Abstract Pollen grains are valuable paleoclimate and paleovegetation proxies which require extensive knowledge of morphotypes and long acquisition time under the microscope. The abundance of damaged, folded, and broken pollen grains in the fossil register and sometimes also in modern soil and sediment samples, has so far prevented automation of pollen identification. Recent improvements in machine learning, however, have allowed reconsidering this approach. Here we present an automated approach which is capable of assisting palynologists with poorly preserved pollen samples. Called multi-CNNs, this approach is based on multiple convolutional neural networks (CNNs) integrated in a decision tree system. To test it, we built a system designed for three botanical families very common in the modern and fossil pollen assemblages of Eastern Africa, namely Amaranthaceae, Poaceae, and Cyperaceae. Our system was tested on stacked optical images of 8 pollen types (6 Amaranthaceae, 1 Poaceae, 1 Cyperaceae) using a training dataset of 1102 intact pollen grains and three validation datasets of intact (276 grains), damaged (223 grains), and fossil pollen (97 grains). We show that our system successfully recognizes intact, damaged, and fossil pollen grains with very low misclassification rates of 0%, 2.8%, and 3.7%, respectively. The use of augmentation on stacked optical images during the training increases classification accuracy. Following a palynologist's approach, our system allows grains without obvious characters to be classified into a class of high taxonomic level or as indeterminable pollen. This is the first software able to process grains with a wide range of taphonomical stages, which makes it the first truly applicable to automated pollen identification of fossil material.

中文翻译：

---

现代、化石、完整和受损花粉粒的多重卷积神经网络自动识别

摘要 花粉粒是宝贵的古气候和古植被代理，需要广泛的形态知识和显微镜下的长采集时间。化石登记册中，有时还有现代土壤和沉积物样本中大量受损、折叠和破碎的花粉粒，迄今为止阻碍了花粉识别的自动化。然而，最近机器学习的改进允许重新考虑这种方法。在这里，我们提出了一种自动化方法，能够帮助孢粉学家处理保存不佳的花粉样本。这种方法称为多 CNN，基于集成在决策树系统中的多个卷积神经网络 (CNN)。为了测试它，我们为东非现代和化石花粉组合中非常常见的三个植物科构建了一个系统，即苋科、禾本科和莎草科。我们的系统在 8 种花粉类型（6 种苋科、1 种禾本科、1 种莎草科）的堆叠光学图像上进行了测试，使用了 1102 种完整花粉粒的训练数据集和完整（276 粒）、受损（223 粒）和化石的三个验证数据集花粉（97 粒）。我们表明，我们的系统成功识别了完整的、损坏的和化石花粉粒，错误分类率分别为 0%、2.8% 和 3.7%。在训练期间在堆叠光学图像上使用增强可提高分类精度。遵循孢粉学家的方法，我们的系统允许将没有明显特征的谷物归类为高分类级别或无法确定的花粉。这是第一个能够处理具有广泛埋藏阶段的谷物的软件，