Hyperspectral Imaging Technology-based Early Diagnosis of a Serious <i>Agaricus Bisporus</i> Disease

CHEN Zihan; HUANG Liang; WEN Zhiqiang; WANG Die; WANG Shengnan; LIAO Xiaoling; WEI Xuan

doi:10.19303/j.issn.1008-0384.2021.11.015

Volume 36 Issue 11

Nov. 2021

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Article Navigation > Fujian Journal of Agricultural Sciences > 2021 > 36(11): 1365-1372

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

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

doi: 10.19303/j.issn.1008-0384.2021.11.015

1.
College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian　350002, China
2.
Fujian College of Water Conservancy and Electric Power, Yong’an, Fujian　366000, China
3.
College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian　350002, China
4.
College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian　350002, China

Received Date: 2021-04-16
Rev Recd Date: 2021-10-25

Available Online: 2021-12-30

Publish Date: 2021-11-28

Abstract

Abstract

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 1^st 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.
- Agaricus bisporus,
- hyperspectral imaging technology,
- early disease detection

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References(19)

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