2019
DOI: 10.9787/pbb.2019.7.3.175
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Analysis of Qualitative and Quantitative Traits to Identify Different Chinese Jujube Cultivars

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Cited by 4 publications
(3 citation statements)
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“…Morphological descriptors have been commonly used in jujube germplasm management (Liu et al 2020). Based on an evaluation of 25 morphological descriptors, Kim et al (2019) reported that the presence or absence of spikes and fruit shape were two descriptors with good stability for jujube cultivar differentiation. In the present study, we also found that jujube fruit shape plays a crucial role in cultivar and/or genotype identification and classification.…”
Section: Discussionmentioning
confidence: 99%
“…Morphological descriptors have been commonly used in jujube germplasm management (Liu et al 2020). Based on an evaluation of 25 morphological descriptors, Kim et al (2019) reported that the presence or absence of spikes and fruit shape were two descriptors with good stability for jujube cultivar differentiation. In the present study, we also found that jujube fruit shape plays a crucial role in cultivar and/or genotype identification and classification.…”
Section: Discussionmentioning
confidence: 99%
“…The Z. mauritiana tree with very short spines was the DP2L1, DP2L2, and DP2L3 accessions (domesticated in the backyard). Zhao (2021) stated that the climate affected the agromorphological traits of ecotypes, such as spine length (Stoyanov 2015;Kim et al 2019).…”
Section: Variation In Spine and Bark Of Z Mauritaniamentioning
confidence: 99%
“…In the case of cultivar discrimination of fruit seeds or pits and stones, the high efficiency of models based on texture parameters was reported for pepper seeds [21], apple seeds [22], peach seeds and stones [23], sour cherry pits [24], and sweet cherry pits [25]. Furthermore, the geometric features proved to be useful for the pit or stone discrimination for different cultivars of apricot [26], plum [27][28][29], olive [30], jujube [31], and sweet cherry [25]. However, in the present study, extensive research using dozens of geometric parameters, including linear dimensions and shape factors, was performed for the first time to discriminate sour cherry pits 'Debreceni botermo', 'Łutówka', 'Nefris', 'Kelleris' using different classifiers (machine learning algorithms).…”
Section: Introductionmentioning
confidence: 99%