Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing
Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing
复制标题
基于深度学习和无人机遥感的松材线虫病死树检测与定位
DOI：
10.3390/agriengineering2020019
复制
发表时间：
2020-06-01
期刊：
影响因子：
2.8
通讯作者：
Huang, Zixiao
中科院分区：
文献类型：
--
作者：
Deng, Xiaoling;Tong, Zejing;Huang, Zixiao
松材线虫病因其危害性大、传播速度快，给松林造成巨大的经济损失。提出了一种采用无人机遥感和人工智能技术对松材线虫病进行大规模检测和定位的方法。利用计算机视觉工具对无人机遥感图像进行增强处理。采用基于RPN（Region Proposal Network）网络和ResNet残差神经网络的Faster-RCNN（Faster Region Convolutional Neural Networks）深度学习框架训练松树枯萎病死树检测模型。对卷积神经网络的损失函数和RPN中的锚点进行了优化。最后对松材线虫病死树进行定位，生成地理信息对检测结果的影响。结果表明，ResNet 101的性能优于VGG 16（Visual Geometry Group 16）卷积神经网络。通过对网络进行一系列优化，检测准确率提高到90%左右，说明本文提出的优化方法对松材线虫病枯树检测是可行的。
Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection.