Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

Abstract

In order to improve the diagnosis accuracy of chlorophyll content in maize canopy, the remote sensing image of maize canopy with multiple growth stages was acquired by using an unmanned aerial vehicle (UAV) equipped with a spectral camera. The dynamic influencing factors of the canopy multispectral images of maize were removed by using different image segmentation methods. The chlorophyll content of maize in the field was diagnosed. The crop canopy spectral reflectance, coverage, and texture information are combined to discuss the different segmentation methods. A full-grown maize canopy chlorophyll content diagnostic model was created on the basis of the different segmentation methods. Results showed that different segmentation methods have variations in the extraction of maize canopy parameters. The wavelet segmentation method demonstrated better advantages than threshold and ExG index segmentation methods. This method segments the soil background, reduces the texture complexity of the image, and achieves satisfactory results. The maize canopy multispectral band reflectance and vegetation index were extracted on the basis of the different segmentation methods. A partial least square regression algorithm was used to construct a full-grown maize canopy chlorophyll content diagnostic model. The result showed that the model accuracy was low when the image background was not removed (Rc2 (the determination coefficient of calibration set) = 0.5431, RMSEF (the root mean squared error of forecast) = 4.2184, MAE (the mean absolute error) = 3.24; Rv2 (the determination coefficient of validation set) = 0.5894, RMSEP (the root mean squared error of prediction) = 4.6947, and MAE = 3.36). The diagnostic accuracy of the chlorophyll content could be improved by extracting the maize canopy through the segmentation method, which was based on the wavelet segmentation method. The maize canopy chlorophyll content diagnostic model had the highest accuracy (Rc2 = 0.6638, RMSEF = 3.6211, MAE = 2.89; Rv2 = 0.6923, RMSEP = 3.9067, and MAE = 3.19). The research can provide a feasible method for crop growth and nutrition monitoring on the basis of the UAV platform and has a guiding significance for crop cultivation management.

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

“…Various types of sensors mounted on UAV platforms, such as multispectral, hyperspectral, RGB, and thermal, are being widely used in the phenotypic evaluation of crops, with satisfactory data accuracy. The UAV-based nondestructive multispectral assessments of the LAI ( Comba et al, 2020 ), biomass ( Yue et al, 2019 ), chlorophyll content ( Qiao et al, 2020 ), nitrogen use efficiency ( Yang et al, 2020 ), senescence ( Hassan et al, 2021 ), and GY ( Hassan et al, 2019a ) have been reported for several crops. These assessments are based on the spectral reflectance from the canopy of plants in the form of light bands with different wavelengths ( Li et al, 2014 ).…”

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

“…Similarly, Qiao et al (2020) [46] described a segmentation-based method to estimate chlorophyll content at different maize growth stages using multispectral imagery. They showed different segmentation results that produced variations in the extraction of maize canopy parameters, with the wavelet method as the best output.…”

Generative-Model-Based Data Labeling for Deep Network Regression: Application to Seed Maturity Estimation from UAV Multispectral Images

“…To extract specific information from multispectral images, vegetation indexes (VIs) have been proposed, which result from arithmetic combinations of images of different wavelengths. In agriculture, they can highlight vegetation health, photosynthetic activity, and leaves' chlorophyll concentration [22][23][24][25][26][27][28]. The best-known indexes are the Normalized Difference Vegetation Index (NDVI) [29] for chlorophyll content and the Normalized Difference Red-Edge (NDRE) for plant nitrogen diagnosis [30].…”