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Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases. [ABSTRACT FROM AUTHOR]

Near-infrared (874–1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972–1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries. [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]

[Display omitted] • Acquiring Raman spectra of pure dried persimmon icing and titanium dioxide adulteration samples using a hand-portable Raman spectrometer; • Conducting preprocessing of Raman spectral of tested samples based on air-PLS algorithm; • Developing qualitative and quantitive analysis for detecting titanium dioxide adulteration in persimmon icing using Machine Learning. Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing. [ABSTRACT FROM AUTHOR]