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| 烤烟叶片镉含量高光谱预测模型的构建 |
| Establishment of hyperspectral prediction model for cadmium content in flue-cured tobacco leaves |
| Received:August 28, 2020 |
| DOI：10.13254/j.jare.2020.0471 |
| 中文关键词: 烟草，镉，高光谱，模型，BP神经网络 |
| 英文关键词: tobacco, cadmium, hyperspectral, model, BP neural network |
| 基金项目:国家重点研发计划课题（2017YFD0200808）；河南青年骨干教师资助项目（2020GGJS047）；南平烟草公司重点科技攻关项目（南烟司叶[2017]21号）；广东中烟科技攻关项目（2020440000340029） |
| Author Name | Affiliation | E-mail | | CHEN Nan | Tobacco College of Henan Agricultural University/Henan Biochar Engineering Research Center, Zhengzhou 450002, China
Henan Biochar Engineering Technology Research Center, Zhengzhou 450002, China | | | FENG Hui-lin | Tobacco College of Henan Agricultural University/Henan Biochar Engineering Research Center, Zhengzhou 450002, China
Henan Biochar Engineering Technology Research Center, Zhengzhou 450002, China | | | YANG Yan-dong | Tobacco College of Henan Agricultural University/Henan Biochar Engineering Research Center, Zhengzhou 450002, China
Henan Biochar Engineering Technology Research Center, Zhengzhou 450002, China
Agricultural College of Nanjing Agricultural University, Nanjing 210095, China | | | CHEN Ping | Tobacco College of Henan Agricultural University/Henan Biochar Engineering Research Center, Zhengzhou 450002, China | | | REN Tian-bao | Tobacco College of Henan Agricultural University/Henan Biochar Engineering Research Center, Zhengzhou 450002, China
Henan Biochar Engineering Technology Research Center, Zhengzhou 450002, China | tianbao1016@126.com | | JIA Fang-fang | College of Biology and Food, Shangqiu Normal University, Shangqiu 476000, China | jiafang840928@163.com | | LIU Guo-shun | Tobacco College of Henan Agricultural University/Henan Biochar Engineering Research Center, Zhengzhou 450002, China
Henan Biochar Engineering Technology Research Center, Zhengzhou 450002, China | |
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| 中文摘要: |
| 为快速准确地获取烟草叶片镉含量，本研究模拟了4个镉污染水平，用美国ASD光谱仪获取每个污染水平的烟草叶片光谱反射率，并测定不同时期烟草叶片的镉含量，筛选出与镉含量相关性最好的敏感波段，并建立光谱参数，将光谱参数作为输入因子建立烟草叶片镉含量的BP神经网络模型。结果表明：随着镉含量增加，在可见光和近红外范围（400~910 nm）内反射率先降低后增加，在930~1 000 nm波段范围内，叶片反射率与烟叶中镉含量呈正相关，在1 000~2 500 nm波段范围内反射率先增加后降低。经筛选，比值植被指数（RVI）和归一化植被指数（NDVI）的光谱指数分别为RVI （520，710）和NDVI （530，710）； BP神经网络模型的决定系数（R2）为0.681，均方根误差（RMSE）为8.001，并对模型进行检验，R2为0.801，RMSE为4.430。研究表明，BP神经网络模型对烟草叶片镉含量具有良好的预测效果。 |
| 英文摘要: |
| Heavy metal pollution has become the focus of environmental biology and crop quality and safety. In order to obtain the cadmium content of tobacco leaves quickly and accurately, four cadmium pollution levels were simulated. The spectral reflectance of tobacco leaves in each treatment was obtained using an American ASD spectrometer, and the cadmium content of tobacco leaves in different periods was measured. The sensitive bands with the best correlation with the cadmium content were selected, and the spectral parameters were used as input factors to establish a back propagation(BP) neural network model of the cadmium content in tobacco leaves. The results showed that as the cadmium content increased, the reflectance in the visible light and near-infrared range(400~910 nm) first decreased and then increased. In the wavelength range of 930~1 000 nm, the leaf reflectivity was positively correlated with the cadmium content in the tobacco leaf. The reflectance in the 1 000~2 500 nm broadband range first increased and then decreased. The selected spectral indexes ratio vegetation index(RVI) and normalized difference vegetation index(NDVI) were RVI(520, 710) and NDVI(530, 710), respectively. The R2 of the BP neural network model was 0.681 and the root mean square error(RMSE) was 8.001, the model was tested, and the test results showed that the R2 was 0.801 and the RMSE was 4.430. The results showed that the BP neural network model could provide a good prediction of the cadmium content in tobacco leaves. |
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