Computer vision and machine learning for robust phenotyping in genome-wide studies-Reference-Cited by-同舟云学术

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Computer vision and machine learning for robust phenotyping in genome-wide studies

Published:2017-03-08 Issue:1 Volume:7 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Zhang Jiaoping,Naik Hsiang Sing,Assefa Teshale,Sarkar Soumik,Reddy R. V. Chowda,Singh Arti,Ganapathysubramanian Baskar,Singh Asheesh K.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/srep44048.pdf

Reference41 articles.

1. Franzen, D. & Richardson, J. Soil factors affecting iron chlorosis of soybean in the Red River Valley of North Dakota and Minnesota. Journal of Plant Nutrition 23, 67–78 (2000).

2. Schulte, E. Soil and applied iron. (University of Wisconsin–Extension, 1992).

3. Morrissey, J. & Guerinot, M. L. Iron uptake and transport in plants: the good, the bad, and the ionome. Chem Rev 109, 4553–4567 (2009).

4. Hansen, N. C., Schmitt, M. A., Anderson, J. E. & Strock, J. S. Iron deficiency of soybean in the upper Midwest and associated soil properties. Agron J 95, 1595–1601 (2003).

5. Terry, N. & Low, G. Leaf Chlorophyll Content and Its Relation to the Intracellular-Localization of Iron. Journal of Plant Nutrition 5, 301–310 (1982).

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