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Hyperspectral Estimation Methods for Chlorophyll Content of Apple Based on Random Forest

Abstract : Chlorophyll content is a good indicator of fruit tree nutrition stress, photosynthesis, and another physiological state. 10 vegetation indices were selected and used as input variables of RF model, the number of input variables was gradually increased from 1 to 10. The modeling accuracy of 10 RF models with vegetation indices was compared. Finally, the accuracy of 2 estimation models, the RF model with the original spectrum, and the RF optimal model with vegetation indices were established and compared. The result, For modeling accuracy of 2 models, the R2 of four models are 0.527 and 0.609, and the RMSE of 2 models are 8.728 and 7.930 μg/cm2, respectively. For validation accuracy of 2 models, R2 of 2models is 0.411 and 0.843, RMSE is 14.455 and 11.034 μg/cm2, respectively. The result showed, (1) the accuracy of RF model with vegetation indices is higher than the other model. (2) The RF model with vegetation indices can estimate the chlorophyll content of apple leaves more accurately and it had the potential for estimating chlorophyll content of apple leaf. And it provides a new method for the accurate estimation of chlorophyll of apple leaves.
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Submitted on : Friday, April 26, 2019 - 10:00:21 AM
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Haojie Pei, Changchun Li, Haikuan Feng, Guijun Yang, Mingxing Liu, et al.. Hyperspectral Estimation Methods for Chlorophyll Content of Apple Based on Random Forest. 11th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Aug 2017, Jilin, China. pp.194-207, ⟨10.1007/978-3-030-06179-1_20⟩. ⟨hal-02111543⟩



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