Natural color dispersion of corbicular pollen limits color-based classification

Parzival Borlinghaus , Frederic Tausch , Richard Odemer
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Abstract

Various methods have been developed to assign pollen to its botanical origin. They range from technically complex approaches to the less precise but sophisticated chromatic assessment, in which the pollen colors are used for identification. However, a common challenge lies in the similarity of colors of pollen from different plant species. The advent of camera-based bee monitoring systems has sparked renewed interest in classifying pollen based on color and offers potential advances for honey bee biomonitoring. Despite the promise of improved sensor accuracy, a critical examination of whether color diversity within a single species may be the primary limiting factor has been lacking. Our comprehensive analysis, which includes over 85,000 corbicular pollen from 30 major pollen species, shows that the average color variation within each species is distinguishable to a human observer, similar to the difference between two dissimilar colors. From today's perspective, the considerable color variation within a single pollen source makes the use of color alone to classify pollen impractical. When picking a single pollen color from the entire dataset, we report a correct pollen type classification rate of 67 %. The accuracy was highly dependent on the type and ranged from 0 % for rare types with common colors to 99 % for distinct colors. The large color dispersion within species highlights the need for complementary methods to improve the accuracy and reliability of color-based pollen identification in biomonitoring applications.

Abstract Image

花冠花粉的自然色分散限制了基于颜色的分类
目前已开发出多种方法来确定花粉的植物来源。这些方法既有技术上复杂的方法,也有不太精确但复杂的色度评估方法,即利用花粉的颜色进行鉴定。然而,一个共同的挑战在于不同植物物种花粉颜色的相似性。基于摄像头的蜜蜂监测系统的出现再次激发了人们对根据颜色对花粉进行分类的兴趣,并为蜜蜂生物监测提供了潜在的进展。尽管传感器的准确性有望得到提高,但对于单一物种内的颜色多样性是否会成为主要限制因素,一直缺乏批判性的研究。我们的综合分析包括来自 30 个主要花粉物种的 85,000 多枚鸡冠花粉,结果表明,对于人类观察者来说,每个物种内部的平均颜色变化是可以区分的,类似于两种不同颜色之间的差异。从今天的角度来看,单个花粉源的颜色差异很大,因此仅用颜色来对花粉进行分类是不切实际的。从整个数据集中挑选单一花粉颜色时,我们的花粉类型分类正确率为 67%。准确率在很大程度上取决于花粉类型,对于具有常见颜色的稀有类型,准确率为 0%,而对于具有独特颜色的类型,准确率则高达 99%。物种内部颜色的巨大分散性凸显了在生物监测应用中提高基于颜色的花粉识别准确性和可靠性的补充方法的必要性。
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