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Different Algorithms for Detection of Malondialdehyde Content in Eggplant Leaves Stressed by Grey Mold Based on Hyperspectral Imaging Technique


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Abstract

The feasibility of using hyperspectral imaging (HSI) technique to measure malondialdehyde (MDA) content in eggplant leaves stressed by grey mold was evaluated in this paper. Hyperspectral images of infected and healthy eggplant leaves were obtained in the spectral region of 380 to 103000A0nm, and their spectral reflectance of region of interest (ROI) was extracted by Environment for Visualizing Images (ENVI 4.7) software. Several pre-processing methods were adopted and partial least squares (PLS) models were established to estimate MDA content in eggplant leaves. In order to reduce high dimensionality of spectral data, competitive adaptive re-weighted sampling (CARS) and latent variables (LV) were carried out to identify the most effective wavebands. The result showed that PLS model based on baseline pre-processing had a good performance for prediction set. On the basis of the effective wavelengths suggested by CARS and LV, PLS and multiple linear regression (MLR) models were established, respectively. Among these models, LV-MLR performed best with the highest value of correlation coefficient (r) and lowest value of root mean square error of prediction (RMSEP) for prediction set. The overall results demonstrated the potentiality of HSI technique as an objective and non-destructive method to detect MDA content in eggplant leaves stressed by grey mold.


Keywords


Pages

Total Pages: 13
Pages: 395-407

DOI
10.1080/10798587.2015.1015773


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Published

Volume: 21
Issue: 3
Year: 2015

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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