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Proceedings Paper

Winter wheat GPC estimation with fluorescence-based sensor measurements of canopy
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Paper Abstract

This study focused on the wheat grain protein content (GPC) estimation based on wheat canopy chlorophyll parameters which acquired by hand-held instrument, Multiplex 3. Nine fluorescence spectral indices from Multiplex sensor were used in this study. The wheat GPC estimation experiment was conducted in 2012 at the National Experiment Station for Precision Agriculture in Changping district, Beijing. A square with area of 1.1 ha was selected and divided to 110 small plots by 10×10m in this study. In each plot, four 1-m2 area distributed in the square were selected for canopy fluorescence spectral measurements, physiological and biochemical analyses. Measurements were performed five times at wheat raising, jointing, heading stage, milking and ripening stage, respectively. The wheat plant samples for each plot were then collected after the measurement and sent to Lab for leaf N concentration (LNC) and canopy nitrogen density (CND) analyzed. GPC sampling for each plot was collected manually during the harvested season. Then, statistical analysis were performed to detect the correlation between fluorescence spectral indices and wheat CND for each growth stage, as well as GPC. The results indicate that two Nitrogen Balance Indices, NBI_G and NBI_R were more sensitive to wheat GPC than other fluorescence spectral indices at milking stage and ripening stage. Five linear regression models with GPC and fluorescence indices at different winter wheat growth stages were then established. The R2 of GPC estimated model increased form 0.312 at raising stage to 0.686 at ripening stage. The study reveals that canopy-level fluorescence spectral parameters were better indicators for the wheat group activity and could be demonstrated to be good indicators for winter wheat GPC estimation.

Paper Details

Date Published: 14 October 2015
PDF: 8 pages
Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 96371L (14 October 2015); doi: 10.1117/12.2195289
Show Author Affiliations
Xiaoyu Song, Beijing Research Ctr. for Information Technology in Agriculture (China)
Beijing Academy of Agriculture and Forestry Science (China)
Jihua Wang, Beijing Academy of Agriculture and Forestry Science (China)
Beijing Research Ctr. for Agri-food Testing and Farmland Monitoring (China)
Xiaohe Gu, Beijing Research Ctr. for Information Technology in Agriculture (China)
Beijing Academy of Agriculture and Forestry Science (China)
Xingang Xu, Beijing Research Ctr. for Information Technology in Agriculture (China)
Beijing Academy of Agriculture and Forestry Science (China)


Published in SPIE Proceedings Vol. 9637:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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