胚乳
高光谱成像
人工智能
模式识别(心理学)
子空间拓扑
特征(语言学)
计算机科学
集成学习
特征选择
数学
植物
生物
语言学
哲学
作者
Wei Zhao,Xueni Zhao,Bin Luo,Weiwei Bai,Kai Kang,Peichen Hou,Han Zhang
标识
DOI:10.1016/j.jfca.2023.105398
摘要
Differences in wheat endosperm structure contribute to differences in wheat flour texture and directly affect aspects such as flour quality, processing, and use. Therefore, the accurate classification of wheat based on endosperm texture is of immense practical interest. In this study, hyperspectral imaging technology (400–1000 nm) was combined with ensemble learning to classify wheat with different endosperm textures using spectral and shape features. Two feature extraction algorithms, competitive adaptive reweighted sampling and successive projection algorithm, were used to extract feature wavelengths. Furthermore, unknown characteristic data (new varieties of wheat) were fed into the model for classification. The results showed that feature fusion can markedly improve classification accuracy. The full-wavelength, subspace-based ensemble learning model based on the fusion of spectral and shape features had the best performance, and its classification accuracy reached 92.10%. In addition, the accuracy of all models for predicting new varieties decreased. However, the subspace-based ensemble learning model showed the best performance for identifying new wheat varieties with 88.03% accuracy. Thus, ensemble learning effectively classified both multiple known and new varieties of wheat with different endosperm textures. These results and this technology can help farmers and food manufacturers optimize their crop selection and processing strategies.
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