Machine learning methods for crop chlorophyll variable retrieval
Abstract
Abstract: Hyperspectral remote sensing technology improves the retrieval ability of chlorophyll content in crops. Â The machine learning method has been developed and applied to crop phenotyping information inversion. Â This study combined radiative transfer model (PROSPECT-4) and Gauss Process Regression algorithm (GPR) to retrieval crop leaf chlorophyll content. Â The test was conducted in the eastern city of Shenyang, Liaoning Province, China with a japonica rice. Â This paper describes (1) The PROSPECT-4 model was analyzed by GSA tool, and the sensitivity band range of crop chlorophyll was at 400-750 nm. Â (2) The chlorophyll content model was established with great accuracy (R2=0.8638) that can predict the crop leaf chlorophyll content; (3) Theresults demonstrated that crop chlorophyll is inversion by PROSPECT model and machine learning algorithm. Â Therefore,crop chlorophyll content can be estimated by hyperspectral datathat may be used for cropgrowth management. Â This research can provide an efficient method to detect crop leaf chlorophyll content at the RTMS in the future.
Keywords: Gaussian Processes Regression, hyperspectral remote sensing, leaf chlorophyll, machine learning, PROSPECT
DOI: 10.33440/j.ijpaa.20180101.0006
Citation:Yu F H, Xu T Y, Chen C L, Du W, Feng S A.  Machine learning methods for crop chlorophyll variable retrieval.  Int J Precis Agric Aviat, 2018; 1(1): 32–36.
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