Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images
Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images
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DOI：
10.1080/01431161.2019.1685721
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发表时间：
2020-03-18
影响因子：
3.4
通讯作者：
Chen, Quansheng
中科院分区：
文献类型：
--
作者：
Zhou, Xin;Sun, Jun;Chen, Quansheng
研究了可见-近红外（Vis-NIR）高光谱成像技术用于生菜叶片中铅含量测定的有效性和可靠性。此外，本文还提出了一种基于小波变换和层叠式自动编码器（WT-SAE）的光谱数据多尺度分解方法，以获取光谱的深层特征。获取了1120个生菜叶片样品的可见光近红外高光谱图像，并对整个区域的生菜叶片样品光谱数据进行了采集和预处理。此外，WT-SAE使用db 5作为小波基函数的深光谱特征，并使用支持向量机回归（SVR）进行回归建模。此外，对生菜叶片中铅含量的预测效果最好，校正决定系数（R-c（2））为0.9911，校正均方根误差（RMSEC）为0.05187，预测决定系数（R-p（2））为0.9590，预测均方根误差（RMSEP）为0.05587，残差预测偏差（RPD）为3.251，使用db 5作为小波基函数，小波第五层分解。研究结果表明，WT-SAE能够有效地选择最优深光谱特征，可见光-近红外高光谱成像在生菜叶片铅含量检测中具有很大的潜力。
The validity and reliability of visible-near infrared (Vis-NIR) hyperspectral imaging were investigated for the determination of lead concentration in lettuce leaves. Besides, a method involving wavelet transform and stacked auto-encoders (WT-SAE) is proposed to decompose the spectral data in the multi-scale transform and obtain the deep spectral features. The Vis-NIR hyperspectral images of 1120 lettuce leaf samples were obtained and the whole region of lettuce leaf sample spectral data was collected and preprocessed. In addition, WT-SAE the deep spectral features using db5 as wavelet basis function, and support vector machine regression (SVR) was used for regression modelling. Furthermore, the best prediction performances for detecting lead (Pb) concentration in lettuce leaves was obtained from raw data set, with coefficient of determination for calibration (R-c(2)) of 0.9911, root mean square error for calibration (RMSEC) of 0.05187, coefficient of determination for prediction (R-p(2)) of 0.9590, root mean square error for prediction (RMSEP) of 0.05587 and residual predictive deviation (RPD) of 3.251 using db5 as wavelet basis function with wavelet fifth layer decomposition. The results of this study indicated that WT-SAE can effectively select the optimal deep spectral features and Vis-NIR hyperspectral imaging has great potential for detecting lead content in lettuce leaves.