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© 2019. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Hyperspectral imaging, with many narrow bands of spectra, is strongly capable to detect or classify objects. It has been become one research hotspot in the field of near-ground remote sensing. However, the higher demands for computing and complex operating of instrument are still the bottleneck for hyperspectral imaging technology applied in field. Band selection is a common way to reduce the dimensionality of hyperspectral imaging cube and simplify the design of spectral imaging instrument. In this research, hyperspectral images of blueberry fruit were collected both in the laboratory and in field. A set of spectral bands were selected by analyzing the differences among blueberry fruits at different growth stages and backgrounds. Furthermore, a normalized spectral index was set up using the bands selected to identify the three growth stages of blueberry fruits, aiming to eliminate the impact of background included leaf, branch, soil, illumination variation and so on. Two classifiers of spectral angle mapping (SAM), multinomial logistic regression (MLR) and classification tree were used to verify the results of identification of blueberry fruit. The detection accuracy was 82.1% for SAM classifier using all spectral bands, 88.5% for MLR classifier using selected bands and 89.8% for decision tree using the spectral index. The results indicated that the normalization spectral index can both lower the complexity of computing and reduce the impact of noisy background in field.

Details

Title
Spectral difference analysis and identification of different maturity blueberry fruit based on hyperspectral imaging using spectral index
Author
Ma, Hao 1 ; Zhao, Kaixuan 1 ; Jin, Xin 1 ; Ji, Jiangtao 1 ; Qiu, Zhaomei 1 ; Gao, Song

 College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China 
Pages
134-140
Publication year
2019
Publication date
May 2019
Publisher
International Journal of Agricultural and Biological Engineering (IJABE)
ISSN
19346344
e-ISSN
19346352
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2244074499
Copyright
© 2019. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.