Rice yield prediction with panicle spectral indices

doi:10.3969/j.issn.1004-1524.2018.02.02

›› 2018, Vol. 30 ›› Issue (2): 187-193.DOI: 10.3969/j.issn.1004-1524.2018.02.02

• Crop Science • Previous Articles Next Articles

Rice yield prediction with panicle spectral indices

JIANG Qingsu^{1, 2}, CHENG Qilu², XU Ligen^{1, *}, ZHOU Qifa²

1. College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China;
2. College of Life Sciences, Zhejiang University, Hangzhou 310058, China

Received:2016-01-19 Online:2018-02-20 Published:2018-02-11

Abstract

Abstract: This pilot study was aimed to investigate the feasibility of predicting rice yield with panicle spectral indices. A field experiment was conducted with two rice genotypes of contrasting yield potential and three contrasting nitrogen (N) levels at Hangzhou, China in 2015. Leaf and panicle hyperspectral reflectance (350-2 500 nm), chlorophyll concentrations (Chlc) and carotenoids concentrations (Carc) were measured at three different dates, and the yield components and grain yield were determined at maturing stage. It was found that the panicle spectra were distinctly different from the leaf spectra with the disappearance of the sharp green “hump”. As compared to the corresponding leaf indices, the panicle normalized vegetation index (NDVI) and photochemical reflectance index (PRI) were more reliable in differentiating the N levels, more sensitive to the chlorophyll content, and performed more accurately in predicting the rice yield. In the rice yield prediction based on best relationship fit, NDVI and PRI in the leaves at the three dates yielded a RSME of 873.4-1 125.0 and 723.3-889.4 kg·hm^-2, respectively, while NDVI and PRI in the panicles at the three dates achieved a RMSE of 681.7-743.1 and 515.0-637.8 kg·hm^-2, respectively.

Key words: rice, hyperspectral reflectance, vegetation indices, panicle, yield

CLC Number:

S511

JIANG Qingsu, CHENG Qilu, XU Ligen, ZHOU Qifa. Rice yield prediction with panicle spectral indices[J]. , 2018, 30(2): 187-193.

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Rice yield prediction with panicle spectral indices

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