scholarly journals Exploring hyperspectral reflectance indices for the estimation of water and nitrogen status of spinach

2022 ◽  
Vol 214 ◽  
pp. 58-71
Author(s):  
Samantha Rubo ◽  
Jana Zinkernagel
Keyword(s):  
Hyperspectral Reflectance ◽  
Nitrogen Status ◽  
Reflectance Indices

Estimating nitrogen status of rice canopy using hyperspectral reflectance combined with BPSO-SVR in cold region

2018 ◽  
Vol 172 ◽  
pp. 68-79 ◽  
Author(s):  
Kezhu Tan ◽  
Shuwen Wang ◽  
Yuzhu Song ◽  
Yao Liu ◽  
Zhenping Gong
Keyword(s):  
Hyperspectral Reflectance ◽  
Cold Region ◽  
Nitrogen Status

scholarly journals Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping

2021 ◽  
Author(s):  
Alexei Solovchenko ◽  
Boris Shurygin ◽  
Andrey Kuzin ◽  
Vitaly Velichko ◽  
Olga Solovchenko ◽  
...  
Keyword(s):  
Plant Development ◽  
Precision Agriculture ◽  
Plant Traits ◽  
Apple Fruit ◽  
Hyperspectral Reflectance ◽  
Spatially Resolved ◽  
Stress Acclimation ◽  
Secondary Carotenoids ◽  
As Stress ◽  
Reflectance Indices

Hyperspectral reflectance imaging is an emerging method for rapid non-invasive quantitative screening of plant traits. This method is essential for high-throughput phenotyping and hence for accelerated breeding of crop plants as well as for precision agriculture practices. However, extraction of sensible information from reflectance images is hindered by the complexity of plant optical properties, especially when they are measured in the field. We propose using reflectance indices (Plant Senescence Reflectance Index, PSRI; Anthocyanin Reflectance Index, ARI; and spectral deconvolution) previously developed for remote sensing of vegetation and point-based reflectometers to infer the spatially resolved information on plant development and biochemical composition using ripening apple fruit as the model. Specifically, the proposed approach enables capturing data on distribution of chlorophylls and primary carotenoids as well as secondary carotenoids (both linked with fruit ripening and leaf senescence during plant development) as well as the information on spatial distribution of anthocyanins (known as stress pigments) over the plant surface. We argue that the proposed approach would enrich the phenotype assessments made on the base of reflectance image analysis with valuable information on plant physiological condition, stress acclimation state, and the progression of the plant development.

Monitoring leaf nitrogen status with hyperspectral reflectance in wheat

2008 ◽  
Vol 28 (3) ◽  
pp. 394-404 ◽  
Author(s):  
W. Feng ◽  
X. Yao ◽  
Y. Zhu ◽  
Y.C. Tian ◽  
W.X. Cao
Keyword(s):  
Leaf Nitrogen ◽  
Hyperspectral Reflectance ◽  
Nitrogen Status

Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis

Crop Science ◽  
1995 ◽  
Vol 35 (5) ◽  
pp. 1400-1405 ◽  
Author(s):  
I. Filella ◽  
L. Serrano ◽  
J. Serra ◽  
J. Peñuelas
Keyword(s):  
Discriminant Analysis ◽  
Canopy Reflectance ◽  
Nitrogen Status ◽  
Reflectance Indices

Monitoring Rice Nitrogen Status Using Hyperspectral Reflectance and Artificial Neural Network

2007 ◽  
Vol 41 (19) ◽  
pp. 6770-6775 ◽  
Author(s):  
Qiu-Xiang Yi ◽  
Jing-Feng Huang ◽  
Fu-Min Wang ◽  
Xiu-Zhen Wang ◽  
Zhan-Yu Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hyperspectral Reflectance ◽  
Nitrogen Status ◽  
Artificial Neural
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