Optimal Wavelengths Extraction of Apple Brix and Firmness Based on Hyperspectral Imaging

FENG Di; JI Jian-wei; ZHANG Li; LIU Si-jia; TIAN You-wen

doi:10.3788/fgxb20173806.0799

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Optimal Wavelengths Extraction of Apple Brix and Firmness Based on Hyperspectral Imaging

更新时间：2020-08-12

- Optimal Wavelengths Extraction of Apple Brix and Firmness Based on Hyperspectral Imaging
- Chinese Journal of Luminescence Vol. 38, Issue 6, Pages: 799-806(2017)
- 作者机构：
  
  1. 沈阳农业大学信息与电气工程学院, 辽宁沈阳 110866
  2. 辽宁广播电视台,辽宁沈阳,110004
  3. 辽宁广播电视传输发射中心,辽宁沈阳,110004
- 作者简介：
- 基金信息：
  
  Supported by Liaoning Large Scale Instrument and Equipment Sharing Service Project(L
- DOI：10.3788/fgxb20173806.0799
  CLC： S126;TP391.44
- Received：17 December 2016，
  
  Revised：08 March 2017，
  
  Published：05 June 2017
- 稿件说明：
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冯迪, 纪建伟, 张莉等. 基于高光谱成像提取苹果糖度与硬度最佳波长[J]. 发光学报, 2017,38(6): 799-806

FENG Di, JI Jian-wei, ZHANG Li etc. Optimal Wavelengths Extraction of Apple Brix and Firmness Based on Hyperspectral Imaging[J]. Chinese Journal of Luminescence, 2017,38(6): 799-806
冯迪, 纪建伟, 张莉等. 基于高光谱成像提取苹果糖度与硬度最佳波长[J]. 发光学报, 2017,38(6): 799-806 DOI： 10.3788/fgxb20173806.0799.

FENG Di, JI Jian-wei, ZHANG Li etc. Optimal Wavelengths Extraction of Apple Brix and Firmness Based on Hyperspectral Imaging[J]. Chinese Journal of Luminescence, 2017,38(6): 799-806 DOI： 10.3788/fgxb20173806.0799.

摘要

利用高光谱成像技术提取可同时检测苹果糖度与硬度的最佳波长。首先双面采集苹果的高光谱图像，获取亮度相近感兴趣区域（ROIs）的反射波形，采用二阶导数结合标准正态变量（SD+SNV）的方法平滑波形，测试ROIs的糖度与硬度;之后采用连续投影算法（SPA）提取两项指标的特征波长，根据特征波长的分布提出二次连续投影算法，结合波形集特征与两次投影结果确定不同取样面的最佳波长;最后采用遗传算法开发神经网络（GA-BP）建立预测模型，双面取样波长（543 nm和674 nm）效果最优，糖度相关系数（

）为0.847 6，均方误差（MSE）为3.32;硬度

为0.793 8，MSE为9.6。结果表明，相同波长信息可以检测苹果糖度与硬度。

Abstract

Hyperspectral imaging technology was used to extract the optimal wavelength for apple brix and firmness test. Firstly

the hyperspectral images of apples were acquired from double-sided sampling. The reflection waveforms of the regions of interest (RIOs) with similar brightness were acquired and smoothed by the second derivation and standard normal variate (SD+SNV) method. The brix and firmness values of RIOs were also tested. Then

the characteristic wavelengths of two indicators were extracted by using the successive projections agorithm(SPA). According to the distribution of characteristics wavelengths

two times SPA was proposed. Combined the feature of waveforms and the results of two projections

the optimal wavelengths of different sampling facets were determined. Finally

the genetic algorithm for back propagation(GA-BP) was used to build the prediction model. The best results were obtained from the double-sided sampling wavelengths (543 nm and 674 nm). The correlation coefficient of brix (

) is 0.847 6 and the mean square error (MSE) is 3.32

and for the firmness

is 0.793 8 and MSE is 9.6. The results show that the brix and firmness can be detected by the same wavelength information.

关键词

Keywords

references

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