Counting of Pennisetum alopecuroides at heading stage in a grazed pasture using images from an unmanned aerial vehicle-Reference-Cited by-同舟云学术

Counting of Pennisetum alopecuroides at heading stage in a grazed pasture using images from an unmanned aerial vehicle

Published:2020-03-18 Issue:4 Volume:66 Page:285-292
ISSN:1744-6961
Container-title:Grassland Science
language:en
Short-container-title:Grassl Sci

Author:

Yuba Norio¹²,Kawamura Kensuke³^ORCID,Yasuda Taisuke⁴,Lim Jihyun⁵⁶^ORCID,Yoshitoshi Rena⁵,Kurokawa Yuzo⁷,Maeda Teruo⁷

Affiliation:

1. Graduate School for International Development and Cooperation (IDEC) Hiroshima University Higashi‐Hiroshima Hiroshima Japan

2. Forestry Research Center Hiroshima Prefectural Technology Research Institute Hiroshima Japan

3. Japan International Research Center for Agricultural Sciences (JIRCAS) Tsukuba Ibaraki Japan

4. Mount Fuji Research Institute Yamanashi Prefectural Government Fujiyoshida Yamanashi Japan

5. Western Region Agricultural Research Center National Agriculture and Food Research Organization (NARO) Oda Shimane Japan

6. Research Center for Agricultural Information Technology National Agriculture and Food Research Organization (NARO) Tsukuba Ibaraki Japan

7. Graduate School of Integrated Sciences for Life Hiroshima University Higashi‐Hiroshima Hiroshima Japan

Publisher

Wiley

Subject

Plant Science,Agronomy and Crop Science,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/grs.12277

Reference39 articles.

1. Characteristics of pasturages and crops in spectroscopic reflectance. 5 Survey on distribution of Pennisetum alopecuroides by Landsat TM data at different times;Akiyama T.;Journal of Japanese Society of Grassland Science,1989

2. Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

3. Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops

4. Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control

5. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

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