The plant community composition is an essential indicator of environmental changes and is, for this reason, usually analyzed in ecological field studies in terms of the so-called plant cover. The manual acquisition of this kind of data is time-consuming, laborious, and prone to human error. Automated camera systems can collect high-resolution images of the surveyed vegetation plots at a high frequency. In combination with subsequent algorithmic analysis, it is possible to objectively extract information on plant community composition quickly and with little human effort. An automated camera system can easily collect the large amounts of image data necessary to train a Deep Learning system for automatic analysis. However, due to the amount of work required to annotate vegetation images with plant cover data, only few labeled samples are available. As automated camera systems can collect many pictures without labels, we introduce an approach to interpolate the sparse labels in the collected vegetation plot time series down to the intermediate dense and unlabeled images to artificially increase our training dataset to seven times its original size. Moreover, we introduce a new method we call Monte-Carlo Cropping. This approach trains on a collection of cropped parts of the training images to deal with high-resolution images efficiently, implicitly augment the training images, and speed up training. We evaluate both approaches on a plant cover dataset containing images of herbaceous plant communities and find that our methods lead to improvements in the species, community, and segmentation metrics investigated.

翻译：植物群落组成是评估环境变化的重要指标，因此通常在生态实地研究中通过所谓的植物覆盖度进行分析。手动获取此类数据耗时费力且易受人为误差影响。自动化相机系统能够以高频率采集被调查植被样地的高分辨率图像。结合后续算法分析，可快速且几乎无需人工干预地客观提取植物群落组成信息。自动化相机系统可轻松收集训练深度学习系统进行自动分析所需的大量图像数据。然而，由于标注植被图像中植物覆盖数据所需的工作量巨大，仅有少量标注样本可用。鉴于自动化相机系统可收集大量无标签图像，我们提出一种方法，对采集的植被样地时间序列中的稀疏标签进行插值，将其扩展到中间密集但无标签的图像，从而将训练数据集人工扩充至原始规模的七倍。此外，我们引入一种称为蒙特卡洛裁剪的新方法。该方法通过训练一组从训练图像中裁剪的子图像块，高效处理高分辨率图像，隐式增强训练图像，并加速训练过程。我们在包含草本植物群落图像的植物覆盖数据集上评估了这两种方法，结果表明我们的方法改进了所研究的物种、群落和分割指标。