Valicharla Sruthi Keerthi, Li Xin, Greenleaf Jennifer, Turcotte Richard, Hayes Christopher, Park Yong-Lak
Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
Division of Plant and Soil Sciences, West Virginia University, Morgantown, WV 26506, USA.
Plants (Basel). 2023 Feb 10;12(4):798. doi: 10.3390/plants12040798.
Emerald ash borer () is an invasive pest that has killed millions of ash trees ( spp.) in the USA since its first detection in 2002. Although the current methods for trapping emerald ash borers (e.g., sticky traps and trap trees) and visual ground and aerial surveys are generally effective, they are inefficient for precisely locating and assessing the declining and dead ash trees in large or hard-to-access areas. This study was conducted to develop and evaluate a new tool for safe, efficient, and precise detection and assessment of ash decline and death caused by emerald ash borer by using aerial surveys with unmanned aerial systems (a.k.a., drones) and a deep learning model. Aerial surveys with drones were conducted to obtain 6174 aerial images including ash decline in the deciduous forests in West Virginia and Pennsylvania, USA. The ash trees in each image were manually annotated for training and validating deep learning models. The models were evaluated using the object recognition metrics: mean average precisions () and two average precisions ( and ). Our comprehensive analyses with instance segmentation models showed that Mask2former was the most effective model for detecting declining and dead ash trees with 0.789, 0.617, and 0.542 for and , respectively, on the validation dataset. A follow-up in-situ field study conducted in nine locations with various levels of ash decline and death demonstrated that deep learning along with aerial survey using drones could be an innovative tool for rapid, safe, and efficient detection and assessment of ash decline and death in large or hard-to-access areas.
翡翠灰螟（ ）是一种入侵性害虫，自2002年首次被发现以来，已在美国杀死了数百万棵灰树（ 属）。尽管目前诱捕翡翠灰螟的方法（如粘性诱捕器和诱捕树）以及地面和空中目视调查总体上是有效的，但在大型或难以进入的区域中，它们在精确定位和评估衰退及死亡的灰树方面效率低下。本研究旨在开发和评估一种新工具，通过使用无人机进行空中调查和深度学习模型，安全、高效且精确地检测和评估由翡翠灰螟导致的灰树衰退和死亡情况。利用无人机进行空中调查，获取了6174张航拍图像，包括美国西弗吉尼亚州和宾夕法尼亚州落叶林中的灰树衰退情况。对每张图像中的灰树进行人工标注，用于训练和验证深度学习模型。使用目标识别指标：平均精度均值（ ）和两个平均精度（ 和 ）对模型进行评估。我们对实例分割模型的综合分析表明，Mask2former是检测衰退和死亡灰树最有效的模型，在验证数据集上，其 、 和 的值分别为0.789、0.617和0.542。在九个具有不同灰树衰退和死亡程度的地点进行的后续实地研究表明，深度学习结合无人机空中调查可以成为一种创新工具，用于在大型或难以进入的区域快速、安全且高效地检测和评估灰树的衰退和死亡情况。
