|Table of Contents|

Using Canopy Hyperspectral Reflectance to Predict Growth Traits and Seed Yield of Soybeans from Middle and Lower Yangtze Valleys through Partial Least Squares Regression(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2015年03期
Page:
414-419,426
Research Field:
Publishing date:

Info

Title:
Using Canopy Hyperspectral Reflectance to Predict Growth Traits and Seed Yield of Soybeans from Middle and Lower Yangtze Valleys through Partial Least Squares Regression
Author(s):
QI Bo;  ZHANG Ning;  ZHAO Tuan-jie;  XING Guang-nan;  ZHAO Jin-ming;  and GAI Jun-yi
Soybean Research Institute of Nanjing Agricultural University/National Center for Soybean Improvement /Key Laboratory for Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture/ National Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing 210095, China
Keywords:
Soybean;  Hyperspectral reflectance;  Leaf area index (LAI);  Aboveground biomass(ABM);  Yield;  Partial least squares regression (PLSR)
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2015.03.0414
Abstract:
Hyperspectral remote sensing technique as a fast and non-destructive method can estimate growth traits and yield in crop, which provides an effective tool for field evaluation and selection in large-scale breeding programs. In the present study, a field experiment comparing 52 soybean varieties with similar flowering and maturity dates were tested a randomized blocks design with three replications in two years. The measurement of leaf area index (LAI) and aboveground biomass (ABM) was synchronized with the information collection of the canopy hyperspectral reflectance at R2, R4, and R5 growth stages.The seed yield was acquired after harvest.The partial least squares regression (PLSR) between canopy spectral reflectance at different growth stages and growth traits and seed yield showed that the PLSR models of ABM and LAI at different growth stages could explain 65.5%~67.0% and 54.4%~61.0% of the total variance of ABM and LAI, respectively, and R5 stage performed as the best of the three growth stages for predicting yield using canopy spectral reflectance with an explanation up to 66.1% of the total seed yield variance. The results can serve a quick and non-destructive technique for monitoring field growing status and predicting yield in large-scale soybean breeding programs.

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Last Update: 2015-07-15