Cotton Science

Estimation of chlorophyll content in cotton canopy using UAV multispectral imagery and machine learning algorithms

Zhao Xin, Li Zhaoyang, Wang Hongbo, Liu Jiangfan, Jiang Wenge, Zhao Zeyi, Wang Xingpeng, Gao Yang

Cotton Science, 2024, 36(1): 1-13. DOI: 10.11963/cs20230026

波段名称 Band name	简称 Abbreviation	中心波长 Center wavelength/nm	波段宽度 Band width/nm
近红外1 Near infrared 1	NIR 1	800	80
蓝光 Blue band	B	490	80
绿光 Green band	G	550	70
红光 Red band	R	680	80
红边 Red edge band	RE	720	100
近红外2 Near infrared 2	NIR 2	900	140

Table 2 Multispectral camera sensor parameters

Other figure/table from this article

Fig. 1 Overview map of the experimental area
Table 1 Basic soil nutrient content of experimental field
Table 3 Calculation formula for vegetation index
Table 4 Statistical description of chlorophyll content in cotton canopy leaves
Fig. 2 The correlation coefficient between vegetation index and chlorophyll content
Table 5 Simple linear model of vegetation index and SPAD and the model validation
Fig. 3 Distribution of measured cholorophyll content and the predicted chlorophyll content value from machine learning regression model A-F are methods based on LASSO regression, RR, PLSR, KNNR, RFR, and SVR, respectively.
Table 6 Estimation results of machine learning regression
Fig. 4 Inversion mapping of cotton canopy chlorophyll content using the RFR model