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Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns

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  • Cuiling Li

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Nongxin (Nanjing) Smart Agriculture Research Institute Co., Ltd., Nanjing 211800, China)

  • Xiu Wang

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing 100097, China)

  • Liping Chen

    (National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing 100097, China)

  • Xueguan Zhao

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing 100097, China)

  • Yang Li

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Mingzhou Chen

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Haowei Liu

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Changyuan Zhai

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Nongxin (Nanjing) Smart Agriculture Research Institute Co., Ltd., Nanjing 211800, China)

Abstract

This study adopted hyperspectral imaging technology combined with machine learning to detect the disease severity of stem blight through the canopy of asparagus mother stem. Several regions of interest were selected from each hyperspectral image, and the reflection spectra of the regions of interest were extracted. There were 503 sets of hyperspectral data in the training set and 167 sets of hyperspectral data in the test set. The data were preprocessed using various methods and the dimension was reduced using PCA. K−nearest neighbours (KNN), decision tree (DT), BP neural network (BPNN), and extreme learning machine (ELM) were used to establish a classification model of asparagus stem blight. The optimal model depended on the preprocessing methods used. When modeling was based on the ELM method, the disease grade discrimination effect of the FD−MSC−ELM model was the best with an accuracy (ACC) of 1.000, a precision (PREC) of 1.000, a recall (REC) of 1.000, an F1-score (F1S) of 1.000, and a norm of the absolute error (NAE) of 0.000, respectively; when the modeling was based on the BPNN method, the discrimination effect of the FD−SNV−BPNN model was the best with an ACC of 0.976, a PREC of 0.975, a REC of 0.978, a F1S of 0.976, and a mean square error (MSE) of 0.072, respectively. The results showed that hyperspectral imaging of the asparagus mother stem canopy combined with machine learning methods could be used to grade and detect stem blight in asparagus mother stems.

Suggested Citation

  • Cuiling Li & Xiu Wang & Liping Chen & Xueguan Zhao & Yang Li & Mingzhou Chen & Haowei Liu & Changyuan Zhai, 2023. "Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns," Agriculture, MDPI, vol. 13(9), pages 1-26, August.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1673-:d:1224222
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    References listed on IDEAS

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    1. Zimei Zhang & Jianwei Xiao & Wenjie Wang & Magdalena Zielinska & Shanyu Wang & Ziliang Liu & Zhian Zheng, 2024. "Automated Grading of Angelica sinensis Using Computer Vision and Machine Learning Techniques," Agriculture, MDPI, vol. 14(3), pages 1-21, March.

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