The quality of cotton yarn, such as evenness and strength, relies on not only the overall length of spun fibers but also fiber length uniformity. In the current cotton classification system (Cotton Incorporated in Classification of upland cotton, 2018; USDA in The classification of cotton, Agricultural Marketing Service, Washington, DC, 1995), length uniformity is measured by a single factor—uniformity index (UI), which does not explicitly include short fiber content (SFC) and neglects the interactive effects among length attributes. The goal of this study was to search for key length attributes and new classification methods for more comprehensive evaluations of cotton length uniformity. We firstly investigated the associations of length attributes measurable by the dual-beard fibrography (DBF) (Zhou et al. in Text Res J 90(1):37–48, 2020) to select a set of key features to reduce the dimensionality for consecutive statistical analysis. This set contains an overall length attribute (upper half mean length—UHML), SFC and UI that represent more realistic information about cotton quality. We then used the K-means clustering to determine the natural clusters of the length uniformity based on the data of 29 selected cotton samples that have a wide range of fiber length distributions. The clustering resulted in six optimal clusters, each representing a group of homogeneous length attributes. Thirdly, we adopted one support-vector-machine (SVM) classifier for cotton length uniformity prediction on unknown fibers. To verify the prediction accuracy, 25 new specimens were taken from the 29 samples used in the K-mean clustering to run the DBF test and the SVM classification. It was found that 92% of these specimens yielded the same cluster numbers as the ones resulted from the clustering. In summary, UHML, SFC and UI represent more comprehensive length attributes of cotton, and the six new clusters from the K-mean clustering offer more holistic evaluation on cotton length uniformity.

棉纱的质量（例如均匀性和强度）不仅取决于纺制纤维的总长度，还取决于纤维长度的均匀性。在当前的棉花分类系统中（Cotton Incorporated在《陆地棉分类》中，2018； USDA在《棉花分类》中，农业销售局，华盛顿特区，1995年），长度均匀性是通过单一因素（均匀度指数（UI），它没有明确包含短纤维含量（SFC），而忽略了长度属性之间的交互作用。这项研究的目的是寻找关键长度属性和新的分类方法，以对棉花长度均匀性进行更全面的评估。我们首先研究了可通过双胡须纤维化（DBF）测量的长度属性的关联（Zhou等人在Text Res J 90（1）：37–48，2020）选择一组关键特征以降低维数，以便进行连续的统计分析。这组包含一个总长度属性（上半部平均长度-UHML），SFC和UI，它们代表有关棉花质量的更实际的信息。然后，我们使用K均值聚类基于29个选定的棉纤维长度分布范围广泛的样本的数据来确定长度均匀性的自然聚类。聚类产生六个最佳聚类，每个聚类代表一组均一的长度属性。第三，我们采用了一种支持向量机（SVM）分类器来预测未知纤维的棉长均匀度。为了验证预测准确性，从K中使用的29个样本中提取了25个新样本-平均集群以运行DBF测试和SVM分类。发现这些标本中有92％产生的聚类数与聚类产生的聚类数相同。综上所述，UHML，SFC和UI代表了棉花的更全面的长度属性，而K均值聚类的六个新聚类对棉花长度均匀性提供了更全面的评估。