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Heavy metal lead (Pb) accumulation in agricultural soil has become a serious threat to human health through the food chain in recent decades. Wheat, being one of the most important crops, is a crucial source of food for humans. Pb in soil can pose a significant risk to the wheat grain, as it can be transported through the root and stem. This transportation not only increases the Pb concentration in wheat, but also threatens the wheat's overall food security. Therefore, it is imperative to investigate the effects of Pb stress on wheat seedlings. Double-pulse laser-induced breakdown spectroscopy (DP-LIBS) and surface-enhanced Raman spectroscopy (SERS) as promising enhanced spectroscopy techniques were coupled to estimate Pb stress in wheat seedlings. Wheat seedling samples under Pb stress at 6 concentration gradients (0(c.k.), 500, 1000, 2000, 3000, and 5000 mg L−1) were cultivated with the same substrate and management. The SERS and DP-LIBS spectra of the root, stem and leaf were obtained by using spectrometers in an enhanced way. Principal component analysis (PCA) was employed for preliminary analysis of DP-LIBS and SERS data to investigate the clustering of samples, including substrates and the wheat seedling root and leaf, under different Pb concentration stresses. However, while PCA enabled the rough differentiation of the samples, the classification accuracy requires improvement. The pre-processed SERS, DP-LIBS, and fusion spectral data (LIBS-Raman) were analyzed using discriminant models such as least squares support vector machines (LS-SVM), multi-layer perceptron-artificial neural network (MLP-ANN), radial basis function-artificial neural network (RBF-ANN) and probabilistic neural networks (PNN). The accuracy of the model built by LIBS-Raman was significantly better than the model built based on SERS and DP-LIBS data alone. LIBS-Raman increased the accuracy of the discrimination model, which highlights the effectiveness of collaborative spectroscopic methods for heavy metal detection. This study investigated the migration of Pb in the substrate-root-leaf. At 0 concentration (c.k.), Pb was not detected in the substrate and wheat seedling root and leaf. At the same concentration, the level of Pb stress in the root was higher than that in the leaf. This might be attributed to the self-protection mechanism of wheat seedlings, which had a weak ability to transport Pb ions. This work provides theoretical support for collaborative spectral data, furnishes a reference for the development of a combined spectrometer, and offers a valuable method for monitoring heavy metal contamination in plants. [ABSTRACT FROM AUTHOR]

Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments. [ABSTRACT FROM AUTHOR]

[Display omitted] • Acquisition of rapid response curves and spectral peak attribution of samples. • Rapid detection of Cu stress level by SERS and SAE-CNN model. • TEM confirmed that excessive copper damaged the cell structure of apple rootstock. Copper-containing chemicals are commonly used in orchard management, and their use over a long time causes excessive copper content in orchard soil. Surface-enhanced Raman spectroscopy (SERS) technique combined with deep learning (DL) methods were used to characterize the response of apple rootstocks under heavy metal Cu stress. The hydroponic method prepared Apple rootstocks under five concentration gradients of heavy metal Cu stress as the experimental samples. The Raman spectral data were collected and subjected to spectral preprocessing. A stacked auto-encoder convolutional neural network (SAE-CNN) model was used to develop a rapid discriminative model for root, stem, and leaf stress levels of heavy metal Cu on apple rootstocks. The SAE-CNN models all outperformed the traditional models, with performance metrics above 99%. The results showed that the proposed SAE-CNN model could rapidly discriminate heavy metal Cu in apple rootstocks. [ABSTRACT FROM AUTHOR]

