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Related Experiment Video

Updated: Sep 14, 2025

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

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Efficient wheat variety identification using Raman hyperspectral imaging in combination with deep learning.

Yaoyao Fan¹, Zheli Wang², Xueying Yao²

¹College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|July 22, 2025

Summary

This study introduces an efficient deep learning method for identifying wheat varieties using Raman hyperspectral imaging. The approach enhances accuracy and interpretability in wheat classification for agriculture and food safety.

Keywords:

Attention mechanism Chemical peaks selection Raman hyperspectral imaging Segment anything model Wheat variety identification

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Area of Science:

Agricultural Science
Spectroscopy
Machine Learning

Background:

Wheat varietal differences impact food processing, nutrition, and productivity.
Traditional wheat identification methods are inefficient and subjective.
Existing spectral techniques face challenges with complex preprocessing and limited interpretability.

Purpose of the Study:

To develop an efficient and interpretable method for wheat variety identification.
To overcome limitations of traditional and existing spectral identification techniques.
To integrate Raman hyperspectral imaging with deep learning for improved wheat classification.

Main Methods:

Developed a segmentation framework (One-Target Hyperspectral Image Segmentation and Extraction based on the Segment Anything Model) for efficient region extraction from wheat grains.
Selected Raman characteristic peaks using chemical prior knowledge to enhance interpretability.
Designed a Raman Spectral Attention Network with multiscale feature extraction and a Transformer module for improved modeling.

Main Results:

The segmentation framework significantly improved preprocessing efficiency.
The Raman Spectral Attention Network achieved up to 99% accuracy in classifying eight wheat varieties.
The integrated approach demonstrated enhanced reliability, interpretability, and efficiency.

Conclusions:

The study presents a robust solution for wheat variety identification using Raman hyperspectral imaging and deep learning.
This method offers promising applications in food quality assessment, precision agriculture, and food safety.
The approach enhances the efficiency and interpretability of spectral techniques in agricultural applications.