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- Title
Design and Test of Chlorophyll Fluorescence Image Acquisition System for Greenhouse Plant.
- Authors
Xue LI; Yilu YANG; Xiaochan WANG; Yu ZHANG
- Abstract
To achieve the chlorophyll fluorescence image acquisition system for plants grown in greenhouse and accurately predict the chlorophyll fluorescence kinetic parameters, this paper built a prototype plant chlorophyll fluorescence image acquisition system by: i) utilizing computer machine vision technology to test the RGB color component, HSV indexes and the GRAY value of the marked leaves, ii) then modelling these image characteristics with chlorophyll fluorescence kinetics parameters via a) artificial neural network (ANN), b) support vector machine (SVM) and c) partial least squares regression (PLSR) methods. We analyzed and compared the prediction accuracy of the three models to fluorescence kinetic parameters, respectively, with different inputs of RGB with GRAY and HSV with GRAY. Our results showed that the three models could accurately predict the Y (Ⅱ), ETR, qL, NPQ and Fv/Fm parameters, and with input of RGB and GRAY, the prediction efficiency of the three models is generally superior to that with input of HSV and GRAY. It was the SVM model with input of RGB and GRAY that had the best prediction efficiency, with the correlation coefficient R of the Y (Ⅱ), ETR, qL, NPQ and Fv/Fm between predicted values and the real values were: 0.935, 0.941, 0.994, 0.987 and 0.941, the mean square deviation RMSE were: 0.013, 0.100, 0.036, 0.023 and 0.025. Our study indicated that the chlorophyll fluoresces image acquisition system could quickly and efficiently obtain and predict the plant leaf chlorophyll fluorescence image information and the chlorophyll fluorescence kinetic parameters as well, resulting in the monitoring and forecasting of plant health, plant environmental adaptation and plant photosynthetic performance.
- Subjects
CHLOROPHYLL spectra; FORECASTING
- Publication
International Journal of Simulation: Systems, Science & Technology, 2016, Vol 17, Issue 40, p1
- ISSN
1473-8031
- Publication type
Article
- DOI
10.5013/IJSSST.a.17.40.24