文献类型: 外文期刊
作者: Chen, Pengfei 1 ; Ma, Xiao 1 ; Yang, Guijun 4 ;
作者机构: 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
关键词: Failure detection; Unmanned aerial vehicles; Wheat; Multispectral
期刊名称:EUROPEAN JOURNAL OF AGRONOMY ( 影响因子:5.722; 五年影响因子:6.384 )
ISSN: 1161-0301
年卷期: 2022 年 141 卷
页码:
收录情况: SCI
摘要: Crop failure detection using UAV images is helpful for precision agriculture, enabling the precision management of failure areas to reduce crop loss. For wheat failure area detection at the seedling stage using UAV images, the commonly used methods are not sufficiently accurate. Thus, herein, a new tool for precision wheat management at the seedling stage is designed. For this purpose, field experiments with two wheat cultivars and four nitrogen (N) treatments were conducted to create different scenarios for the failure area, and multispectral UAV images were acquired at the seedling growth stage. Based on the above data, a new failure detection method was designed by assimilating prior knowledge and a filter analysis strategy and compared with classical filter-based methods and Hough transform-based methods for wheat failure area detection. The results showed that the newly proposed assimilation method had a detection accuracy between 83.86% and 97.67% for different N levels and cultivars. In contrast, the filter-based methods and Hough transform-based methods had detection accuracies between 53.73% and 83.95% and between 20.71% and 75.79%, respectively. Thus, the assimilation method demonstrated the best failure detection performance.
- 相关文献
作者其他论文 更多>>
-
Recognition of wheat rusts in a field environment based on improved DenseNet
作者:Chang, Shenglong;Cheng, Jinpeng;Fan, Zehua;Ma, Xinming;Li, Yong;Zhao, Chunjiang;Chang, Shenglong;Yang, Guijun;Cheng, Jinpeng;Fan, Zehua;Yang, Xiaodong;Zhao, Chunjiang
关键词:Plant disease; Wheat rust; Image processing; Deep learning; Computer vision (CV); DenseNet
-
Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images
作者:Wang, Gengze;Chen, Riqiang;Yang, Guijun;Feng, Haikuan;Wang, Gengze;Chen, Riqiang;Yang, Guijun;Feng, Haikuan;Meng, Di;Jin, Hailiang;Ge, Xiaosan;Wang, Laigang;Feng, Haikuan
关键词:early-season rice mapping; spectral index (SI); synthetic aperture radar (SAR); Simple Non-Iterative Clustering (SNIC); time series filtering; K-Means; Jeffries-Matusita (JM) distance
-
A Two-Stage Leaf-Stem Separation Model for Maize With High Planting Density With Terrestrial, Backpack, and UAV-Based Laser Scanning
作者:Lei, Lei;Lei, Lei;Li, Zhenhong;Li, Zhenhong;Yang, Hao;Xu, Bo;Yang, Guijun;Hoey, Trevor B.;Wu, Jintao;Yang, Xiaodong;Feng, Haikuan;Yang, Guijun;Yang, Guijun
关键词:Vegetation mapping; Laser radar; Point cloud compression; Feature extraction; Agriculture; Data models; Data mining; Different cultivars; different growth stages; different planting densities; different platforms; light detection and ranging (LiDAR) data; simulated datasets; two-stage leaf-stem separation model
-
Remote sensing of quality traits in cereal and arable production systems: A review
作者:Li, Zhenhai;Fan, Chengzhi;Li, Zhenhai;Zhao, Yu;Song, Xiaoyu;Yang, Guijun;Jin, Xiuliang;Casa, Raffaele;Huang, Wenjiang;Blasch, Gerald;Taylor, James;Li, Zhenhong
关键词:Remote sensing; Quality traits; Grain protein; Cereal
-
A method to rapidly construct 3D canopy scenes for maize and their spectral response evaluation
作者:Zhao, Dan;Xu, Tongyu;Yang, Hao;Zhang, Chengjian;Cheng, Jinpeng;Yang, Guijun;Henke, Michael
关键词:3D maize canopy scene; Functional-structural model; Canopy structure; 3D radiative transfer; Spectral response
-
Analyzing winter-wheat biochemical traits using hyperspectral remote sensing and deep learning
作者:Yue, Jibo;Wang, Jian;Guo, Wei;Ma, Xinming;Qiao, Hongbo;Yang, Guijun;Liu, Yang;Feng, Haikuan;Yue, Jibo;Yang, Guijun;Li, Changchun;Niu, Qinglin;Feng, Haikuan
关键词:Unmanned aerial vehicle; Transfer learning; Deep learning; Hyperspectral
-
Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China
作者:Liu, Miao;Yang, Guijun;Li, Zhenhong;Gao, Meiling;Yang, Yun;Liu, Miao;Yang, Guijun;Long, Huiling;Meng, Yang;Hu, Haitang;Li, Heli;Yuan, Wenping;Li, Changchun;Yuan, Zhanliang;Meng, Yang
关键词:Vapor pressure deficit (VPD); Aridity index (AI); EVI; NIRv; Vegetation; Sensitivity



