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Differentiation of sweet chestnut (Castanea sativa Mill.) cultivars by leaf, nut and burr dimensions.

机译:甜栗(Castanea sativa Mill。)品种通过叶片,坚果和毛刺尺寸的区分。

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摘要

Morphological traits including leaf, nut and burr dimensions were measured in chestnut stands in Fonsagrada (Galicia, northwest Spain). A total of 5760 data (720 measurements of eight variables) from 72 chestnuts belonging to the three main cultivars growing in the area (Lemos, Parede and Portuguesa) were available for analysis. The variables include: leaf length, leaf width, leaf stalk length, nut length, nut width, nut thickness, burr height, and burr largest width. Two discriminant linear rules/models for classifying chestnut specimens as one of the three cultivars were developed. The models differed in terms of the sampling effort involved - the full model included all the analysed variables (leaf-, nut- and burr-related variables) and the leaf and nut model included only variables related to leaves and nuts. The full model performed best and correctly classified 79% of the Lemos, 96% of the Parede and 100% of the Portuguesa cultivars, according to the results of the cross-validation method used to assess the misclassified cultivars. High accuracy was also achieved with the leaf and nut model, with only a slight increase in misclassification compared with the full model. Therefore, when time consumption is an important concern for users, the leaf and nut model is recommended as a good compromise between accuracy and sampling effort..
机译:在丰萨格拉达(西班牙西北部加利西亚)的板栗林中测量了叶片,坚果和毛刺尺寸等形态特征。来自该地区三个主要品种(Lemos,Parede和Portuguesa)的72个栗子的总共5760个数据(八个变量的720个测量值)可供分析。变量包括:叶片长度,叶片宽度,茎秆长度,螺母长度,螺母宽度,螺母厚度,毛刺高度和毛刺最大宽度。建立了两个区分线性规则/模型以将栗子标本分类为三个品种之一。这些模型在所涉及的采样工作方面有所不同-完整模型包括所有分析变量(与叶,坚果和毛刺相关的变量),而叶和坚果模型仅包括与叶和坚果相关的变量。根据用于评估错误分类品种的交叉验证方法的结果,完整模型表现最佳,正确分类了79%的Lemos,96%的Parede和100%的Portuguesa。叶片和螺母模型也实现了高精度,与完整模型相比,分类错误仅稍有增加。因此,当时间消耗对于用户来说是一个重要问题时,建议使用叶片和螺母模型,以在准确性和采样工作之间取得良好的平衡。

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