The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.
To realize the non-destructive identification of Panax notoginseng powder in different parts, we propose a non-destructive identification method based on the electronic nose and time-domain feature extraction. First, The electronic nose technology combined with statistical analysis method was used to collect and extract nine time-domain characteristics of the response information of Panax notoginseng whole root powder, tap root powder, rhizome powder, and fibrous powder, including the data at 110s, the mean value between 101-120s, the maximum value, minimum value, integral value, differential value, skewness factor, kurtosis factor, and standard deviation between 0-120s. Next, three classical feature selection method were used to reduce the data dimension. Subsequently, the classification models of support vector machine (SVM), least-square support vector machine (LSSVM), and extreme learning machine (ELM) were established based on original data, multi-feature data, and feature selection data. Finally, the Grey Wolf Optimization (GWO) algorithms were introduced to optimize the parameters of the classification model. The results show that the GWO-CARS-LSSVM achieved the best modeling effect, and the classification accuracy on the test set was 97.92%.Therefore, This study provides a theoretical basis and technical support for rapid identification of adulteration of Panax notoginseng powder.
Panax notoginseng saponin (PNS) is the most important physical and chemical index of panax notoginseng. In order to detect PNS rapidly and non-destructively, 160 hyperspectral images of panax notoginseng rhizome and main root were acquired by using a visible-near infrared hyperspectral image acquisition system (400-1000 nm), and the original spectrum were extracted from hyperspectral images. The signal-to-noise ratio of the spectrum was improved by savitzky-golay mixed multiplication scatter correction (SG-MSC) pretreatment. Feature wavelengths were extracted by using competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS), and support vector regression (SVR) model was established based on the feature spectrum and the original spectrum. By comparing, it was found that BOSS had the best effect of feature selection. In order to improve the accuracy of the model, equilibrium optimizer (EO) was used to optimise the parameters (c, g) of the BOSS-SVR model. The results showed that BOSS-EO-SVR of the optimal prediction model of PNS, achieving R 2 P and RMSEP of 0.95 and 0.32%, respectively. Therefore, hyperspectral imaging combined with BOSS-EO-SVR model is a feasible method to detect PNS.
The classification of the taproots of Panax notoginseng is conducive to improving the economic added value of its products. In this study, a real-time sorting robot system for Panax notoginseng taproots was developed based on the improved DeepLabv3+ model. The system is equipped with the improved DeepLabv3+ classification model for different grades of Panax notoginseng taproots. The model uses Xception as the taproot feature extraction network of Panax notoginseng. In the residual structure of the Xception network, a group normalization layer with deep separable convolution is adopted. Meanwhile, the global maximum pooling method is added in the Atrous Spatial Pyramid Pooling (ASPP) part to retain more texture information, and multiple shallow effective feature layers are designed to overlap in the decoding part to minimize the loss of features and improve the segmentation accuracy of Panax notoginseng taproots of all grades. The model test results show that the Xception-DeepLabv3+ model performs better than VGG16-U-Net and ResNet50-PSPNet models, with a Mean Pixel Accuracy (MPA) and a Mean Intersection over Union (MIoU) of 78.98% and 88.98% on the test set, respectively. The improved I-Xce-DeepLabv3+ model achieves an average detection time of 0.22 s, an MPA of 85.72%, and an MIoU of 90.32%, and it outperforms Xce-U-Net, Xce-PSPNet, and Xce-DeepLabv3+ models. The system control software was developed as a multi-threaded system to design a system grading strategy, which solves the problem that the identification signal is not synchronized with the grading signal. The system test results show that the average sorting accuracy of the system is 77% and the average false detection rate is 21.97% when the conveyor belt running speed is 1.55 m/s. The separation efficiency for a single-channel system is 200–300 kg/h, which can replace the manual work of three workers. The proposed method meets the requirements of current Panax notoginseng processing enterprises and provides technical support for the intelligent separation of Panax notoginseng taproots.
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