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Defective wheat kernel classification using dual-range hyperspectral imaging and an interpretable spectral-spatial

Dianyang Sun¹, Weijie Lan¹, Kang Tu¹

¹College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|July 15, 2026

Summary

A new spectral-spatial fusion convolutional neural network (SSFCNN) accurately identifies defective wheat kernels using hyperspectral imaging. This advanced method improves quality control for wheat screening.

Keywords:

Attention mechanism Defective wheat kernels Dual-range hyperspectral imaging Model interpretability Spectral-spatial fusion

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Area of Science:

Agricultural Science
Computer Vision
Spectroscopy

Background:

Accurate detection of defective wheat kernels is crucial for quality control.
Existing methods struggle with subtle spectral and spatial differences, limiting reliability.
Hyperspectral imaging offers rich spectral and spatial data for kernel analysis.

Purpose of the Study:

To develop a novel spectral-spatial fusion convolutional neural network (SSFCNN) for classifying defective wheat kernels.
To integrate complementary spectral and spatial information for enhanced classification accuracy.
To provide a validated framework for intelligent wheat grading and online quality control.

Main Methods:

A spectral-spatial fusion convolutional neural network (SSFCNN) was designed for wheat kernel classification.
Attention mechanisms (SE, SA, ECA) were integrated for spectral channel recalibration and feature enhancement.
The SSFCNN performance was compared against Support Vector Machine (SVM) and conventional Convolutional Neural Network (CNN) methods.

Main Results:

The SSFCNN achieved high accuracies: 96.48% (Vis-NIR) and 95.61% (SWIR).
The SSFCNN significantly outperformed SVM and conventional CNN approaches in precision, recall, specificity, and F1-score.
Visualizations confirmed improved spatial coherence and decision reliability of the SSFCNN model.

Conclusions:

The developed SSFCNN provides a validated and interpretable method for hyperspectral screening of defective wheat kernels.
This approach offers a strong methodological basis for future intelligent grading and online quality control systems.
Further validation under real-world sorting conditions is recommended for practical implementation.