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.
Highlights
Chlorophyll content is one of the important indicators that reflect the photosynthetic ability and nutrient status of maize plants [1,2,3]
The results showed that the accuracy of the diagnostic model constructed using the original canopy spectral data was low (Rc2 = 0.5431, RMSEF = 4.2184, mean absolute error (MAE) = 3.24; Rv2 = 0.5894, RMSEP = 4.6947, and MAE = 3.36), and the accuracy of the diagnosis of chlorophyll content in maize canopy could be improved to varying degrees by using three segmentation methods to remove background noise in maize multispectral images
We studied the background noise in the multispectral images of maize canopy acquired by unmanned aerial vehicle (UAV) and discussed the performance of three noise rejection methods
Summary
Chlorophyll content is one of the important indicators that reflect the photosynthetic ability and nutrient status of maize plants [1,2,3]. The traditional crop chlorophyll diagnosis is mainly carried out by chemical analysis, which requires destructive sampling, takes a long time, and is costly. These conditions might not satisfy the requirements of rapid chlorophyll monitoring on field crops for making management decision. According to the principle of light absorption and reflectance, technologies of spectral analysis, imaging spectroscopy, and other nondestructive methods have been widely used in crop monitoring [4,5,6,7,8]. This article aims to use the multispectral sensor carried by the UAV to collect maize canopy spectral data in the field and conduct a rapid diagnosis of the chlorophyll content to estimate the growth status and guide the field management
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