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

土层深度 Depth of soil/cm	全氮含量 Total nitrogen content/(mg·g^-1)	全磷含量 Total phosphorus content/(mg·g^-1)	碱解氮含量 Alkali hydrolyzed nitrogen content/(mg·kg^-1)	速效钾含量 Available potassium content/(mg·kg^-1)	有机质含量 Organic matter content/(g·kg^-1)
0~20	0.39	0.53	42.46	197.25	5.75
20~40	0.25	0.48	25.08	192.57	4.71
40~60	0.17	0.42	10.15	139.71	3.49
60~80	0.12	0.39	6.30	90.19	2.37

Table 1 Basic soil nutrient content of experimental field

Other figure/table from this article

Fig. 1 Overview map of the experimental area
Table 2 Multispectral camera sensor parameters
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