Monitoring the chlorophyll fluorescence parameters in rice under flooding and waterlogging stress based on remote sensing
- Resource Type
- Conference
- Authors
- Gu, Xiaohe; Xu, Peng; Qiu, He; Feng, Haikuan
- Source
- 2014 World Automation Congress (WAC) World Automation Congress (WAC), 2014. :848-854 Aug, 2014
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Physiology
Agriculture
Fluorescence
Monitoring
Filling
Stress
Frequency modulation
rice
flood and waterlogging stress
chlorophyll fluorescence parameters
neural network
- Language
- ISSN
- 2154-4824
Flood and waterlog disaster is one of the most serious catastrophes for rice in China. Timely and accurately monitoring waterlogging damage can provide quantitative damage assessment and support for after-flood field management. Chlorophyll fluorescence (CF) is directly related to the waterlogging stress. This paper aims to establish models to monitor the change of chlorophyll fluorescence parameters (FPs) at different growth stages under waterlogging stress based on hyperspectral data. Waterlogging stress was simulated in experimental environment. Back Propagation Neural Network (BPNN) model were proposed by analyzing the relationship between chlorophyll fluorescence parameters (FPs) and spectra absorption feature parameters, which were extracted from continuum removal spectra (550nm–750nm) to represent absorption features. The experimental results indicated that absorption feature parameters and BPNN can improve the estimation accuracy of FPs under flooding and waterlogging stress.