A Real-Time Sorting Robot System for Panax Notoginseng Taproots Equipped with an Improved Deeplabv3+ Model

Zhang, Fujie; Lin, Yuhao; Zhu, Yongwei; Li, Lixia; Cui, Xiuming; Gao, Yongping

doi:10.3390/agriculture12081271

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Zhu et al classified the taproot grades of Panax notoginseng by integrating the size, shape, color, texture, and other characteristics into traditional machine vision technology. F. Zhang, Lin, et al (2022) used machine vision technology and the improved DeepLabV3+ model to successfully develop a real‐time robotic system for classifying Panax notoginseng. The machine vision and weighing methods, however, cannot effectively classify Panax notoginseng by grades after it is sliced; thus, neither method is suitable for identifying the grade of PNS.…”

Section: Introductionmentioning

confidence: 99%

Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices

Zhang

Shi

et al. 2023

Journal of Food Safety

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Panax notoginseng slices (PNS) are prepared from the taproot of a rare Chinese herbal plant, Panax notoginseng. The price and efficacy of PNS change depending on its grade, but substandard PNS are more prevalent in the Asian market. In this study, a portable near‐infrared spectrometer was used to collect the spectra of 240 PNS samples divided into four grades. The spectral data were preprocessed by the Savitzky–Golay (SG) filter to eliminate noise interference. Principal component analysis (PCA), competitive adaptive reweighted sampling, and variable combination population analysis were used to extract the feature variables of the spectral data. The selected feature variables were used to establish least squares support vector machine (LSSVM), support vector machine (SVM), and extreme learning machine (ELM) classification models. To further improve the classification accuracy of the most effective of these models, a grey wolf optimizer (GWO) was introduced, and particle swarm optimization (PSO) as well as genetic algorithm (GA) were used to conduct comparative analyses. The results showed that PCA provided accurate identification information of different PNS grades and that the classification effect of the LSSVM model was better than that of the ELM and SVM models. During the optimization process, the optimization accuracy of the GWO was better than that of the PSO and GA systems. Therefore, the optimal classification model was established as GWO–PCA–LSSVM, and the classification accuracy of the test set was 91.67%. Therefore, portable near‐infrared spectroscopy technology can be used to identify the grade of PNS effectively.

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Section: Introductionmentioning

confidence: 99%

Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices

Zhang

Shi

et al. 2023

Journal of Food Safety

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“…Though the classification of Gastrodia elata takes both weights and shape into consideration, it is only sorted by manual experience or only considering weight, leading to low sorting accuracy and a heavy workload. The application of computer vision technology based on deep learning to the detection and classification of agricultural products, such as fruits [10,11], vegetables [12,13], and Chinese medicinal materials [14,15], provides a reference for the visual classification of Gastrodia elata.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Detection of Gastrodia elata Based on Improved YOLOX

Duan,

Lin,

et al. 2023

Agronomy

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Identifying the grade of Gastrodia elata in the market has low efficiency and accuracy. To address this issue, an I-YOLOX object detection algorithm based on deep learning and computer vision is proposed in this paper. First, six types of Gastrodia elata images of different grades in the Gastrodia elata planting cooperative were collected for image enhancement and labeling as the model training dataset. Second, to improve feature information extraction, an ECA attention mechanism module was inserted between the backbone network CSPDarknet and the neck enhancement feature extraction network FPN in the YOLOX model. Then, the impact of the attention mechanism and application position on model improvement was investigated. Third, the 3 × 3 convolution in the neck enhancement feature extraction network FPN and the head network was replaced by depthwise separable convolution (DS Conv) to reduce the model size and computation amount. Finally, the EIoU loss function was used to predict boundary frame regression at the output prediction end to improve the convergence speed of the model. The experimental results indicated that compared with the original YOLOX model, the mean average precision of the improved I-YOLOX network model was increased by 4.86% (97.83%), the model computation was reduced by 5.422 M (reaching 3.518 M), the model size was reduced by 20.6 MB (reaching 13.7 MB), and the image frames detected per second increased by 3 (reaching 69). Compared with other target detection algorithms, the improved model outperformed Faster R-CNN, SSD-VGG, YOLOv3s, YOLOv4s, YOLOv5s, and YOLOv7 algorithms in terms of mean average precision, model size, computation amount, and frames per second. The lightweight model improved the detection accuracy and speed of different grades of Gastrodia elata and provided a theoretical basis for the development of online identification systems of different grades of Gastrodia elata in practical production.

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