Analysis of Ploidy Levels of Korean Wild Asteraceae Species Using Chromosome Counting

Wu, Yan; Lim, Jin Hee; Jung, Jae A; Kim, Wonhee; Lim, Ki-Byung; Cabahug, Raisa Aone M.; Hwang, Yoon‐Jung

doi:10.11623/frj.2019.27.4.05

Cited by 5 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8). 한국의 야생국화 염색체 검정 결과 (Lee, 1969;Lee, 1975), 구절초의 배수성은 2x, 4x, 5x, 6x가 발견되었으며, 울릉국화는 4배체로 보고 (Yan et al, 2019)…”

Section: 배수성 검정unclassified

Collection and Morphological Analysis of Korean Wild Chrysanthemum zawadskii and C. makinoi

So,

Jung,

Lee

et al. 2024

HST

Self Cite

View full text Add to dashboard Cite

Over the course of seven years, different varieties of Chrysanthemum zawadskii were collected from 30 regions of Korea. All C. zawadskii varieties exhibit ray flowers that were either white or pink regardless of the collection region. However, they were classified into three groups according to the shape of the leaves, with large differences in the leaf shape (Group 1: C. zawadskii, Group 2: C. zawadskii with a slender leaf, and Group 3: C. zawadskii var. yezoense). A different species of Chrysanthemum called C. makinoi was assigned to Group 4. In terms of location, Group 1 can be found throughout Korea, while Group 2 is found primarily in highlands and confined locations such as Mt. Baekdu, Ulleung Island, and Mt. Halla on Jeju Island. Group 3 is found near the coast and has broad leaves with minimal splits, whereas Group 4 has been identified in Gangwon Province, a limestone location, with a variety of leaf shapes. One region was chosen for each group for an extensive comparison (Group 1: C. zawadskii var. latilobum from Mt. Hwangmae, Group 2: C. zawadskii var. lucidum from Ulleung Island, Group 3: C. zawadskii var. yezoense from Geoje Island, and Group 4: C. makinoi from Jeongseon). When morphological traits were compared by group, C. zawadskii var. lucidum (Group 2) had the widest flower diameter at 60.35 mm. Moreover, C. zawadskii (Group 1) and C. makinoi (Group 4) had the largest leaf length and width at the base, with no significant differences in the chlorophyll content. The cauline leaf of C. zawadskii var. yezoense (Group 3) had the greatest leaf length (38.79 mm), width (37.14 mm), and chlorophyll content (37.13 SPAD). However, the difference was not statistically significant when compared to the basal leaf. Ultimately, the results of this study showed that C. zawadskii var. latilobum (Group 1) and C. zawadskii var. yezoense (Group 3) were hexaploid, C. zawadskii var. lucidum (Group 2) was tetraploid, and C. makinoi (Group 4) was diploid. This research was carried out as part of a standardized procedure aimed at determining resources for use as basic data for Chrysanthemum genome research and functional breeding.

show abstract

Section: 배수성 검정unclassified

Collection and Morphological Analysis of Korean Wild Chrysanthemum zawadskii and C. makinoi

So,

Jung,

Lee

et al. 2024

HST

Self Cite

View full text Add to dashboard Cite

show abstract

“…including bias vectors b (1) , b (2) ; weight matrices W (1) , W (2) ; and activation functions G and s. These models are termed feed-forward because data progresses through the function being evaluated, starting from the input x, passing through the intermediate functions and computations used defining f, and finally reaching the output y.…”

Section: Mlp Input Vectormentioning

confidence: 99%

“…The proliferation of diverse breeding cultivars of chrysanthemums worldwide has made their identification increasingly challenging, even for experienced researchers, due to the complexities inherent in chrysanthemum cultivars, which pose significant challenges in their management and authorized protection. Several complications have emerged as unforeseen obstacles in cultivar recognition, including the vast number of cultivars, complex floral structure, diverse floret types, and the highly heterozygous genetic capacity [2]. Thus, there is an urgent requirement for the creation of efficient development methods for rapid and accurate phenotypic recognition characteristics.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Deep Neural Networks for Chrysanthemum Leaf and Flower Feature Recognition

