期刊
GESUNDE PFLANZEN
卷 75, 期 1, 页码 37-48出版社
SPRINGER
DOI: 10.1007/s10343-022-00794-0
关键词
Annotation; Cherry fruit fly; Deep learning; Insect detection; Sticky traps
类别
The aim of this study is to use state-of-the-art object detection algorithms from deep learning to automatically detect cherry fruit flies in images from yellow sticky traps. The results indicate that deep learning can act as an integral component of an automated system for high-throughput assessment of pest insects in orchards. This can reduce the time for repetitive and laborious trap assessment and increase the observed amount of sticky traps.
Insect populations appear with a high spatial, temporal and type-specific diversity in orchards. One of the many monitoring tools for pest management is the manual assessment of sticky traps. However, this type of assessment is laborious and time-consuming so that only a few locations can be controlled in an orchard. The aim of this study is to test state-of-the art object detection algorithms from deep learning to automatically detect cherry fruit flies (Rhagoletis cerasi), a common insect pest in cherry plantations, within images from yellow sticky traps. An image annotation database was built with images taken from yellow sticky traps with more than 1600 annotated cherry fruit flies. For better handling in the computational algorithms, the images were augmented to smaller ones by the known image preparation methods flipping and cropping before performing the deep learning. Five deep learning image recognition models were tested including Faster Region-based Convolutional Neural Network (R-CNN) with two different methods of pretraining, Single Shot Detector (SSD), RetinaNet, and You Only Look Once version 5 (YOLOv5). R-CNN and RetinaNet models outperformed other ones with a detection average precision of 0.9. The results indicate that deep learning can act as an integral component of an automated system for high-throughput assessment of pest insects in orchards. Therefore, this can reduce the time for repetitive and laborious trap assessment but also increase the observed amount of sticky traps
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据