农产品品质光谱成像的空间预测规律

杨君荣; 汪希伟; 赵茂程; 陈一鸣; 姚凤莹

doi:10.3788/fgxb20183904.0580

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农产品品质光谱成像的空间预测规律

发光学应用及交叉前沿 | 更新时间：2020-08-12

- 农产品品质光谱成像的空间预测规律
- Law of Spatial Prediction of Agro-product Qualities Based on Spectral Imaging
- 发光学报 2018年39卷第4期页码：580-588
- 作者机构：
  
  1. 南京林业大学机械电子工程学院,江苏南京,210037
  2. 泰州学院, 江苏秦州,225300
  3. 南京航空航天大学机电学院, 江苏南京 210016
- 作者简介：
- 基金信息：
  
  国家自然科学基金面上项目（31570714）();江苏省重点研发计划（BE2015304-3）();江苏省高校优势学科建设工程（PAPD）();2016年度省级战略性新
- DOI：10.3788/fgxb20183904.0580
  中图分类号： TS201.2
- 纸质出版日期：2018-4-5，
  
  网络出版日期：2017-11-27，
  
  收稿日期：2017-8-15，
  
  修回日期：2017-11-3，
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杨君荣, 汪希伟, 赵茂程等. 农产品品质光谱成像的空间预测规律[J]. 发光学报, 2018,39(4): 580-588

YANG Jun-rong, WANG Xi-wei, ZHAO Mao-cheng etc. Law of Spatial Prediction of Agro-product Qualities Based on Spectral Imaging[J]. Chinese Journal of Luminescence, 2018,39(4): 580-588
杨君荣, 汪希伟, 赵茂程等. 农产品品质光谱成像的空间预测规律[J]. 发光学报, 2018,39(4): 580-588 DOI： 10.3788/fgxb20183904.0580.

YANG Jun-rong, WANG Xi-wei, ZHAO Mao-cheng etc. Law of Spatial Prediction of Agro-product Qualities Based on Spectral Imaging[J]. Chinese Journal of Luminescence, 2018,39(4): 580-588 DOI： 10.3788/fgxb20183904.0580.

摘要

由于受到化学检测手段限制，无法获取光谱图像中的农产品品质在各像素位置处的参考值，因此无法直接验证基于光谱图像得到的农产品品质空间预测结果。为探索基于光谱图像的农产品空间品质检测规律，本文采用计算机生成已知空间品质样本，并分别以固定曝光和变曝光方式采集不同灰度等级标准板的光谱图像。定量分析采集系统误差，借助样本区域光谱与区域指标之间的全波段偏最小二乘（ALL-PLS）和遗传特征波长偏最小二乘（GA-PLS）预测函数，研究出区域指标预测准确时样本空间品质指标的预测精度规律，建立光谱成像空间预测准确度模型。实验结果表明：变曝光方式下的数据采集可以提高光谱图像信噪比，在波段两侧极限处尤为明显；应用区域品质数据预测空间品质分布，空间预测误差主要受光谱图像采集噪声影响：即使区域预测准确，空间预测可能已完全失真。通过衡量数据采集系统误差，可以间接评价农产品品质空间预测的准确度。只有在数据采集系统误差在允许范围内时，光谱成像技术才可准确预测农产品品质的空间分布。

Abstract

Due to the limitation of chemical testing methods

we cannot get reference value of agro-product qualities in every pixel location and are not directly to verify spatial prediction results of agro-product qualities based on spectral images. In order to research spatial prediction laws of agro-product qualities based on spectral images

the computer was used to generate the samples of known spatial distribution and to acquire the spectral images of different gray-level reflectance standard boards by ways of fixed and variable exposures respectively. The error of data acquisition system was analyzed quantitatively. The spatial prediction accuracy model of spectral imaging was built and the prediction accuracy law of spatial qualities was researched as regional parameter values which could be predicted accurately by means of partial least squares(PLS) prediction methods based on full wavelengths and characteristic wavelengths between regional spectral values and regional parameter values. The results show the signal-to-noise ratio of the spectral image can be improved by changing exposure time and it is more obvious on both sides of the spectral band. When these models that can predict regional parameter values accurately are used to predict space parameter values

their spatial prediction accuracy is affected by error in a similar way. The accuracy of spatial prediction results of agro-product qualities can be evaculated by the way of measuring the error of the data acuisition system. Spectral image detection technology can be used to predict spatial parameter values of agro-product qualities when the error of acquisition system is limited in a specified range.

关键词

空间预测光谱图像遗传算法偏最小二乘系统误差

Keywords

space predictionspectral imagegenetic algorithmpartial least squaressystematic error

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