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Related Experiment Video

Updated: Oct 30, 2025

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

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Published on: August 8, 2017

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model.

Weihua Liu¹, Shan Zeng², Guiju Wu³

¹School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces a new hyperspectral method using the LASSO logistic regression model (LLRM) for accurate rice seed purity identification. LLRM effectively selects key spectral features, significantly improving recognition accuracy compared to traditional methods.

Keywords:

LASSO logistic regression model grey-scale image hyperspectral imaging seed purity identification wavelength band selection

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Last Updated: Oct 30, 2025

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Area of Science:

Agricultural Science
Spectroscopy
Machine Learning

Background:

Hyperspectral technology offers simultaneous spectral and spatial information for seed purity identification.
Limitations include high costs and redundant data, necessitating advanced analytical methods.

Purpose of the Study:

To develop an efficient hyperspectral method for rice seed purity identification.
To integrate the sparse feature selection of LASSO with the classification power of LRM.
To evaluate the proposed LASSO logistic regression model (LLRM) for identifying feature wavelength bands and seed purity.

Main Methods:

Utilized hyperspectral imaging for data acquisition.
Applied the Least Absolute Shrinkage and Selection Operator (LASSO) for feature wavelength band selection.
Employed Logistic Regression Model (LRM) for seed purity classification.
Tested the integrated LLRM on four types of rice seeds across 13 adulteration cases.

Main Results:

LLRM achieved recognition accuracies ranging from 91.67% to 100%, with an average of 98.47%.
Full-band LRM showed lower average accuracy (89.63%), despite a similar range (71.60-100%).
LLRM demonstrated stable selection of feature wavelength bands and enhanced recognition accuracy.

Conclusions:

The LLRM method is feasible and effective for hyperspectral rice seed purity identification.
LLRM overcomes limitations of hyperspectral technology by reducing data redundancy and improving accuracy.
This approach holds promise for developing advanced seed quality assessment tools.