A fast multi-source information fusion strategy based on deep learning for species identification of boletes
文献类型： 外文期刊
作者： Chen, Xiong 1 ; Li, Jieqing 3 ; Liu, Honggao 1 ; Wang, Yuanzhong 1 ;
作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China
2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China
3.Yunnan Agr Univ, Coll Resources & Environm, Kunming 650201, Yunnan, Peoples R China
4.Zhaotong Univ, Zhaotong 657000, Peoples R China
关键词： Boletes; Near-infrared; Two-dimensional correlation spectroscopy; Deep learning; Species identification
期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.831； 五年影响因子：4.073 ）
ISSN： 1386-1425
年卷期： 2022 年 274 卷
页码：
收录情况： SCI
摘要： Wild mushroom market is an important economic source of Yunnan province in China, and its wild mushroom resources are also valuable wealth in the world. This work will put forward a method of species identification and optimize the method in order to maintain the market order and protect the economic benefits of wild mushrooms. Here we establish deep learning (DL) models based on the two-dimensional correlation spectroscopy (2DCOS) images of near-infrared spectroscopy from boletes, and optimize the identification effect of the model. The results show that synchronous 2DCOS is the best method to establish DL model, and when the learning rate was 0.01, the epochs were 40, using stipes and caps data, the identification effect would be further improved. This method retains the complete information of the samples and can provide a fast and noninvasive method for identifying boletes species for market regulators. (C) 2022 Elsevier B.V. All rights reserved.
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