving the efficiency of subordinate power plant elements.Methods: the author provides a classification of wind-diesel power plants and describes their performance pro rata to the share of wind energy consumption. The author has also compiled a set of specifications applicable to the technological solutions of wind-driven power plants. He also formulates the principles underlying an intelligent automatic control system for off-grid power supply facilities. This system served as the basis for a software and hardware module designed and developed for an intelligent power conversion/control/distribution system. The author provides diagrams of (1) electrical circuits of a software and hardware module for an intelligent power conversion/control/distribution system, and (2) the operation of an off-grid power supply package using renewable energy sources and controlled by the power conversion/control/distribution system. The author analyzes the primary and secondary sources of power in an off-grid power supply facility and describes principles of their operation within a software and hardware module.Results and discussions: the author offers a methodology of intelligent control over off-grid power supply facilities within the framework of the project for development of a wind-diesel power plant in thevillage ofAmderma. The author describes the results of incorporation of a wind-diesel power plant into the power supply facility operating in the permafrost environment of the Arctic region.Conclusion: presently,Russia has pre-conditions in place capable of boosting the development of power supply technologies using hybrid facilities comprising renewable energy sources. The implementation of such off-grid power supply facilities, controlled by newly designed software and hardware modules designated for an intelligent power conversion/ control/distribution system optimizes electric power generation and consumption modes and substantially reduces fuel consumption by diesel power plants.

Plant systematics and biogeography in the Australasian tropics: part 2

[Extract:] This double issue is the second on the topic of plant systematics and biogeography in the Australasian tropics published in Australian Systematic Botany. The first issue on this topic (vol. 31, 5–6) was published in 2018 and brought together ten papers that contributed original research in a wide range of disciplines, including biogeography, developmental anatomy, evolutionary radiation and taxonomy. This second special issue follows directly on, and showcases 10 additional papers, many across those same disciplines; however, several fascinating papers expand the range of disciplines represented. These include palaeobotanical work that contributes critical new information on the history of horsetails (Equisetum) in Australia (Rozefelds et al. 2019); a detailed account of the taxonomic work on Australian plants of the Wendland family, Court Gardeners at the Royal Gardens of Herrenhausen, Hanover, Germany (Dowe et al. 2019), a little known aspect of Australian botanical history; and lastly a demonstration of the utility of genome skimming approaches for untangling evolutionary relationships in species-rich and morphologically complex tropical Australasian groups, using the carnivorous plant genus Nepenthes as an example (Nauheimer et al. 2019).

A hybrid approach combining CUR matrix decomposition and weighted kernel sparse representation for plant leaf recognition

Plant species recognition is one of the important and difficult research areas. Many existing plant recognition and classification methods cannot meet the requirements of the automatic plant recognition system, due to the irregular, complex, and diverse nature of plant leaves. In this paper, a plant recognition approach is proposed by combining CUR decomposition and weighted kernel sparse representation (WKSR). The proposed method is different from traditional plant leaf classification methods. Instead of establishing a classification model by extracting the color, shape, and texture classifying features, the proposed method directly reduces the dimensionality of image and recognizes the test samples based on WKSR coefficients. In order to reduce the recognition time the proposed method uses class specific dictionary learning for sparse modeling. By using the comparison analysis, the proposed method is verified on four plant leaf datasets namely, Flavia leaf dataset, Swedish leaf dataset, Original leaf images and Leafsnap dataset with existing plant classification methods such as Random Forest, Support Vector Machine and K-Nearest Neighbors. The accuracy of the proposed method is 98% which is the best classification rate.

Helitron distribution in Brassicaceae and whole Genome Helitron density as a character for distinguishing plant species

BackgroundHelitron is a rolling-circle DNA transposon; it plays an important role in plant evolution. However, Helitron distribution and contribution to evolution at the family level have not been previously investigated.ResultsWe developed the software easy-to-annotate Helitron (EAHelitron), a Unix-like command line, and used it to identify Helitrons in a wide range of 53 plant genomes (including 13 Brassicaceae species). We determined Helitron density (abundance/Mb) and visualized and examined Helitron distribution patterns. We identified more than 104,653 Helitrons, including many new Helitrons not predicted by other software. Whole genome Helitron density is independent from genome size and shows stability at the species level. Using linear discriminant analysis, de novo genomes (next-generation sequencing) were successfully classified into Arabidopsis thaliana groups. For most Brassicaceae species, Helitron density negatively correlated with gene density, and Helitron distribution patterns were similar to those of A. thaliana. They preferentially inserted into sequence around the centromere and intergenic region. We also associated 13 Helitron polymorphism loci with flowering-time phenotypes in 18 A. thaliana ecotypes.ConclusionEAHelitron is a fast and efficient tool to identify new Helitrons. Whole genome Helitron density can be an informative character for plant classification. Helitron insertion polymorphism could be used in association analysis.

