Mapping and Quantifying White Mold in Soybean across South Dakota Using Landsat Images

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ISSN Print: 2151-1950
ISSN Online: 2151-1969
www.scirp.org/journal/jgis
E-mail: jgis@scirp.org

"Mapping and Quantifying White Mold in Soybean across South Dakota Using Landsat Images"
written by Confiance Mfuka, Xiaoyang Zhang, Emmanuel Byamukama,
published by Journal of Geographic Information System, Vol.11 No.3, 2019

has been cited by the following article(s):

[1]	High-Throughput Phenotyping in Soybean
	2021

[2]	Spatiotemporal characteristics of white mold and impacts on yield in soybean fields in South Dakota
	2020

[3]	The Utility of the Upcoming HyspIRI's Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI …
	2019

[1]	Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica Revista de Teledetección, 2023 DOI:10.4995/raet.2023.19014

[2]	Spatiotemporal characteristics of white mold and impacts on yield in soybean fields in South Dakota Geo-spatial Information Science, 2020 DOI:10.1080/10095020.2020.1712265

[3]	The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors Agronomy, 2019 DOI:10.3390/agronomy9120846

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