Tsiknakis Nikos, Savvidaki Elisavet, Manikis Georgios C, Gotsiou Panagiota, Remoundou Ilektra, Marias Kostas, Alissandrakis Eleftherios, Vidakis Nikolas
Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas-FORTH, 70013 Heraklion, Greece.
Department of Agriculture, Hellenic Mediterranean University, 71004 Heraklion, Greece.
Plants (Basel). 2022 Mar 29;11(7):919. doi: 10.3390/plants11070919.
Pollen identification is an important task for the botanical certification of honey. It is performed via thorough microscopic examination of the pollen present in honey; a process called melissopalynology. However, manual examination of the images is hard, time-consuming and subject to inter- and intra-observer variability. In this study, we investigated the applicability of deep learning models for the classification of pollen-grain images into 20 pollen types, based on the Cretan Pollen Dataset. In particular, we applied transfer and ensemble learning methods to achieve an accuracy of 97.5%, a sensitivity of 96.9%, a precision of 97%, an F1 score of 96.89% and an AUC of 0.9995. However, in a preliminary case study, when we applied the best-performing model on honey-based pollen-grain images, we found that it performed poorly; only 0.02 better than random guessing (i.e., an AUC of 0.52). This indicates that the model should be further fine-tuned on honey-based pollen-grain images to increase its effectiveness on such data.
花粉鉴定是蜂蜜植物学认证的一项重要任务。它通过对蜂蜜中存在的花粉进行全面的显微镜检查来完成；这个过程称为蜂蜜花粉学。然而，人工检查图像既困难又耗时，并且存在观察者间和观察者内的变异性。在本研究中，我们基于克里特岛花粉数据集，研究了深度学习模型对花粉粒图像进行20种花粉类型分类的适用性。特别是，我们应用迁移学习和集成学习方法，实现了97.5%的准确率、96.9%的灵敏度、97%的精确率、96.89%的F1分数和0.9995的AUC。然而，在一个初步的案例研究中，当我们将性能最佳的模型应用于基于蜂蜜的花粉粒图像时，我们发现它的表现很差；仅比随机猜测好0.02（即AUC为0.52）。这表明该模型应在基于蜂蜜的花粉粒图像上进一步微调，以提高其在此类数据上的有效性。
