• 中文核心期刊
  • CSCD来源期刊
  • 中国科技核心期刊
  • CA、CABI、ZR收录期刊

Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review,        editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name
E-mail
Phone
Title
Content
Verification Code
Volume 36 Issue 11
Nov.  2021
Turn off MathJax
Article Contents
CHEN Z H, HUANG L, WEN Z Q, et al. Hyperspectral Imaging Technology-based Early Diagnosis of a Serious Agaricus Bisporus Disease [J]. Fujian Journal of Agricultural Sciences,2021,36(11):1365−1372 doi: 10.19303/j.issn.1008-0384.2021.11.015
Citation: CHEN Z H, HUANG L, WEN Z Q, et al. Hyperspectral Imaging Technology-based Early Diagnosis of a Serious Agaricus Bisporus Disease [J]. Fujian Journal of Agricultural Sciences,2021,36(11):1365−1372 doi: 10.19303/j.issn.1008-0384.2021.11.015

Hyperspectral Imaging Technology-based Early Diagnosis of a Serious Agaricus Bisporus Disease

doi: 10.19303/j.issn.1008-0384.2021.11.015
  • Received Date: 2021-04-16
  • Rev Recd Date: 2021-10-25
  • Available Online: 2021-12-30
  • Publish Date: 2021-11-28
  •   Objective   A nondestructive, effective method was developed based on the hyperspectral imaging technology for early diagnosis of the highly destructive wet bubble disease on Agaricus Bisporus caused by Mycogone perniciosa.   Method   Information on the full band (401-1046nm) visible/near-infrared hyperspectral images on 200 healthy and 200 infected A. bisporus specimens was collected. After a preprocess using Savitzky-Golay 1st order derivative, Savitzky-Golay smoothing, or multiple scattering correction (MSC) on the obtained information of the 360 full bands, accuracy of the methodology in separating the healthy from the infected samples was scrutinized by using the Random Forest (RF), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models.   Result  The 3 models yielded similar results and the MSC-SVM combination had the best detection effect with the identification accuracy on the test set increased from 85.02% to 92.21%, and on the prediction set, from 87.38% to 91.04%.   Conclusion   The MSC-SVM model appeared to significantly improve the identification accuracy using the full band. It provided a basis for the development of rapid, nondestructive diagnostic device on the devastating disease of A. bisporus at early stage which has been conventionally conducted by expert visual examination, PCR analysis on the internal transcribed spacer gene, or traditional Koch's postulation.
  • loading
  • [1]
    秦文韬, 王守现, 荣成博, 等. 我国食用菌病害发生与防控概况 [J]. 中国食用菌, 2020, 39(12):1−7.

    QIN W T, WANG S X, RONG C B, et al. Occurrence and management of edible fungus diseases in China [J]. Edible Fungi of China, 2020, 39(12): 1−7.(in Chinese)
    [2]
    谭琦, 王镭, 王永红, 等. 不同地区有害疣孢霉菌株生物学特性研究 [J]. 食用菌学报, 1996, 3(1):46−50.

