Schnalke Marie, Funk Jonas, Wagner Andreas
Faculty of Management Science and Engineering, Karlsruhe University of Applied Sciences (HKA), Karlsruhe, Germany.
Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany.
Front Plant Sci. 2025 Mar 18;16:1498913. doi: 10.3389/fpls.2025.1498913. eCollection 2025.
The decline of insect biomass, including pollinators, represents a significant ecological challenge, impacting both biodiversity and ecosystems. Effective monitoring of pollinator habitats, especially floral resources, is essential for addressing this issue. This study connects drone and deep learning technologies to their practical application in ecological research. It focuses on simplifying the application of these technologies. Updating an object detection toolbox to TensorFlow (TF) 2 enhanced performance and ensured compatibility with newer software packages, facilitating access to multiple object recognition models - Faster Region-based Convolutional Neural Network (Faster R-CNN), Single-Shot-Detector (SSD), and EfficientDet. The three object detection models were tested on two datasets of UAV images of flower-rich grasslands, to evaluate their application potential in practice. A practical guide for biologists to apply flower recognition to Unmanned Aerial Vehicle (UAV) imagery is also provided. The results showed that Faster RCNN had the best overall performance with a precision of 89.9% and a recall of 89%, followed by EfficientDet, which excelled in recall but at a lower precision. Notably, EfficientDet demonstrated the lowest model complexity, making it a suitable choice for applications requiring a balance between efficiency and detection performance. Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability.
包括传粉者在内的昆虫生物量下降是一项重大的生态挑战，对生物多样性和生态系统均产生影响。有效监测传粉者栖息地，尤其是花卉资源，对于解决这一问题至关重要。本研究将无人机和深度学习技术与它们在生态研究中的实际应用联系起来。它侧重于简化这些技术的应用。将一个目标检测工具箱更新到TensorFlow (TF) 2提高了性能，并确保与更新的软件包兼容，便于访问多个目标识别模型——基于区域的快速卷积神经网络(Faster R-CNN)、单发检测器(SSD)和高效检测器(EfficientDet)。在两个富含花卉的草原无人机图像数据集上对这三种目标检测模型进行了测试，以评估它们在实际中的应用潜力。还为生物学家提供了一份将花卉识别应用于无人机(UAV)图像的实用指南。结果表明，Faster RCNN的整体性能最佳，精度为89.9%，召回率为89%，其次是EfficientDet，其召回率较高，但精度较低。值得注意的是，EfficientDet的模型复杂度最低，使其成为需要在效率和检测性能之间取得平衡的应用的合适选择。挑战依然存在，比如在茂密植被中检测花朵以及考虑环境变异性。
