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Related Experiment Video

Updated: May 31, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network

Imran Said¹, Vasit Sagan^1,2,3, Kyle T Peterson⁴

¹Department of Computer Science, Saint Louis University, Saint Louis, MO 63104, USA.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

Hyperspectral imaging and machine learning accurately predict wheat seed protein. Convolutional Neural Networks (CNNs) show promise for automated protein estimation in wheat breeding programs.

Keywords:

3D CNN modeling attentive models hyperspectral imaging machine learning seed composition estimation

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Published on: August 6, 2018

Area of Science:

Agricultural Science
Spectroscopy
Machine Learning

Background:

Wheat seed protein concentration is a critical quality trait for global food security and animal feed.
Accurate and rapid methods for determining wheat protein content are essential for breeding programs and quality control.
Traditional methods for protein analysis are often time-consuming and destructive.

Purpose of the Study:

To develop and evaluate robust hyperspectral imaging methods for predicting wheat seed protein concentration.
To compare the performance of convolutional neural networks (CNNs) with traditional machine learning models for protein estimation.
To assess the utility of CNN classification for categorizing wheat protein levels for breeding applications.

Main Methods:

Bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions was employed.
Computer-vision-aided image co-registration was used to align VNIR and SWIR spectral data.
CNNs with attention mechanisms, Random Forest (RF), and Support Vector Machine (SVM) regression models were utilized for analysis.

Main Results:

The CNN with attention mechanisms achieved R² values of 0.70 (ventral) and 0.65 (dorsal) for protein content prediction.
The RF model outperformed CNNs for direct protein content prediction, reaching an R² of 0.77.
CNN classification effectively distinguished low, medium, and high protein concentrations with an R² of 0.82.

Conclusions:

Hyperspectral imaging combined with machine learning offers a powerful, non-destructive approach for wheat protein analysis.
CNNs demonstrate significant potential for automating wheat protein estimation, particularly for classification tasks in breeding.
These techniques can advance precision breeding, optimize seed sorting, and guide targeted agricultural inputs.