Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of cr...

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Bibliographic Details
Published in:Agronomy (Basel) Vol. 14; no. 5; p. 941
Main Authors: Zou, Chaojun, Zhu, Xinghui, Wang, Fang, Wu, Jinran, Wang, You-Gan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2024
Subjects:
Agriculture
agronomy
Algorithms
Calibration
Chlorophyll
Classification
Color
color classification
Coloration
computer vision
Depth perception
Diagnosis
Digital agriculture
Fatty acids
Feature extraction
Fourier transforms
hyperspectral imagery
hyperspectral reflectance
Information technology
Machine learning
Machine vision
Mathematical models
Methods
Proteins
Quality control
Rape plants
Rapeseed
Reflectance
seed coat
seed color
Seeds
Software
Spectral reflectance
Spectrum analysis
Statistical analysis
Statistical models
Technology assessment
Traditional farming
vector-square distance
Visibility
visibility graph algorithm
Vision systems
Visual perception
Germany
ISSN:2073-4395, 2073-4395
Online Access:Get full text
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Summary:Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional agriculture. This work applies these techniques to address the color classification of rapeseed, which is of great significance in the field of rapeseed growth diagnosis research. To bridge the gap between machine vision and hyperspectral technology, a framework is developed that includes seed color calibration, spectral feature extraction and fusion, and the recognition modeling of three seed colors using four machine learning methods. Three categories of rapeseed coat colors are calibrated based on visual perception and vector-square distance methods. A fast-weighted visibility graph method is employed to map the spectral reflectance sequences to complex networks, and five global network attributes are extracted to fuse the full-band reflectance as model input. The experimental results demonstrate that the classification recognition rate of the fused feature reaches 0.943 under the XGBoost model, confirming the effectiveness of the network features as a complement to the spectral reflectance. The high recognition accuracy and simple operation process of the framework support the further application of hyperspectral technology to analyze the quality of rapeseed.
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ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy14050941