Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton

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ISSN Print: 2158-2742
ISSN Online: 2158-2750
www.scirp.org/journal/ajps
E-mail: ajps@scirp.org

"Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton"
written by Reginald S. Fletcher, Rickie B. Turley,
published by American Journal of Plant Sciences, Vol.8 No.12, 2017

has been cited by the following article(s):

[1]	Advanced Machine Learning in Point Spectroscopy, RGB-and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review
	2020

[2]	Maximum discrimination index: a tool for land cover identification
	2019

[3]	Canopy Hyperspectral Reflectance of Redroot Pigweed versus Okra and Super Okra Leaf Cotton
	2019

[4]	Machine learning classification methods in hyperspectral data processing for agricultural applications
	ICGDA 2018 Proceedings of the International Conference on Geoinformatics and Data Analysis, 2018

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[2]	Fine hyperspectral classification of rice varieties based on self-attention mechanism Ecological Informatics, 2023 DOI:10.1016/j.ecoinf.2023.102035

[3]	Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review Smart Cities, 2020 DOI:10.3390/smartcities3030039

[4]	Maximum discrimination index: a tool for land cover identification International Journal of Environmental Science and Technology, 2019 DOI:10.1007/s13762-019-02547-5

[5]	Machine learning classification methods in hyperspectral data processing for agricultural applications Proceedings of the International Conference on Geoinformatics and Data Analysis, 2018 DOI:10.1145/3220228.3220242

[6]	Machine learning classification methods in hyperspectral data processing for agricultural applications Proceedings of the International Conference on Geoinformatics and Data Analysis, 2018 DOI:10.1145/3220228.3220242

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