End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses
文献类型： 外文期刊
作者： Zhang, Chu 1 ; Zhou, Lei 2 ; Xiao, Qinlin 3 ; Bai, Xiulin 3 ; Wu, Baohua 3 ; Wu, Na 3 ; Zhao, Yiying 5 ; Wang, Junmin 6 ; Feng, Lei 3 ;
作者机构： 1.Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China
2.Nanjing Forestry Univ, Coll Mech & Elect Engn, Nanjing, Peoples R China
3.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
4.Minist Agr & Rural Affairs, Key Lab Spect Sensing, Hangzhou 310058, Peoples R China
5.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Peoples R China
6.Zhejiang Acad Agr Sci, Inst Crop Sci & Nucl Technol Utilizat, Hangzhou 310021, Peoples R China
期刊名称：PLANT PHENOMICS （ 影响因子：6.961； 五年影响因子：6.961 ）
ISSN： 2643-6515
年卷期： 2022 年 2022 卷
页码：
收录情况： SCI
摘要： Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.
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