Skip to Main content Skip to Navigation
Conference papers

Wheat Grain Protein Content Estimation Based on Multi-temporal Remote Sensing Data and Generalized Regression Neural Network

Abstract : Monitoring grain protein content in large areas by remote sensing is very important for guiding graded harvest, and facilitates grain purchasing for processing enterprises. Wheat grain protein content (GPC) at maturity was measured and multi- temporal Landsat TM and Landsat ETM + images at key stages in 2003, 2004 growth stages were acquired in this study. GPC was estimated with multi-temporal remote sensing data and generalized regression neural network (GRNN) method. Results show that the GPC prediction accuracy of the GRNN model is higher, with the average relative deviation of self-modeling, average relative deviation of cross-validation as 0.003%, 0.321%; 4.300%, 7.349% for 2003 and 2004 respectively. GRNN method proves to be reliable and robust to monitoring GPC in large areas by multi-temporal and multi-spectral remote sensing data.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-01361006
Contributor : Hal Ifip <>
Submitted on : Tuesday, September 6, 2016 - 3:15:20 PM
Last modification on : Tuesday, September 6, 2016 - 4:06:06 PM
Long-term archiving on: : Wednesday, December 7, 2016 - 1:46:14 PM

File

978-3-642-27278-3_41_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Cunjun Li, Qian Wang, Jihua Wang, Yan Wang, Xiaodong Yang, et al.. Wheat Grain Protein Content Estimation Based on Multi-temporal Remote Sensing Data and Generalized Regression Neural Network. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. pp.381-389, ⟨10.1007/978-3-642-27278-3_41⟩. ⟨hal-01361006⟩

Share

Metrics

Record views

244

Files downloads

205