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Purpose <p>Bacterial blight poses a significant threat to red kidney bean growth, often leading to substantial yield losses. Real-time monitoring of this disease is crucial for effective prevention and control, ensuring optimal yield. This study proposes a hyperspectral-based approach to assess the the bacterial blight disease in red kidney beans.</p> Methods <p>In this study, canopy hyperspectral data from two experimental areas were collected, and five spectral preprocessing methods—multiple scattering correction (MSC), standard normal variation (SNV), first-order derivative (FD), second-order derivative (SD), and logarithmic transformation (LOG)—were applied to the raw spectra (R). Feature bands were extracted using a combination of the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA). Disease severity classification models were constructed using support vector machine (SVM) and partial least squares discriminant analysis (PLS-LDA), while estimation models were developed using support vector regression (SVR) and partial least squares regression (PLSR).</p> Results <p>Results demonstrated that FD preprocessing most effectively enhanced spectral features for bacterial blight detection, with sensitive bands optimally extracted using the CARS-SPA algorithm. The bands centered at 750&#xa0;nm and 945&#xa0;nm were identified as the most sensitive across all preprocessing methods. For condition index estimation, the PLSR model performed best, with FD preprocessing achieving R² values of 0.883 (modeling set) and 0.863 (validation set), and RMSE values of 0.072 and 0.083, respectively.</p> Conclusions <p>These findings highlight the potential of hyperspectral technology, combined with feature extraction and machine learning algorithms, for efficient and accurate detection of bacterial blight in red kidney beans. This study provides a methodological and technical framework for monitoring other crops and diseases.</p>

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Hyperspectral assessment of bacterial blight disease in red kidney beans by feature selection and machine learning algorithms

  • 期刊论文

  • 发表日期：

    2025年6月21日

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  • Xingxing Qiao,
  • Jiachen Wang,
  • Binghan Jing,
  • Xin Zhang,
  • Yaoxuan Jia,
  • Kunming Huang,
  • Wude Yang,
  • Meichen Feng,
  • Zhen Zhang,
  • Yu Zhao,
  • Fahad Shafiq,
  • Lujie Xiao,
  • Xiaoyan Song,
  • Meijun Zhang,
  • Chao Wang

摘要

Purpose

Bacterial blight poses a significant threat to red kidney bean growth, often leading to substantial yield losses. Real-time monitoring of this disease is crucial for effective prevention and control, ensuring optimal yield. This study proposes a hyperspectral-based approach to assess the the bacterial blight disease in red kidney beans.

Methods

In this study, canopy hyperspectral data from two experimental areas were collected, and five spectral preprocessing methods—multiple scattering correction (MSC), standard normal variation (SNV), first-order derivative (FD), second-order derivative (SD), and logarithmic transformation (LOG)—were applied to the raw spectra (R). Feature bands were extracted using a combination of the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA). Disease severity classification models were constructed using support vector machine (SVM) and partial least squares discriminant analysis (PLS-LDA), while estimation models were developed using support vector regression (SVR) and partial least squares regression (PLSR).

Results

Results demonstrated that FD preprocessing most effectively enhanced spectral features for bacterial blight detection, with sensitive bands optimally extracted using the CARS-SPA algorithm. The bands centered at 750 nm and 945 nm were identified as the most sensitive across all preprocessing methods. For condition index estimation, the PLSR model performed best, with FD preprocessing achieving R² values of 0.883 (modeling set) and 0.863 (validation set), and RMSE values of 0.072 and 0.083, respectively.

Conclusions

These findings highlight the potential of hyperspectral technology, combined with feature extraction and machine learning algorithms, for efficient and accurate detection of bacterial blight in red kidney beans. This study provides a methodological and technical framework for monitoring other crops and diseases.