Light and Scanning Electron Microscopy of Some Species of Genus Solanum (Solanaceae) With Special Referance to the Importance of Trichomes

The trichomes are taxonomically very important because of their use in plant classification with other morphological characters. This study provides information about trichomes of six species add name of species here of genus Solanum. This study will also helpful to produce more information to develop modern nomenclature for trichomes. The plant material was taken from Sindh University Herbarium as well as new plant substance was gathered from the field for the study. During the trichomal analysis the plant components were observed under stereo zoom microscope. Provisional and long-term slides were produced. For the scanning electron microscopic observation, the plant material was treated with ethanol alcohol for 20 minutes, ca 1mm2 piece of specimens were fitted on the stubs made of metal with double-sided cellophane tape and also covered with C-30-50 nm gold by Jeol JFC- 1500 Ion sputter machine. After coating samples were observed as well as pictures were captured on different magnifications by enhancing voltage between 5-15 KV by Scanning Electron Microscope (SEM) Jeol JSM-T200 & Jeol JSM-T6380 at Biological Research Centre and Central Research Laboratory Karachi, University of Karachi.

Plant Letters: A citizen science project uncovering historical biodiversity data

The XIXth century saw an enormous accumulation of biological specimens coming to Europe from all over the world, which are now part of museums, herbaria and other natural history collections. For many centuries, the exchange of letters was the privileged means of circulating information and knowledge. At the University of Coimbra (UC), the Life Sciences Department safeguards almost 5000 letters and other documentation addressed to directors, gardeners and other collaborators of the Botanic Garden. These records of Portuguese botanical science and expeditions of plant discovery, collection and identification are held in thousands of handwritten letters, species lists and assorted notebooks, in more than five different languages. Historical repositories such as this archive, but also the biological and museum collections and objects that it documents, imply added responsibilities to the University of Coimbra, as the information contained within the documentation, pertains not only to a country (in this case Portugal), but also to its developing historical roles and actions. As a colonial power for many centuries, the records of Portuguese scientific activity and occupation strategies of overseas territories, in Africa, South America, Asia and the Pacific, are also documented, and its valuable data (e.g., historical biological records) is of crucial importance to these now independent countries. Received correspondence is a great resource for understanding the process of knowledge creation and circulation in the plant sciences, including botany and agriculture, as well as the scientific colonial practices and their implication for the amassing of biological collections at the UC. In order to uncover historical biodiversity data within this archival material, we have implemented Plant Letters (https://www.zooniverse.org/projects/catedraunesco/plant-letters), a citizen science transcription project that seeks to uncover the stories within these historical archives, the tales of travelers and scientists, on the quest of recording of the world’s diversity, mostly in the Portuguese ex-colonies in sub-Saharan Africa. Using the collaborative platform Zooniverse (https://www.zooniverse.org), users are requested to engage with the archive and transcribe mostly handwritten letters in several languages, giving dimension to our continuous efforts of promoting open and widespread access to information. The project invites everyone to transcribe handwritten or typed letters received by the Botanic Garden between about 1870 and 1928, from more than 1100 correspondents from around the world. The main purpose of this project is to track plant species, locations and scientists in the correspondence received in the 19th and 20th centuries at the Botanic Garden of the University of Coimbra. To do so, Plant Letters seeks in users, both experts or simply curious, a source of participation in the construction of knowledge, making use of collective intelligence, in a lively exchange of information, experiences and knowledge. In transcribing the letters, we want to retrieve information that can include: inquiries and doubts about plant classification and taxonomy; historical plant species locations, distribution records and abundance; biological material circulation (plant and seed exchanges); track the path of herbarium and museum specimens in our collections; unravel networks of botanical knowledge. inquiries and doubts about plant classification and taxonomy; historical plant species locations, distribution records and abundance; biological material circulation (plant and seed exchanges); track the path of herbarium and museum specimens in our collections; unravel networks of botanical knowledge. Transcribing the information contained in these documents will allow us to: track plant specimens as they travelled from their native countries to the scientists who named them; to determine historical plant locations in parts of sub-Saharan Africa; to better understand the scientific processes of plant discovery, taxonomy and botany; and to collect information that gives context to biological specimens in museum objects and other natural history collections. All of these data, valuable to the present and future conservation of tropical flora, will be made available, bearing in mind the open science principles.

