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Abstract：
BACKGROUND: Avena fatua and A. sterilis are challenging to distinguish due to their strong similarities. However, Artificial Neural Networks (ANN) can effectively extract patterns and identify these species. We measured seed traits of Avena species from 122 locations across the Balkans and from some populations from southern, western, and central Europe (total over 22 000 seeds). The inputs for the ANN model included seed mass, size, color, hairiness, and placement of the awn attachment on the lemma.
RESULTS: The ANN model achieved high classification accuracy for A. fatua and A. sterilis (R2 > 0.99, RASE < 0.0003) with no misclassification. Incorporating geographic coordinates as inputs also resulted in successful classification (R2 > 0.99, RASE < 0.000001) with no misclassification. This highlights the significant influence of geographic coordinates on the occurrence of Avena species. The models revealed hidden relationships between morphological traits that are not easily detectable through traditional statistical methods. For example, seed color can be partially predicted by other seed traits combined with geographic coordinates. When comparing the two species, A. fatua predominantly had the lemma attachment point in the upper half, while A. sterilis had it in the lower half. A. sterilis exhibited slightly longer seeds and hairs than A. fatua, while seed hairiness and mass were similar in both species. A. fatua populations primarily had brown, light brown, and black colors, while A. sterilis populations had black, brown, and yellow colors.
CONCLUSIONS: Distinguishing A. fatua from A. sterilis based solely on individual characteristics is challenging due to their shared traits and considerable variability of traits within each species. However, it is possible to classify these species by combining multiple seed traits. This approach also has significant potential for exploring relationships among different traits that are typically difficult to assess using conventional methods.
RESULTS: The ANN model achieved high classification accuracy for A. fatua and A. sterilis (R2 > 0.99, RASE < 0.0003) with no misclassification. Incorporating geographic coordinates as inputs also resulted in successful classification (R2 > 0.99, RASE < 0.000001) with no misclassification. This highlights the significant influence of geographic coordinates on the occurrence of Avena species. The models revealed hidden relationships between morphological traits that are not easily detectable through traditional statistical methods. For example, seed color can be partially predicted by other seed traits combined with geographic coordinates. When comparing the two species, A. fatua predominantly had the lemma attachment point in the upper half, while A. sterilis had it in the lower half. A. sterilis exhibited slightly longer seeds and hairs than A. fatua, while seed hairiness and mass were similar in both species. A. fatua populations primarily had brown, light brown, and black colors, while A. sterilis populations had black, brown, and yellow colors.
CONCLUSIONS: Distinguishing A. fatua from A. sterilis based solely on individual characteristics is challenging due to their shared traits and considerable variability of traits within each species. However, it is possible to classify these species by combining multiple seed traits. This approach also has significant potential for exploring relationships among different traits that are typically difficult to assess using conventional methods.
摘要：
背景：Avenafatua和A。由于它们具有很强的相似性，因此难以区分。然而,人工神经网络（ANN）可以有效地提取模式并识别这些物种。我们从巴尔干地区的122个地点和南部的一些种群中测量了Avena物种的种子性状，西方,和中欧（总计超过22000个种子）。ANN模型的输入包括种子质量，尺寸,颜色,毛羽，以及引理上的芒附件的位置。
结果：ANN模型实现了A.fatua和A.sterilis的高分类精度（R2>0.99，RASE<0.0003），没有错误分类。将地理坐标作为输入也导致成功的分类(R2>0.99,RASE<0.000001)而没有错误分类。这凸显了地理坐标对Avena物种发生的重大影响。这些模型揭示了形态性状之间的隐藏关系，通过传统的统计方法不容易检测到。例如,其他种子性状结合地理坐标可以部分预测种子颜色。当比较这两个物种时，A.fatua主要在上半部分有引理附着点，而绝育在下半部分。A.灭菌的种子和毛发比A.fatua稍长，而两个物种的种子毛羽和质量相似。A.fatua种群主要是棕色的，浅棕色,和黑色的颜色，而绝育A种群有黑色，棕色,和黄色的颜色。
结论：仅根据个体特征来区分A.fatua和A.无菌是具有挑战性的，因为它们具有共同的性状和每个物种内性状的相当大的变异性。然而,可以通过组合多种种子性状对这些物种进行分类。这种方法对于探索通常难以使用常规方法评估的不同性状之间的关系也具有巨大的潜力。
结果：ANN模型实现了A.fatua和A.sterilis的高分类精度（R2>0.99，RASE<0.0003），没有错误分类。将地理坐标作为输入也导致成功的分类(R2>0.99,RASE<0.000001)而没有错误分类。这凸显了地理坐标对Avena物种发生的重大影响。这些模型揭示了形态性状之间的隐藏关系，通过传统的统计方法不容易检测到。例如,其他种子性状结合地理坐标可以部分预测种子颜色。当比较这两个物种时，A.fatua主要在上半部分有引理附着点，而绝育在下半部分。A.灭菌的种子和毛发比A.fatua稍长，而两个物种的种子毛羽和质量相似。A.fatua种群主要是棕色的，浅棕色,和黑色的颜色，而绝育A种群有黑色，棕色,和黄色的颜色。
结论：仅根据个体特征来区分A.fatua和A.无菌是具有挑战性的，因为它们具有共同的性状和每个物种内性状的相当大的变异性。然而,可以通过组合多种种子性状对这些物种进行分类。这种方法对于探索通常难以使用常规方法评估的不同性状之间的关系也具有巨大的潜力。
