IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i12p1274-d703029.html
   My bibliography  Save this article

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

Author

Listed:
  • Xingpeng Li

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Hongzhe Jiang

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Xuesong Jiang

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Minghong Shi

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.

Suggested Citation

  • Xingpeng Li & Hongzhe Jiang & Xuesong Jiang & Minghong Shi, 2021. "Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm," Agriculture, MDPI, vol. 11(12), pages 1-19, December.
  • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1274-:d:703029
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/12/1274/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/11/12/1274/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    2. Jerick Imbao & Jeroen A. Bokhoven & Adam Clark & Maarten Nachtegaal, 2020. "Elucidating the mechanism of heterogeneous Wacker oxidation over Pd-Cu/zeolite Y by transient XAS," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Martina Vrešak & Merete Halkjaer Olesen & René Gislum & Franc Bavec & Johannes Ravn Jørgensen, 2016. "The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-10, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang Zhang & Bora Cetin & Tuncer B. Edil, 2021. "Seasonal Performance Evaluation of Pavement Base Using Recycled Materials," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    2. Paolo Lazzeroni & Brunella Caroleo & Maurizio Arnone & Cristiana Botta, 2021. "A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study," Energies, MDPI, vol. 14(20), pages 1-18, October.
    3. Eldar Yeskuatov & Sook-Ling Chua & Lee Kien Foo, 2022. "Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    4. Andrew Ogolla Egesa & Maria Teresa Davidson & Héctor E. Pérez & Kevin Begcy, 2024. "Biochemical and Physical Screening Using Optical Oxygen-Sensing and Multispectral Imaging in Sea Oats Seeds," Agriculture, MDPI, vol. 14(6), pages 1-16, May.
    5. Qi Chu & Guang Bao & Jiayu Sun, 2022. "Progress and Prospects of Destination Image Research in the Last Decade," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    6. Israr Ullah & Bilal Aslam & Syed Hassan Iqbal Ahmad Shah & Aqil Tariq & Shujing Qin & Muhammad Majeed & Hans-Balder Havenith, 2022. "An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping," Land, MDPI, vol. 11(8), pages 1-20, August.
    7. Mariusz Woszczyński & Joanna Rogala-Rojek & Krzysztof Stankiewicz, 2022. "Advancement of the Monitoring System for Arch Support Geometry and Loads," Energies, MDPI, vol. 15(6), pages 1-21, March.
    8. Gang Zhou & Manyi Cui & Junhong Wan & Shiqiang Zhang, 2021. "A Review on Snowmelt Models: Progress and Prospect," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    9. Yan Yang & Chunfa Sha & Wencheng Su & Edwin Kofi Nyefrer Donkor, 2022. "Research on Online Destination Image of Zhenjiang Section of the Grand Canal Based on Network Content Analysis," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    10. Xiangyong Ni & Kangkang Duan, 2022. "Machine Learning-Based Models for Shear Strength Prediction of UHPFRC Beams," Mathematics, MDPI, vol. 10(16), pages 1-26, August.
    11. Muhammad Majeed & Aqil Tariq & Muhammad Mushahid Anwar & Arshad Mahmood Khan & Fahim Arshad & Faisal Mumtaz & Muhammad Farhan & Lili Zhang & Aroosa Zafar & Marjan Aziz & Sanaullah Abbasi & Ghani Rahma, 2021. "Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data," Land, MDPI, vol. 10(10), pages 1-17, September.
    12. You-Hyun Park & Sung-Hwa Kim & Yoon-Young Choi, 2021. "Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms," IJERPH, MDPI, vol. 18(16), pages 1-11, August.
    13. Anders Krogh Mortensen & René Gislum & Johannes Ravn Jørgensen & Birte Boelt, 2021. "The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research," Agriculture, MDPI, vol. 11(4), pages 1-18, April.
    14. Vikkram Singh & Joshua Chobotaru, 2022. "Digital Divide: Barriers to Accessing Online Government Services in Canada," Administrative Sciences, MDPI, vol. 12(3), pages 1-12, September.
    15. Jingfang Liu & Mengshi Shi & Huihong Jiang, 2022. "Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion," IJERPH, MDPI, vol. 19(13), pages 1-13, July.
    16. Yingfan Zhang & Tingting Fu & Xueyao Chen & Hancheng Guo & Hongyi Li & Bifeng Hu, 2022. "Modeling Cadmium Contents in a Soil–Rice System and Identifying Potential Controls," Land, MDPI, vol. 11(5), pages 1-13, April.
    17. Carlos Henrique Queiroz Rego & Fabiano França-Silva & Francisco Guilhien Gomes-Junior & Maria Heloisa Duarte de Moraes & André Dantas de Medeiros & Clíssia Barboza da Silva, 2020. "Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea," Agriculture, MDPI, vol. 10(8), pages 1-12, August.
    18. Liuchang Xu & Jie Wang & Dayu Xu & Liang Xu, 2022. "Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization," IJERPH, MDPI, vol. 19(1), pages 1-12, January.
    19. Qinglin Wu & Meidie Pan & Shikai Zhang & Dongpeng Sun & Yang Yang & Dong Chen & David A. Weitz & Xiang Gao, 2022. "Research Progress in High-Throughput Screening of CO 2 Reduction Catalysts," Energies, MDPI, vol. 15(18), pages 1-18, September.
    20. Liang Xu & Yanyang Luo & Xin Wen & Zaoyi Sun & Chiju Chao & Tianshu Xia & Liuchang Xu, 2022. "Human Personality Is Associated with Geographical Environment in Mainland China," IJERPH, MDPI, vol. 19(17), pages 1-13, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1274-:d:703029. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.