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Related Experiment Video

Updated: Mar 29, 2026

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

Early Detection and Classification of Gibberella Zeae Contamination in Maize Kernels Using SWIR Hyperspectral Imaging

Kaili Liu^1,2, Shiling Li¹, Wenbo Shi¹

¹College of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo 255049, China.

Sensors (Basel, Switzerland)

|March 28, 2026

Summary

Short-wave infrared (SWIR) hyperspectral imaging effectively detects early fungal contamination in maize kernels. This non-destructive method combined with machine learning ensures food safety and quality monitoring during storage and transport.

Keywords:

Gibberella zeae classification model feature wavelength selection hyperspectral imaging maize kernel spectral preprocessing

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Author Spotlight: Investigating Fungal Pathogenicity Mechanisms in Maize

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Published on: September 15, 2023

Area of Science:

Agricultural Science
Food Science
Spectroscopy

Background:

Early fungal contamination in maize is visually undetectable, posing significant risks to quality and safety during storage and transport.
Short-wave infrared (SWIR) hyperspectral imaging provides a rapid, non-destructive method to analyze chemical compositions related to water, proteins, and lipids in maize kernels.

Purpose of the Study:

To investigate the early detection and classification of *Gibberella zeae* contamination in maize kernels.
To evaluate the efficacy of SWIR hyperspectral imaging combined with machine learning algorithms for identifying fungal contamination at various stages.

Main Methods:

Maize kernels were artificially inoculated with *Gibberella zeae* and monitored over six contamination stages.
SWIR hyperspectral data were collected, followed by preprocessing (SNV, SD, MSC) and feature selection (SPA, CARS, UVE).
Various machine learning models (LDA, MLP, SVM, CNN, LSTM, CLT) were employed for binary and multiclass classification.

Main Results:

The best binary classification (healthy vs. contaminated) achieved 100% accuracy using SNV preprocessing and an MLP model.
For multiclass classification (six contamination stages), the SD-preprocessed LDA model yielded a test accuracy of 92.56%.

Conclusions:

SWIR hyperspectral imaging, coupled with optimized preprocessing, feature selection, and machine learning, is a powerful tool for non-destructive, early-stage detection of fungal contamination in maize.
This technology offers significant potential for enhancing food safety and quality monitoring systems in the agricultural industry.