Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy

By Yongni Shao, Chunjiang Zhao, Yidan Bao, Yong He. Food and Bioprocess Technology, 2009

64 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/087c203c3052a02b2b9321bbc2f9204577c8e00f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Hongbin Pu
2. Da‐Wen Sun
3. Lu Wang
4. Dan Liu
5. Z. Xiong
6. Jun‐Hu Cheng
7. Zhiwei Zhu
8. Anne Humeau-Heurtier

In Ecology

1. Da‐Wen Sun
2. Dan Liu

In Industrial and Manufacturing Engineering

1. Da‐Wen Sun
2. Dan Liu
3. Jun‐Hu Cheng

Citations in the corpus

• Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice (2015, background): “2009) have reported the feasibility of visible and near-infrared (Vis/NIR) and/or NIR spectroscopy for discriminating the variety and quality of cereals with varying degrees of success, such as quantification of nitrogen status in rice (Shao et al. 2012), prediction of maize seed composition (Baye e”