Cited by A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space

Timing of red-edge and shortwave infrared reflectance critical for early stress detection induced by bark beetle (Ips typographus, L.) attack DOI

Haidi Abdullah, Andrew K. Skidmore, Roshanak Darvishzadeh

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2019, Volume and Issue: 82, P. 101900 - 101900

Published: July 11, 2019

Language: Английский

Citations

Imaging Spectroscopy of Forest Ecosystems: Perspectives for the Use of Space-borne Hyperspectral Earth Observation Systems DOI

Joachim Hill, Henning Buddenbaum, Philip A. Townsend

et al.

Surveys in Geophysics, Journal Year: 2019, Volume and Issue: 40(3), P. 553 - 588

Published: Feb. 10, 2019

Language: Английский

Citations

Early Monitoring of Forest Wood-Boring Pests with Remote Sensing DOI

Youqing Luo,

Huaguo Huang, Alain Roques

et al.

Annual Review of Entomology, Journal Year: 2022, Volume and Issue: 68(1), P. 277 - 298

Published: Oct. 6, 2022

Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid widespread at later stages, leading large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early of WBPs requires advanced effective methods like remote sensing. This review summarizes the applications various sensing sensors, platforms, monitoring infestations. The current capabilities, gaps future potential accurate are highlighted.

Language: Английский

Citations

Detection of Norway Spruce Trees (Picea Abies) Infested by Bark Beetle in UAV Images Using YOLOs Architectures DOI

Anastasiia Safonova, Yousif A. Hamad,

Anna Alekhina

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 10384 - 10392

Published: Jan. 1, 2022

In recent years, massive outbreaks of the European spruce bark beetle ( Ips typographus , (L.)) have caused colossal harm to coniferous forests. The main solution for this problem is timely prevention spread, which it necessary identify damaged trees in their early stages infestation. Fortunately, high-resolution unmanned aerial vehicle (UAV) imagery together with modern detection models provide a high potential addressing such issues. work, we evaluate and compare three You Only Look Once (YOLO) deep neural network architectures, namely YOLOv2, YOLOv3, YOLOv4, task detecting infested UAV images. We built new dataset training testing these used pre-processing balance contrast enhancement technique (BCET) that improves generalization capacity models. Our experiments show YOLOv4 achieves particularly good results when applying BCET pre-processing. best test result comparing YOLO was obtained mean average precision up 95%. As artificial data augmentation, improvement 65.0%, 7.22%, 3.19%, respectively.

Language: Английский

Citations

A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space DOI

Long Tian, Ziyi Wang,

Bowen Xue

et al.

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 285, P. 113384 - 113384

Published: Nov. 28, 2022

Language: Английский

Citations