Rodent hole detection in a typical steppe ecosystem using UAS and deep learning

作者：Mingzhu Du, Dawei Wang, Shengping Liu, Chunyang Lv, Yeping Zhu · 发表于：Frontiers in Plant Science · 年份：2022 · DOI：10.3389/fpls.2022.992789 · 被引用次数：15 · 研究领域：Cell Image Analysis Techniques、Digital Imaging for Blood Diseases、Advanced Neural Network Applications

Introduction Rodent outbreak is the main biological disaster in grassland ecosystems. Traditional rodent damage monitoring approaches mainly depend on costly field surveys, e.g., rodent trapping or hole counting. Integrating an unmanned aircraft system (UAS) image acquisition platform and deep learning (DL) provides a great opportunity to realize efficient large-scale rodent damage monitoring and early-stage diagnosis. As the major rodent species in Inner Mongolia, Brandt’s voles (BV) ( Lasiopodomys brandtii ) have markedly small holes, which are difficult to identify regarding various seasonal noises in this typical steppe ecosystem. Methods In this study, we proposed a novel UAS-DL-based framework for BV hole detection in two representative seasons. We also established the first bi-seasonal UAS image datasets for rodent hole detection. Three two-stage (Faster R-CNN, R-FCN, and Cascade R-CNN) and three one-stage (SSD, RetinaNet, and YOLOv4) object detection DL models were investigated from three perspectives: accuracy, running speed, and generalizability. Results Experimental results revealed that: 1) Faster R-CNN and YOLOv4 are the most accurate models; 2) SSD and YOLOv4 are the fastest; 3) Faster R-CNN and YOLOv4 have the most consistent performance across two different seasons. Discussion The integration of UAS and DL techniques was demonstrated to utilize automatic, accurate, and efficient BV hole detection in a typical steppe ecosystem. The proposed method has a great p...