An integrated medicine of Bhutan: Sowa Rigpa concepts, botanical identification, and the recorded phytochemical and pharmacological properties of the eastern Himalayan medicinal plants

IntroductionBhutanese Sowa Rigpa medicine (BSM) has established unique pharmacopoeias that describe ethnotaxonomical concepts, classification systems and descriptions of medicinal plants. BSM was integrated with modern health care system in 1967. BSM uses medicinal plants, minerals and animal products as the main ingredients of the formulations used for treating various disorders. This study was aimed to highlight interesting ethnotaxonomical concepts of BSM and identify and document medicinal plants from the two remotest regions of eastern Himalayas of Bhutan – Merak and Sakteng. MethodsEthnopharmacological data was gathered from the pharmacopoeias, published monographs and research papers on Bhutanese medicinal plants. Medicinal plants diversity was surveyed using the convenience sampling methods. A literature review on the phytochemical content of the medicinal plants and their pharmacological activities was carried out using plant databases, PubMed, SciFinder and Google Scholar. ResultsA very interesting ethnotaxonomical concept of BSM has been translated into English terminologies from the traditional pharmacopoeia. A total of 50 alpine medicinal plants belonging to 25 families and 39 genera in 13 study sites between Merak and Sakteng were documented. Only 12 medicinal species were identified as priority species or are currently used at Menjong Sorig Pharmaceuticals for formulating 48 different medicines. ConclusionsThis study described the ethnotaxonomical concepts, classification and description of medicinal plants in Bhutan and botanically identified 50 alpine medicinal plants from 13 different study sites under Merak and Sakteng regions. About 12 of them were currently used as ingredients in BSM formulations and 35 species were studied for their phytochemical and pharmacological properties.

An electrochemical method for plant species determination and classification based on fingerprinting petal tissue

The identification of plant species not only is a hobby but also has important application value in plant resources science. Traditional plant identification often relies on the experience of botanists. The infrageneric identification of plants is easily mistaken due to similarities in organ features. In this work, we propose an electrochemical method to obtain fingerprints of plant petal tissue. Fourteen species of Lycoris were used as a model for validating this methodology. Pattern and color recognition were established for visualization of electrochemical fingerprints recorded after various solvent extractions. In addition, the infrageneric relationships of these Lycoris species were deduced from the electrochemical fingerprints since the type and content of electroactive compounds in plants are controlled by genes. The results indicate that the electrochemical fingerprints of Lycoris petals are correlated with the infrageneric relationships of native Lycoris species.

SHADOW DETECTION IN HYPERSPECTRAL IMAGES ACQUIRED BY UAV

Abstract. Shadows are common in any kind of remote sensing images. Unmanned Aerial Vehicle – UAV with a light camera attached can acquire images illuminated either by direct sunlight or by diffuse light under clouds. Indeed, areas with pixels shaded by clouds must be detected and labelled in order to use this additional information for image analysis. Classification of health and diseased plants in permanent culture as the orange plantation field can present some errors due to tree cast shadow. So, hyperspectral or multispectral image classification can be improved by previous shadow detection. Some FPI hyperspectral camera, designed for agricultural applications is limited in the spectral range between 500 to 900 nm. Wavelengths in the region of blue light and in the SWIR spectral region have physical properties that enable the enhancement of shaded regions in the images. In this work some combinations of different spectral bands were evaluated in order to specify those suitable to detect shadows in agricultural field images. In this sense, considering that vegetation and soil are the two main kind of coverage in an agricultural field, we hypothesized that wavelengths near blue light and the longest near infrared available in the camera range are good choices. In both spectral regions soil and vegetation targets have small spectral differences which contribute to enhance the differences between shaded and illuminated regions in the image. Hyperspectral images acquired with a FPI hyperspectral camera onboard a UAV over a plantation of oranges were used to evaluate these spectral bands. The results showed that the wavelengths of aproximatelly 510 nm and 840 nm available in the FPI camera are the best to detect any type of shadows in the agricultural fields.

IAPT Research Grants Program in Plant Systematics

No abstract is available for this article.

Physicochemical characterization of blueberry (Vaccinium spp.) juices from 55 cultivars grown in Northern Spain

Blueberry (Vaccinium spp.) cultivation has experienced a notable increase both for its good organoleptic characteristics and the nutritional and functional properties of this berry. The aim of this study was the physicochemical characterization of blueberry juices obtained from 55 blueberry cultivars grown under the same environmental conditions for 2–4 years. The results provide a broad and robust database, both for the number of cultivars and the periods of monitoring thereof, in order to cover different aspects of blueberry processing, and more specifically, production of juices. Blueberries belonging to V. virgatum cultivars showed the higher values of total anthocyanin content, total phenolic content, antioxidant activity, soluble solids, and pH, and V. corymbosum cultivars the higher level of titratable acidity. Results also showed a high variability among cultivars. Observed variations can be used in plant breeding and classification of blueberry cultivars, at least, at the species level.