Scientia Agricultura Sinica ›› 2013, Vol. 46 ›› Issue (16): 3504-3513.doi: 10.3864/j.issn.0578-1752.2013.16.022

• RESEARCH NOTES • Previous Articles     Next Articles

Hyperspectral Monitoring of the Canopy Chlorophyll Content at Apple Tree Prosperous Fruit Stage

 FANG  Xian-Yi, ZHU  Xi-Cun, WANG  Ling, ZHAO  Geng-Xing   

  1. College of Resources and Environment, Shandong Agricultural University/National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer, Tai’an 271018, Shandong
  • Received:2012-12-04 Online:2013-08-15 Published:2013-05-28

Abstract: 【Objective】 The aims of the study are to promote the application of hyperspectral techniques in the real-time non-destructive detection of the apple precise fertilization and the diagnosis of the growth of the apple tree by building the quantitative relationship models between the apple canopy chlorophyll content and the characteristics of the canopy spectrum . 【Method】 Taking the apple trees in the Mengyin area as the experimental materials, the spectral reflectance and the apple canopy chlorophyll Chl(a+b) content were measured for two years continuously, the correlation coefficient between them was analyzed, and the RVI, DVI, NDVI and RDVI were calculated by combining any two bands from 400 nm to 1 000 nm. The relationship between them was analyzed and stepwise regression method was used to establish the apple canopy chlorophyll content monitoring model. 【Result】 The results showed that the best spectral index to estimate the content of the chlorophyll is NDVI(975,742), the correlation coefficient is 0.5093. The best apple canopy chlorophyll content monitoring model is Y=-0.56(log1/R)771-0.48 (log1/R)1978+0.20(log1/R)2407-0.10(log1/R)2440+4.749. 【Conclusion】 The model established by using stepwise regression can predict the apple canopy chlorophyll content better, thus it has provided a theoretical basis for reflecting apple growth conditions by using hyperspectral technology.

Key words: apple canopy , hyperspectrum , chlorophyll content , spectral indices , stepwise regression

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