Complex Network-Based Data Classification Using Minimum Spanning Tree Metric and Optimization

Saire, Josimar Chire; Zhao, Liang

doi:10.1109/ijcnn54540.2023.10191004

Cited by 1 publication

(1 citation statement)

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“…We also put the fusion features consisting of the original reflectance and spectral color index in the same model. The result shows that the recognition accuracy is not better than using only the original spectral reflectance, which indicates that the spectral color index cannot form a benign complementarity for the original spectral reflectance, while the network features can do so [60]. One potential reason may lie in the design of VG algorithms, i.e., the VG can capture the difference in the reflectance among every band associated with their order, which compensates for the differential information in variable bands of the original spectral reflectance.…”

Section: Discussionmentioning

confidence: 98%

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

Zou,

Zhu,

Wang

et al. 2024

Agronomy

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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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Section: Discussionmentioning

confidence: 98%