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Rapid Identification of Choy Sum Seeds Infected with Penicillium decumbens Based on Hyperspectral Imaging and Stacking Ensemble Learning

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Abstract

A rapid and effective method for detecting fungal infection in choy sum seeds is necessary to ensure good yield. In this study, 127 spectra of healthy choy sum seeds and 1479 spectra of seeds of choy sum infected with Penicillium decumbens (P. decumbens) were collected using a laboratory-built hyperspectral imaging system. The imbalanced distribution of samples was improved using the synthetic minority over-sampling technique (SMOTE) algorithm. Nine classifiers were used as base classifiers; discriminant analysis was selected as the meta-learner to build the stacking ensemble learning model. The synergy interval partial least square (siPLS) algorithm was used to filter characteristic wavelengths. The SMOTE-siPLS-stacking model was developed using two wavelength ranges (460.96–516.33 nm and 696.61–753.55 nm) as input, achieving accuracy, and F1-score of 99.79% and 99.89%, respectively. The results showed that hyperspectral imaging combined with the SMOTE-siPLS-stacking model is a feasible method to detect P. decumbens.

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Funding

This work was supported by the Guangzhou Science and Technology Project (grant number 202103000095), the National Natural Science Foundation of China (grant number 61975069), the Key-Area Research and Development Program of Guangdong Province (grant number 2020B090922006), the Free Exploration Project of Special Research Funds for the Central Public-Interest Scientific Institution (grant number PM-zx703-202112-338), and Guangdong Provincial Special Project for Rural Revitalization Strategy at the Provincial Level(YCN [2022] No. 92).

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Author notes

  1. Baiheng Xie and Bijuan Chen contributed equally to this work.

Authors and Affiliations

  1. Department of Optoelectronic Engineering, Jinan University, Guangzhou, 510632, Guangdong, China

    Baiheng Xie, Jinfang Ma, Jiaze Chen, Yongxin Zhou, Xueqin Han & Furong Huang

  2. Guangdong Institute of Modern Agricultural Equipment, Guangzhou, 510640, Guangdong, China

    Bijuan Chen

  3. Guangdong Hongke Agricultural Machinery R&D Co., Ltd, Guangzhou, 510555, Guangdong, China

    Zheng Xiong

  4. School of Applied Biology, Shenzhen Institute of Technology, Shenzhen, 518045, Guangdong, China

    Zhanwang Yu

Authors
  1. Baiheng Xie
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  4. Jiaze Chen
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  9. Furong Huang
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Corresponding authors

Correspondence to Zhanwang Yu or Furong Huang.

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Conflict of Interest

Baiheng Xie declares that he has no conflict of interest. Bijuan Chen declares that he has no conflict of interest. Jinfang Ma declares that he has no conflict of interest. Jiaze Chen declares that he has no conflict of interest. Yongxin Zhou declares that he has no conflict of interest. Xueqin Han declares that he has no conflict of interest. Zheng Xiong declares that he has no conflict of interest. Zhanwang Yu declares that he has no conflict of interest. Furong Huang declares that he has no conflict of interest.

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Highlights

•Combined with hyperspectral imaging and machine vision methods, the diagnosis of Penicillium infection in seeds was realized.

•The stacking ensemble learning model showed excellent performance in the imbalanced classification of seeds.

•The accuracy of the diagnostic model with stacking structure was between 99.17% and 100% for the test data.

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Xie, B., Chen, B., Ma, J. et al. Rapid Identification of Choy Sum Seeds Infected with Penicillium decumbens Based on Hyperspectral Imaging and Stacking Ensemble Learning. Food Anal. Methods 17, 416–425 (2024). https://doi.org/10.1007/s12161-024-02574-0

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