    TAN Q, WANG L, WANG Y H, et al. A study on biological characteristics in isolates of Mycogone perniciosa magn [J]. Acta Edulis Fungi, 1996, 3(1): 46−50.(in Chinese)
    [3]
    YANG Y, SOSSAH F L, LI Z, et al. Genome-wide identification and analysis of chitinase GH18 gene family in Mycogone perniciosa [J]. Frontiers in Microbiology, 2021, 11: 596719. doi: 10.3389/fmicb.2020.596719
    [4]
    GEA F J, TELLO J C, NAVARRO M J. Efficacy and effects on yield of different fungicides for control of wet bubble disease of mushroom caused by the mycoparasite Mycogone perniciosa [J]. Crop Protection, 2010, 29(9): 1021−1025. doi: 10.1016/j.cropro.2010.06.006
    [5]
    PANG L, WANG J H, MEN S, et al. Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis [J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy, 2021, 245: 118888. doi: 10.1016/j.saa.2020.118888
    [6]
    SKONECZNY H, KUBIAK K, SPIRALSKI M, et al. Fire blight disease detection for apple trees: Hyperspectral analysis of healthy, infected and dry leaves. [J]. Remote Sensing, 2020, 12(13): 2101. doi: 10.3390/rs12132101
    [7]
    SOBEJANO-PAZ V, MIKKELSEN T N, BAUM A, et al. Hyperspectral and thermal sensing of stomatal conductance, transpiration, and photosynthesis for soybean and maize under drought [J]. Remote Sensing, 2020, 12(19): 3182. doi: 10.3390/rs12193182
    [8]
    PARRAG V, FELFÖLDI J, BARANYAI L, et al. Early detection of cobweb disease infection on Agaricus bisporus sporocarps using hyperspectral imaging[J]. Acta Alimentaria, 2014, 43(Supplement 1): 107−113.
    [9]
    GASTON E, FRÍAS J M, CULLEN P J, et al. Visible-near infrared hyperspectral imaging for the identification and discrimination of brown blotch disease on mushroom (Agaricus bisporus) caps [J]. Journal of Near Infrared Spectroscopy, 2010, 18(5): 341−353. doi: 10.1255/jnirs.894
    [10]
    LIN X H, XU J L, SUN D W. Investigation of moisture content uniformity of microwave-vacuum dried mushroom (Agaricus bisporus) by NIR hyperspectra limaging [J]. LWT-Food Science and Technology, 2019, 109: 108−117. doi: 10.1016/j.lwt.2019.03.034
    [11]
    TAGHIZADEH M, GOWEN A, WARD P, et al. Use of hyperspectral imaging for evaluation of the shelf-life of fresh white button mushrooms (Agaricus bisporus) stored in different packaging films [J]. Innovative Food Science & Emerging Technologies, 2010, 11(3): 423−431.
    [12]
    GOWEN A A, TAGHIZADEH M, O’DONNELL C P. Identification of mushrooms subjected to freeze damage using hyperspectral imaging [J]. Journal of Food Engineering, 2009, 93(1): 7−12. doi: 10.1016/j.jfoodeng.2008.12.021
    [13]
    张春兰, 徐济责, 柿岛真, 等. 双孢蘑菇疣孢霉病的发病过程及病原菌的核相研究 [J]. 微生物学报, 2017, 57(3):422−433.

    ZHANG C L, XU J Z, SHI D Z, et al. The development of Agaricus bisporus wet bubble disease and the nuclear phase of pathogen [J]. Acta Microbiologica Sinica, 2017, 57(3): 422−433.(in Chinese)
    [14]
    郭倩, 凌霞芬, 王志强, 等. 双孢蘑菇工厂化栽培过程中环境因子的调控 [J]. 食用菌学报, 2020, 9(3):38−41.

    GUO Q, LING X F, WANG Z Q, et al. The control of environmental factors in modern industrial cultivation ofAgaricus bisporus [J]. Acta Edulis Fungi, 2020, 9(3): 38−41.(in Chinese)
    [15]
    BELGIU M, DRĂGUŢ L. Random forest in remote sensing: A review of applications and future directions [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114: 24−31. doi: 10.1016/j.isprsjprs.2016.01.011
    [16]
    DALPONTE M, ØRKA H O, GOBAKKEN T, et al. Tree species classification in boreal forests with hyperspectral data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 2632−2645. doi: 10.1109/TGRS.2012.2216272
    [17]
    HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: A new learning scheme of feedforward neural networks [C]// 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541). Budapest, Hungary: IEEE, 2004: 985 − 990.
    [18]
    DING S F, XU X Z, NIE R. Extreme learning machine and its applications [J]. Neural Computing and Applications, 2014, 25(3/4): 549−556.
    [19]
    李鸿强. 基于高光谱分析的蔬菜品质检测方法研究[D]. 北京: 中国农业大学, 2019.

    LI H Q. Study on detection method of vegetable quality based on hyperspectral analysis[D]. Beijing: China Agricultural University, 2019. (in Chinese)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(2)

    Article Metrics

    Article views (659) PDF downloads(29) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return