Literature DB >> 21714269

[Investigation of the hyperspectral image characteristics of wheat leaves under different stress].

Dong-Yan Zhang1, Jing-Cheng Zhang, Da-Zhou Zhu, Ji-Hua Wang, Ju-Hua Luo, Jin-Ling Zhao, Wen-Jiang Huang.   

Abstract

The diagnosis of growing status and vigor of crops under various stresses is an important step in precision agriculture. Hyperspectral imaging technology has the advantage of providing both spectral and spatial information simultaneously, and has become a research hot spot. In the present study, auto-development of the pushbroom imaging spectrometer (PIS) was utilized to collect hyperspectral images of wheat leaves which suffer from shortage of nutrient, pest and disease stress. The hyperspectral cube was processed by the method of pixel average step by step to highlight the spectral characteristics, which facilitate the analysis based on the differences of leaves reflectance. The results showed that the hyperspectra of leaves from different layers can display nutrient differences, and recognize intuitively different stress extent by imaging figures. With the 2 nanometer spectral resolution and millimeter level spatial resolution of PIS, the number of disease spot can be qualitatively calculated when crop is infected with diseases, and, the area of plant disease could also be quantitatively analyzed; when crop suffered from pest and insect, the spectral information of leaves with single aphid and aphids can be detected by PIS, which provides a new means to quantitatively detect the aphid destroying of wheat leaf. The present study demonstrated that hyperspecral imaging has a great potential in quantitative and qualitative analysis of crop growth.

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Year:  2011        PMID: 21714269

Source DB:  PubMed          Journal:  Guang Pu Xue Yu Guang Pu Fen Xi        ISSN: 1000-0593            Impact factor:   0.589


  1 in total

1.  Canopy Vegetation Indices from In situ Hyperspectral Data to Assess Plant Water Status of Winter Wheat under Powdery Mildew Stress.

Authors:  Wei Feng; Shuangli Qi; Yarong Heng; Yi Zhou; Yapeng Wu; Wandai Liu; Li He; Xiao Li
Journal:  Front Plant Sci       Date:  2017-07-13       Impact factor: 5.753

  1 in total

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