Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Wancheng Tao"'
Autor:
Yi Dong, Fu Xuan, Xianda Huang, Ziqian Li, Wei Su, Jianxi Huang, Xuecao Li, Wancheng Tao, Hui Liu, Jiezhi Chen
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-14 (2024)
Abstract Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some
Externí odkaz:
https://doaj.org/article/e9ad854fe62e4d9186bb94c6f3f69466
Autor:
Yi Dong, Wei Su, Fu Xuan, Jiayu Li, Feng Yin, Jianxi Huang, Yelu Zeng, Xuecao Li, Wancheng Tao
Publikováno v:
Egyptian Journal of Remote Sensing and Space Sciences, Vol 26, Iss 3, Pp 732-746 (2023)
Accurate land surface reflectance plays an important role in the accurate inversion of surface parameters, and atmospheric correction plays a decisive role in obtaining accurate reflectance. For GF-1 WFV and GF-6 WFV images, there are two major issue
Externí odkaz:
https://doaj.org/article/551fff966cb74e9c9a119145603b74c8
Autor:
Fu Xuan, Yi Dong, Jiayu Li, Xuecao Li, Wei Su, Xianda Huang, Jianxi Huang, Zixuan Xie, Ziqian Li, Hui Liu, Wancheng Tao, Yanan Wen, Ying Zhang
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 117, Iss , Pp 103178- (2023)
Northeast China is one of the most major grain banks in China and has an overwhelming influence on food security. To mitigate the challenges caused by increasing food demands and soil protection, crop rotation and fallowing policies have been introdu
Externí odkaz:
https://doaj.org/article/e92e6bf62d4947cebe53d06e2fbf4ab1
Publikováno v:
Remote Sensing, Vol 15, Iss 22, p 5289 (2023)
Estimating the complete 3D points of crop plants from incomplete points is vital for phenotyping and smart agriculture management. Compared with the completion of regular man-made objects such as airplanes, chairs, and desks, the completion of corn p
Externí odkaz:
https://doaj.org/article/2f99a4df876d44db9cb9d4f43f0d649d
Autor:
Wancheng Tao, Yi Dong, Wei Su, Jiayu Li, Fu Xuan, Jianxi Huang, Jianyu Yang, Xuecao Li, Yelu Zeng, Baoguo Li
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
The management of crop residue covering is a vital part of conservation tillage, which protects black soil by reducing soil erosion and increasing soil organic carbon. Accurate and rapid classification of corn residue-covered types is significant for
Externí odkaz:
https://doaj.org/article/209718ea3bf14d92863e1a05fe8c03b6
Autor:
Wancheng Tao, Zixuan Xie, Ying Zhang, Jiayu Li, Fu Xuan, Jianxi Huang, Xuecao Li, Wei Su, Dongqin Yin
Publikováno v:
Remote Sensing, Vol 13, Iss 15, p 2903 (2021)
Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurat
Externí odkaz:
https://doaj.org/article/baba69a37ba941ba95c733ff242da4ac
Autor:
Wancheng Tao, Xinsheng Wang, Jing‐Hao Xue, Wei Su, Mingzheng Zhang, Dongqin Yin, Dehai Zhu, Zixuan Xie, Ying Zhang
Publikováno v:
Pest Management Science. 78:2265-2276
The timely, rapid, and accurate near real-time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learning algorithms for iden
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::151b9877983da2c13e4890c1e506f1d5
https://doi.org/10.1002/ps.6852
https://doi.org/10.1002/ps.6852
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d4ca2ecaa5e44b778e04a8078368f33
https://doi.org/10.2139/ssrn.4314622
https://doi.org/10.2139/ssrn.4314622
Autor:
mingzheng zhang, Xinsheng Wang, Jinghao Xue, Wei Su, Dehai Zhu, Wancheng Tao, Chan Yao, Tian Jin, Zixuan Xie
Background: Monitoring armyworm (Mythimna separata Walker) damage in crops requires timely, rapid and accurate observations to avoid severe yield losses. Results: The Random Forest (RF) classifier was more effective at automatically and accurately mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ffe0a00c222e45876eabe9e39c08aa6
https://doi.org/10.21203/rs.3.rs-71451/v1
https://doi.org/10.21203/rs.3.rs-71451/v1
