Building of JUNCAO Leaf Chlorophyll Spectrum Model

BAI Nini; LIU Fengshan; ZHANG Houxi; CAI Yangxing; BU Jianchao; PENG Shimei; LIN Dongmei

doi:10.19303/j.issn.1008-0384.2023.02.011

Volume 38 Issue 2

Feb. 2023

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Article Navigation > Fujian Journal of Agricultural Sciences > 2023 > 38(2): 210-219

BAI N N, LIU F S, ZHANG H X, et al. Building of JUNCAO Leaf Chlorophyll Spectrum Model [J]. Fujian Journal of Agricultural Sciences，2023，38（2）：210−219 doi: 10.19303/j.issn.1008-0384.2023.02.011

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BAI N N, LIU F S, ZHANG H X, et al. Building of JUNCAO Leaf Chlorophyll Spectrum Model [J]. Fujian Journal of Agricultural Sciences，2023，38（2）：210−219 doi: 10.19303/j.issn.1008-0384.2023.02.011

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Building of JUNCAO Leaf Chlorophyll Spectrum Model

doi: 10.19303/j.issn.1008-0384.2023.02.011

BAI Nini^{1, 2
,},
LIU Fengshan^{2, 3
,
,},
ZHANG Houxi¹,
CAI Yangxing²,
BU Jianchao^{2, 3},
PENG Shimei^{2, 3},
LIN Dongmei²

1.
College of Forestry，Fujian Agriculture and Forestry University，Fuzhou，Fujian　350002, China
2.
National Engineering Research Center of JUNCAO Technology, Fujian Agriculture and Forestry University, Fuzhou, Fujian　350002, China
3.
College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian　350002, China

Received Date: 2022-07-24
Rev Recd Date: 2022-12-10

Available Online: 2023-03-28

Publish Date: 2023-02-28

Abstract

Abstract

Objective A model based on the leaf chlorophyll was established to provide a rapid, convenient, and non-invasive means for evaluating and predicting the quality of varieties of JUNCAOs. Methods Using a multispectral camera, spectra of leaf chlorophyll were obtained from different JUNCAOs. NDSI and RSI vegetation indices were established according to the RGB and HSV color space systems. A chlorophyll meter was used to obtain SPAD readings on JUNCAO leaves . Four regression algorithms were applied to build the model to correlate the spectral and SPAD measurements. Results A significant difference on SPAD was observed between healthy and dry or dead leaves, but not between different JUNCAO. A significant correlation was found between the leaf SPAD and spectral reflection, especially, the red band of RGB color showing a coefficient of −0.79. The high sensitivity of the red band in distinguishing healthy from withering leaves allowed an accurate and stable measurements by the color systems that ranked as: 5-channel RGB＞HSV＞3-channel RGB. Among the 4 inversion methods, the rank was: random forest machine learning＞support vector machine learning＞stepwise linear regression＞univariate linear statistical. And the best fitting function in predicting SPAD was the RGB-NDSI-RF type, which had a R² of 0.95 vs. 3.04 for RMSE and 0.19 for MRE and validation R² of 0.75 vs. 8.26 for RMSE and 0.76 for MRE. Conclusion As JUNCAO is becoming increasingly popular for wide applications, a reliable, rapid, non-invasive method to determine the plant quality was much desired. The current leaf chlorophyll spectra-based RGB-NDSI-RF model built by the random forest machine learning method demonstrated an applicability for evaluating varieties of JUNCAO.
- JUNCAO,
- multispectral,
- machine learning,
- HSV color system

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References(34)

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