Generating images of hydrated pollen grains using deep learning
Generating images of hydrated pollen grains using deep learning
复制标题
使用深度学习生成水合花粉粒图像
DOI：
10.1088/2633-1357/ac6780
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
B. Mills
中科院分区：
文献类型：
--
作者：
J. Grant;M. Praeger;R. Eason;B. Mills
花粉粒在发育过程中和脱离寄主柱头后脱水。由于花粉粒的大小和形状可能取决于环境条件，因此能够从脱水状态预测水合花粉粒的这两个因素在气候科学、农业和孢粉学领域可能是有益的。在这里，我们使用深度学习将脱水的猫爪草花粉粒图像转换为水合猫爪草花粉粒的图像。然后，我们还使用深度学习神经网络对不同属的花粉粒的实验图像进行训练，从生成的转换图像中识别出水合花粉粒，以测试图像生成神经网络的准确性。这项试点工作展示了创建基于深度学习的一般复水模型所需的第一步，该模型可能有助于理解和预测花粉形态。
Pollen grains dehydrate during their development and following their departure from the host stigma. Since the size and shape of a pollen grain can be dependent on environmental conditions, being able to predict both of these factors for hydrated pollen grains from their dehydrated state could be beneficial in the fields of climate science, agriculture, and palynology. Here, we use deep learning to transform images of dehydrated Ranunculus pollen grains into images of hydrated Ranunculus pollen grains. We also then use a deep learning neural network that was trained on experimental images of different genera of pollen grains to identify the hydrated pollen grains from the generated transformed images, to test the accuracy of the image generation neural network. This pilot work demonstrates the first steps needed towards creating a general deep learning-based rehydration model that could be useful in understanding and predicting pollen morphology.