Detection of lead content in oilseed rape leaves and roots based on deep transfer learning and hyperspectral imaging technology.
Detection of lead content in oilseed rape leaves and roots based on deep transfer learning and hyperspectral imaging technology.
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DOI：
10.1016/j.saa.2022.122288
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Xin Zhou;Chunjiang Zhao;Jun Sun;Kunshan Yao;Min Xu
中科院分区：
文献类型：
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作者：
Xin Zhou;Chunjiang Zhao;Jun Sun;Kunshan Yao;Min Xu
研究了高光谱成像技术对油菜植株重金属铅含量预测的评价能力。此外，本文还提出了一种传输堆栈式自动编码器（T-SAE）算法，包括双模T-SAE和单模T-SAE两种网络方法。在不同浓度铅胁迫条件下，采集了油菜叶片和根系的高光谱图像。以油菜叶片（或根）的整个区域作为感兴趣区域（ROI）提取光谱数据，采用标准归一化变量（SNV）、一阶导数（1st Der）和二阶导数（2nd Der）对ROI光谱进行预处理。此外，利用主成分分析（PCA）算法对预处理前后的光谱数据进行降维处理。因此，确定了最佳预处理数据，以供后续研究和分析。此外，基于油菜叶数据、油菜根数据以及基于最佳预处理光谱数据的油菜叶和根组合数据，构建SAE深度学习网络。最后，通过最佳SAE深度学习网络的迁移学习获得T-SAE模型。结果表明，油菜叶片光谱和根系光谱的最佳预处理算法分别为SNV和1st Der算法。此外，油菜铅胁迫梯度的最佳T-SAE模型的预测集识别准确率为98.75%。油菜叶片和根系铅含量最佳T-SAE模型的预测集决定系数分别为0.9215和0.9349。因此，深度迁移学习方法结合高光谱成像技术可以有效实现油菜植株中重金属Pb的定性和定量检测。
The evaluation capability of hyperspectral imaging technology was studied for the forecasts of heavy metal lead concentration of oilseed rape plant. In addition, a transfer stacked auto-encoder (T-SAE) algorithm including two network methods, the dual-model T-SAE and the single-model T-SAE, was proposed in this paper. The hyperspectral images of oilseed rape leaf and root were acquired under different Pb stress concentrations. The entire region of the oilseed rape leaf (or root) was selected as the region of interest (ROI) to extract the spectral data, and standard normalized variable (SNV), first derivative (1st Der) and second derivative (2nd Der) were used to preprocess the ROI spectra. Besides, the principal component analysis (PCA) algorithm was used to reduce the dimensionality of the spectral data before and after preprocessing. Hence, the best pre-processed data was determined for subsequent research and analysis. Furthermore, the SAE deep learning networks were built based on the oilseed rape leaf data, oilseed rape root data, and the combined data of oilseed rape leaf and root based on the best pre-processed spectral data. Finally, the T-SAE models were obtained through transfer learning of the best SAE deep learning network. The results show that the best preprocessing algorithms of the oilseed rape leaf and root spectra were SNV and 1st Der algorithm, respectively. In addition, the prediction set recognition accuracy of the best T-SAE model of Pb stress gradient in oilseed rape plants was 98.75%. Additionally, the prediction set coefficient of determination of the best T-SAE model of the Pb content in the oilseed rape leaf and root data were 0.9215 and 0.9349, respectively. Therefore, a deep transfer learning method combined with hyperspectral imaging technology can effectively realize the the qualitative and quantitative detection of heavy metal Pb in oilseed rape plants.