Identification of Pesticide Residue Types in Spinach Leaves Based on Hyperspectral Imaging

JI Hai-yan; REN Zhan-qi; RAO Zhen-hong

doi:10.3788/fgxb20183912.1778

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Identification of Pesticide Residue Types in Spinach Leaves Based on Hyperspectral Imaging

更新时间：2020-08-12

- Identification of Pesticide Residue Types in Spinach Leaves Based on Hyperspectral Imaging
- Chinese Journal of Luminescence Vol. 39, Issue 12, Pages: 1778-1784(2018)
- 作者机构：
  
  1. 中国农业大学现代精细农业系统集成研究教育部重点实验室北京,100083
  2. 中国农业大学农业部农业信息获取技术重点实验室北京,100083
  3. 中国农业大学理学院北京,100083
- 作者简介：
- 基金信息：
  
  Supported by "The 13th Five-year S&
- DOI：10.3788/fgxb20183912.1778
  CLC： O433.4
- Received：15 March 2018，
  
  Revised：19 June 2018，
  
  Published Online：06 July 2018，
  
  Published：05 December 2018
- 稿件说明：
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吉海彦, 任占奇, 饶震红. 高光谱成像技术鉴别菠菜叶片农药残留种类[J]. 发光学报, 2018,39(12): 1778-1784

JI Hai-yan, REN Zhan-qi, RAO Zhen-hong. Identification of Pesticide Residue Types in Spinach Leaves Based on Hyperspectral Imaging[J]. Chinese Journal of Luminescence, 2018,39(12): 1778-1784
吉海彦, 任占奇, 饶震红. 高光谱成像技术鉴别菠菜叶片农药残留种类[J]. 发光学报, 2018,39(12): 1778-1784 DOI： 10.3788/fgxb20183912.1778.

JI Hai-yan, REN Zhan-qi, RAO Zhen-hong. Identification of Pesticide Residue Types in Spinach Leaves Based on Hyperspectral Imaging[J]. Chinese Journal of Luminescence, 2018,39(12): 1778-1784 DOI： 10.3788/fgxb20183912.1778.

摘要

利用高光谱成像技术无损鉴别菠菜叶片农药残留种类。采用高光谱成像仪采集900~1 700 nm波段内的光谱数据，采用多元散射校正对光谱数据进行预处理。利用主成分分析对不同种类菠菜样品的光谱数据进行分析，结果表明主成分分析能在可视化层面对不同种类的农药残留菠菜样品进行有效判别。另外，将卡方检验特征选择算法分别与支持向量机、朴素贝叶斯、决策树和线性判别分析算法结合，并采用10-fold交叉验证评价方法，筛选出最佳波段和最优判别模型（线性判别模型）。筛选出的8个特征波长为1 439.3，1 442.5，1 445.8，1 449，1 452.3，1 455.5，1 458.7，1 462 nm，模型的预测准确率达到0.993且10次交叉验证的标准差为0.009。结果表明，基于高光谱成像技术能准确地识别菠菜叶片上的农药残留种类。

Abstract

Non-destructive identification of pesticide residues in spinach was studied using hyperspectral imaging. The hyperspectral images between 900 nm and 1 700 nm were obtained with the help of hyperspectral imager. The original spectra were corrected by multivariate scatter correction (MSC). The principal component analysis (PCA) was used to analyze the spectral data of different spinach samples

the results showed that PCA could effectively discriminate different kinds of pesticide residues spinach samples on the visualization level. In addition

chi-squared test feature selection algorithm was separately combined with four learning algorithms (

e.g.

support vector machine

naive Bayes

decision tree and linear discriminant analysis) to get the best bands and optimal discriminant model(linear discriminant model) with the help of 10-fold cross-validation technique. The selected eight characteristic wavelengths are 1 439.3

1 442.5

1 445.8

1 449

1 452.3

1 455.5

1 458.7

1 462 nm and the prediction accuracy by optimal discriminant model is 0.993 and 10 times of cross validation standard deviation is 0.009. The results show that hyperspectral imaging technology can accurately identify the types of pesticide residues on spinach leaves.

关键词

Keywords

references

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