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

生育时期 Reproductive period	样本数量 Sample size	最大值Maximum value/(mg·L^-1)	最小值 Minimum value/(mg·L^-1)	平均值 Average value/(mg·L^-1)	标准差 Standard deviation/(mg·L^-1)	方差 Variance
花铃期 Flowering and boll- setting stage	70	23.28	13.75	18.30	2.01	4.07

Table 4 Statistical description of chlorophyll content in cotton canopy leaves

Other figure/table from this article

Fig. 1 Overview map of the experimental area
Table 1 Basic soil nutrient content of experimental field
Table 2 Multispectral camera sensor parameters
Table 3 Calculation formula for vegetation index
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