7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The composition of Pseudostellaria heterophylla (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: not found
          • Article: not found

          Random Forests

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

            Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              A Tutorial on Support Vector Machines for Pattern Recognition

                Bookmark

                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2576915Role: Role: Role: Role: Role: Role:
                Role: Role: Role:
                Role: Role:
                Role:
                Role:
                Role:
                Role:
                Role:
                Role:
                Role:
                URI : https://loop.frontiersin.org/people/389032Role:
                URI : https://loop.frontiersin.org/people/246747Role:
                URI : https://loop.frontiersin.org/people/1322909Role: Role: Role:
                URI : https://loop.frontiersin.org/people/466548Role: Role: Role:
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                15 January 2024
                2023
                : 14
                : 1342970
                Affiliations
                [1] 1 Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University , Fuzhou, China
                [2] 2 Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University , Fuzhou, China
                [3] 3 Pharmaceutical Development Board of Zherong County , Ningde, China
                [4] 4 Huzhou Wuxing Jinnong Ecological Agriculture Development Co., Ltd , Huzhou, China
                Author notes

                Edited by: Lie Deng, Southwest University, China

                Reviewed by: Zheli Wang, China Agricultural University, China

                Yong Hao, East China Jiaotong University, China

                *Correspondence: Yuanyuan Song, yyuansong@ 123456fafu.edu.cn ; Li Gu, guli5101@ 123456163.com

                †These authors have contributed equally to this work

                Article
                10.3389/fpls.2023.1342970
                10822997
                38288409
                af9a279e-89d7-4dee-a26a-b308b06a7631
                Copyright © 2024 Zhang, Lu, Song, Yang, Li, Yuan, Lin, Shi, Li, Yuan, Zhang, Zeng, Song and Gu

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 November 2023
                : 27 December 2023
                Page count
                Figures: 8, Tables: 4, Equations: 0, References: 40, Pages: 14, Words: 5667
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the National Natural Science Foundation of China (82373994 and 32371588), the China agriculture research system of MOF and MARA (CARS-21), the Natural Science Foundation of Fujian Province, China (2021J02024).
                Categories
                Plant Science
                Original Research
                Custom metadata
                Technical Advances in Plant Science

                Plant science & Botany
                pseudostellaria heterophylla,geographical origin,hyperspectral imaging,machine learning,deep learning

                Comments

                Comment on this article