Detection of lead content in oilseed rape leaves and roots based on deep transfer learning and hyperspectral imaging technology

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 hyper -spectral 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 ac-curacy of the best T-SAE model of Pb stress gradient in oilseed rape plants was 98.75%. Additionally, the pre-diction 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 hyper -spectral imaging technology can effectively realize the the qualitative and quantitative detection of heavy metal Pb in oilseed rape plants.

Automated summary

The evaluation capability of hyperspectral imaging technology for forecasting heavy metal lead concentration in oilseed rape plants was studied. A transfer stacked auto-encoder (T-SAE) algorithm, including dual-model and single-model T-SAE, was proposed. The hyper -spectral images of oilseed rape leaf and root under different Pb stress concentrations were acquired. Preprocessing methods like SNV and 1st Der were used to extract the spectral data, and PCA algorithm was employed for dimensionality reduction. Deep learning networks were built and T-SAE models were obtained through transfer learning. The results showed high accuracy in predicting Pb stress gradient and content in oilseed rape plants.