Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Binxiang Qian"'
Autor:
Anting Guo, Wenjiang Huang, Binxiang Qian, Huichun Ye, Quanjun Jiao, Xiangzhe Cheng, Chao Ruan
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 132, Iss , Pp 104076- (2024)
Chlorophyll is both a cornerstone of plant photosynthesis and an important indicator for assessing crop growth and health. Although many previous studies have explored the use of remote sensing to retrieve chlorophyll content, there is room for impro
Externí odkaz:
https://doaj.org/article/1abf620d1559469aa0d29125b61ff0ed
Publikováno v:
Agriculture, Vol 14, Iss 8, p 1311 (2024)
Maize northern leaf blight (MNLB), characterized by a bottom-up progression, is a prevalent and damaging disease affecting maize growth. Early monitoring is crucial for timely interventions, thus mitigating yield losses. Hyperspectral remote sensing
Externí odkaz:
https://doaj.org/article/a3713a92d238412f91a50ee714e3b694
Autor:
Xiangzhe Cheng, Yuyun Feng, Anting Guo, Wenjiang Huang, Zhiying Cai, Yingying Dong, Jing Guo, Binxiang Qian, Zhuoqing Hao, Guiliang Chen, Yixian Liu
Publikováno v:
Remote Sensing, Vol 16, Iss 1, p 105 (2023)
Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for t
Externí odkaz:
https://doaj.org/article/a84dc9afefab45678f716f73c5b034b9
Autor:
Yu Ren, Huichun Ye, Wenjiang Huang, Huiqin Ma, Anting Guo, Chao Ruan, Linyi Liu, Binxiang Qian
Publikováno v:
Big Earth Data, Vol 5, Iss 2, Pp 201-216 (2021)
Yellow rust (Puccinia striiformis f. sp. Tritici) is a frequently occurring fungal disease of winter wheat (Triticum aestivum L.). During yellow rust infestation, fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel
Externí odkaz:
https://doaj.org/article/f5f6fd06db024614876ce505bfdd6b4b
Autor:
Anting Guo, Huichun Ye, Guoqing Li, Bing Zhang, Wenjiang Huang, Quanjun Jiao, Binxiang Qian, Peilei Luo
Publikováno v:
Remote Sensing, Vol 15, Iss 7, p 1784 (2023)
Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval mode
Externí odkaz:
https://doaj.org/article/edc7b905367d4c9193ca411c7b534fa5
Autor:
Zhongxiang Sun, Huichun Ye, Wenjiang Huang, Erden Qimuge, Huiqing Bai, Chaojia Nie, Longhui Lu, Binxiang Qian, Bo Wu
Publikováno v:
Insects, Vol 14, Iss 2, p 138 (2023)
Grasshopper populations can quickly grow to catastrophic levels, causing a huge amount of damage in a short time. Oedaleus decorus asiaticus (Bey-Bienko) (O. d. asiaticus) is the most serious species in Xilingol League of the Inner Mongolia Autonomou
Externí odkaz:
https://doaj.org/article/108f503f4d3846aeb662364758264652
Autor:
Peilei Luo, Huichun Ye, Wenjiang Huang, Jingjuan Liao, Quanjun Jiao, Anting Guo, Binxiang Qian
Publikováno v:
Remote Sensing, Vol 14, Iss 21, p 5624 (2022)
Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimatio
Externí odkaz:
https://doaj.org/article/f62d8ad38cc848e6997dd8df475bad0c
Autor:
Quanjun Jiao, Qi Sun, Bing Zhang, Wenjiang Huang, Huichun Ye, Zhaoming Zhang, Xiao Zhang, Binxiang Qian
Publikováno v:
Remote Sensing, Vol 14, Iss 1, p 98 (2021)
Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for cro
Externí odkaz:
https://doaj.org/article/690c3634a82f40aca0dddcfb145f1b9b
Autor:
Binxiang Qian, Huichun Ye, Wenjiang Huang, Qiaoyun Xie, Yuhao Pan, Naichen Xing, Yu Ren, Anting Guo, Quanjun Jiao, Yubin Lan
Accurate estimation of chlorophyll content is important for diagnosing the physiological and phenological status of vegetation. Establishing the relationship between vegetation indices (VIs) and leaf chlorophyll content using remote sensing is crucia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67b6ad5db71bb42093a82574f5fd926c
https://hdl.handle.net/10453/166095
https://hdl.handle.net/10453/166095