Gallmann Johannes, Schüpbach Beatrice, Jacot Katja, Albrecht Matthias, Winizki Jonas, Kirchgessner Norbert, Aasen Helge
Department of Computer Science, ETH Zürich, Zurich, Switzerland.
Agricultural Landscape and Biodiversity Group, Agroscope, Zurich, Switzerland.
Front Plant Sci. 2022 Feb 9;12:774965. doi: 10.3389/fpls.2021.774965. eCollection 2021.
Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.
人工评估草原上不同开花植物物种的花朵丰度是一个耗时的过程。我们提出了一种自动化方法，通过使用深度学习（Faster R-CNN）目标检测方法，从无人机拍摄的航空图像中确定草原上的花朵丰度，该方法在两个地点的五次飞行数据上进行了训练和评估。我们的深度学习网络能够识别和分类单个花朵。这种新方法能够生成空间明确的花朵丰度图，其精度达到或超过了手动计数数据外推法，同时劳动强度更低。对于某些类型的花朵，结果非常好，精确率和召回率接近或高于90%。由于缺乏足够的训练数据、物候导致的外观变化或花朵太小以至于在航空图像上无法可靠区分等原因，其他花朵的检测效果较差。该方法能够对许多开花植物物种的丰度给出精确估计。未来，收集更多的训练数据将能够对目前预测效果不佳的花朵进行更好的预测。所开发的流程可应用于任何类型的航空目标检测问题。
