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Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component

Jing Li1, Liang He1, Muhua Liu1

  • 1Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China.

Frontiers in Nutrition
|November 7, 2022
PubMed
Summary

Hyperspectral imaging accurately identifies navel orange diseases like canker and penicilliosis. This method significantly reduces detection time for an online system, improving quality control for citrus fruit.

Keywords:
ICA-GAcankerhyperspectral imagenavel orangepenicilliosis

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Area of Science:

  • Agricultural Science
  • Plant Pathology
  • Spectroscopy

Background:

  • Navel orange diseases, canker (pre-harvest) and penicilliosis (post-harvest), impact fruit quality and storage.
  • Accurate identification of these diseases is crucial for effective management and reducing economic losses.

Purpose of the Study:

  • To develop and validate a hyperspectral imaging technique for identifying navel orange surface, canker spots, penicillium spores, and hyphae.
  • To significantly reduce data processing time for an online detection system.

Main Methods:

  • Spherical correction was applied to hyperspectral images to improve light intensity.
  • Independent Component Analysis (ICA) and a genetic algorithm were used for dimension reduction and selecting six characteristic wavelengths.
  • A deep learning neural network model was established for pixel classification.

Main Results:

  • The hyperspectral imaging method successfully identified typical fruit surfaces, canker spots, penicillium spores, and hyphae.
  • Dimension reduction decreased testing time from 46.21 s to 1.26 s in the online detection system.
  • The deep learning model achieved accurate identification of the four surface pixel types.

Conclusions:

  • Hyperspectral imaging combined with dimension reduction and deep learning offers an efficient and accurate method for detecting navel orange diseases.
  • This technology has the potential to enhance quality control in the citrus industry by enabling rapid, non-destructive disease assessment.