[Display omitted] • HSI and CNN models for rapid detection of heavy metal Cu contamination levels in apple rootstocks. • The CNN model showed superior performance in all aspects compared to the conventional model. • PCA and t-SNE data visualization for model validation. In recent years, the prolonged application of copper-based fungicides, ripening agents, and fertilizers in agricultural activities has resulted in copper (Cu) levels in the soil exceeding safe levels. Excess Cu has a negative impact on plant growth and crop yields. The practice of grafting horticultural crops onto appropriate rootstocks is frequently employed as a preventive measure against copper accumulation and its associated toxicity. Therefore, the selection of high-quality rootstocks that are not contaminated with heavy metal Cu is important for the growth of grafted crops and the breeding of grafted crops. This study proposed a technique based on convolutional neural network (CNN) and hyperspectral imaging (HSI) for the rapid, non-destructive, and accurate identification of copper contamination levels in apple rootstocks. The effect of different heavy metal Cu contamination levels on leaves was obtained by measuring chlorophyll content in leaves using the ultraviolet spectrophotometer method. A hybrid strategy for spectral variable selection was utilized, involving competitive adaptive reweighted sampling and successive projection algorithms. A traditional partial least squares discriminant analysis linear classification model, and least squares support vector machine, random forest, and extreme learning machine nonlinear models were developed. The results showed that the accuracy of the CNN model and Macro-F1 were 99.6% and 99.2%, respectively, which were better than the traditional linear and nonlinear models. Principal component analysis and t-distributed stochastic neighbor embedding were used for visual analysis to investigate the clustering patterns among sample classes. The results indicated that the combination of CNN and HSI techniques has great potential for classifying copper contamination levels in apple rootstocks. [ABSTRACT FROM AUTHOR]

Accurate detection of heavy metal stress on the growth status of plants is of great concern for agricultural production and management, food security, and ecological environment. A proximal hyperspectral imaging (HSI) system covered the visible/near-infrared (Vis/NIR) region of 400–1000 nm coupled with machine learning methods were employed to discriminate the tobacco plants stressed by different concentration of heavy metal Hg. After acquiring hyperspectral images of tobacco plants stressed by heavy metal Hg with concentration solutions of 0 mg·L−1 (non-stressed groups), 1, 3, and 5 mg·L−1 (3 stressed groups), regions of interest (ROIs) of canopy in tobacco plants were identified for spectra processing. Meanwhile, tobacco plant's appearance and microstructure of mesophyll tissue in tobacco leaves were analyzed. After that, clustering effects of the non-stressed and stressed groups were revealed by score plots and score images calculated by principal component analysis (PCA). Then, loadings of PCA and competitive adaptive reweighted sampling (CARS) algorithm were employed to pick effective wavelengths (EWs) for discriminating non-stressed and stressed samples. Partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were utilized to estimate the stressed tobacco plants status with different concentrations Hg solutions. The performances of those models were evaluated using confusion matrixes (CMes) and receiver operating characteristics (ROC) curves. Results demonstrated that PLS-DA models failed to offer relatively good result, and this algorithm was abandoned to classify the stressed and non-stressed groups of tobacco plants. Compared to LS-SVM model based on full spectra (FS-LS-SVM), the LS-SVM model established EWs selected by CARS (CARS-LS-SVM) carried 13 variables provided an accuracy of 100%, which was promising to achieve the qualitative discrimination of the non-stressed and stressed tobacco plants. Meanwhile, for revealing the discrepancy between 3 stressed groups of tobacco plants, the other FS-LS-SVM, PCA-LS-SVM, and CARS-LS-SVM models were setup and offered relatively low accuracies of 55.56%, 51.11% and 66.67%, respectively. Performance of those 3 LS-SVM discriminative models was also poorly performing to differentiate 3 stressed groups of tobacco plants, which might be caused by low concentration of heavy metal and similar canopy (especially in fresh leaves) of plant. The achievements of the research indicated that HSI coupled with machine learning methods had a powerful potential to discriminate tobacco plant stressed by heavy metal Hg. Unlabelled Image • Conduct the clustering analysis using PCA on hyperspectral data of tobacco plants stressed by heavy metal Hg • Identify the EWs selected by loading of PCA and CARS for discriminating canopy characteristic of tobacco plants • Employ CM and ROC curve to evaluate discrimination models for revealing distinction of the different groups of tobacco plants [ABSTRACT FROM AUTHOR]