Nguyen,

Dang,

Doan

et al. 2024

AgriEngineering

Self Cite

View full text Add to dashboard Cite

Chrysanthemums, a significant genus within the Asteraceae, hold a paramount position in the global floricultural industry, second only to roses in market demand. The proliferation of diverse chrysanthemum cultivars presents a formidable challenge for accurate identification, exacerbated by the abundance of varieties, intricate floral structures, diverse floret types, and complex genetic profiles. Precise recognition of chrysanthemum phenotypes is indispensable to navigating these complexities. Traditional methods, including morphology studies, statistical analyses, and molecular markers, have fallen short due to their manual nature and time-intensive processes. This study presents an innovative solution employing deep learning techniques for image-based chrysanthemum phenotype recognition. Leveraging machine learning, our system autonomously extracts key features from chrysanthemum images, converting morphological data into accessible two-dimensional representations. We utilized Support Vector Machine (SVM) and Multilayer Perceptron (MLP) algorithms to construct frameworks for processing image data and classifying chrysanthemum cultivars based on color, shape, and texture. Experimental results, encompassing 10 cultivars, 10 flower colors, and five flower shapes, consistently demonstrated recognition accuracy ranging from 79.29% up to 97.86%. This tool promises streamlined identification of flower traits, and we anticipate its potential for real-time identification enhancements in future iterations, promising advances in chrysanthemum cultivation and exportation processes. Our approach offers a novel and efficient means to address the challenges posed by the vast diversity within chrysanthemum species, facilitating improved management, breeding, and marketing strategies in the floricultural industry.

show abstract

“…), which belongs to the Asteraceae family, is a floricultural crop with high economic value, and is second only to roses in the floral trade market [1]. Wild chrysanthemums are attractive because of their flower color, leaf shape and type, and their secondary compounds, which are the main characteristics for discerning the distinctions between floral crops for horticultural studies [1,2]. Chrysanthemum boreale, a diploid species, displays small yellow flowers and shows a variety of morphological characteristics in natural growth in Korea [2].…”

Section: Introductionmentioning

confidence: 99%

“…Wild chrysanthemums are attractive because of their flower color, leaf shape and type, and their secondary compounds, which are the main characteristics for discerning the distinctions between floral crops for horticultural studies [1,2]. Chrysanthemum boreale, a diploid species, displays small yellow flowers and shows a variety of morphological characteristics in natural growth in Korea [2]. Wild C. boreale is present in numerous habitats, including in Gangwon-do, Gyeonggi-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeollabuk-do in Korea [2].…”

Section: Introductionmentioning

confidence: 99%

Wild Chrysanthemums Core Collection: Studies on Leaf Identification

et al. 2022

Self Cite

View full text Add to dashboard Cite

Wild chrysanthemums mainly present germplasm collections such as leaf multiform, flower color, aroma, and secondary compounds. Wild chrysanthemum leaf identification is critical for farm owners, breeders, and researchers with or without the flowering period. However, few chrysanthemum identification studies are related to flower color recognition. This study contributes to the leaf classification method by rapidly recognizing the varieties of wild chrysanthemums through a support vector machine (SVM). The principal contributions of this article are: (1) an assembled collection method and verified chrysanthemum leaf dataset that has been achieved and improved; (2) an adjusted SVM model that is offered to deal with the complex backgrounds presented by smartphone pictures by using color and shape classification results to be more attractive than the original process. As our study presents, the proposed method has a viable application in real-picture smartphones and can help to further investigate chrysanthemum identification.

show abstract

Analysis of Ploidy Levels of Korean Wild Asteraceae Species Using Chromosome Counting

Cited by 5 publications

References 27 publications

Collection and Morphological Analysis of Korean Wild Chrysanthemum zawadskii and C. makinoi

Collection and Morphological Analysis of Korean Wild Chrysanthemum zawadskii and C. makinoi

Utilizing Deep Neural Networks for Chrysanthemum Leaf and Flower Feature Recognition

Wild Chrysanthemums Core Collection: Studies on Leaf Identification

Contact Info

Product

Resources

About