Abstract
The accurate estimation of nitrogen accumulation is of great significance to nitrogen fertilizer management in wheat production. To overcome the shortcomings of spectral technology, which ignores the anisotropy of canopy structure when predicting the nitrogen accumulation in wheat, resulting in low accuracy and unstable prediction results, we propose a method for predicting wheat nitrogen accumulation based on the fusion of spectral and canopy structure features. After depth images are repaired using a hole-filling algorithm, RGB images and depth images are fused through IHS transformation, and textural features of the fused images are then extracted in order to express the three-dimensional structural information of the canopy. The fused images contain depth information of the canopy, which breaks through the limitation of extracting canopy structure features from a two-dimensional image. By comparing the experimental results of multiple regression analyses and BP neural networks, we found that the characteristics of the canopy structure effectively compensated for the model prediction of nitrogen accumulation based only on spectral characteristics. Our prediction model displayed better accuracy and stability, with prediction accuracy values (R2) based on BP neural network for the leaf layer nitrogen accumulation (LNA) and shoot nitrogen accumulation (SNA) during a full growth period of 0.74 and 0.73, respectively, and corresponding relative root mean square errors (RRMSEs) of 40.13% and 35.73%.
Highlights
Nitrogen is a key element required for the growth and development of wheat, and exerts an important influence on its yield and quality
Since only visible light band information is used, the R, G, and B values of the image are affected by lighting conditions at the time the image is captured, and, since it is difficult for a two-dimensional digital image to reflect the three-dimensional morphological information of the crop canopy, the accuracy of constructing a nitrogen nutrition index estimation model based on two-dimensional digital images is not ideal [19]
RGB information and depth information to express the characteristics of canopy structure, we propose a pixel-level RGB-D image fusion method, which uses the connection between the corresponding pixels of RGB images and depth images to complement the two kinds of information in the modeling process
Summary
Nitrogen is a key element required for the growth and development of wheat, and exerts an important influence on its yield and quality. At present, during wheat production, in order to pursue high crop yields, problems exist such as excessive nitrogen fertilizer application and unscientific fertilization methods, which cause environmental pollution, and reduce profits [1,2,3]. A quick and effective grasp of the status of nitrogen nutrition indicators during the key growth period of wheat is a prerequisite for achieving accurate fertilizer management. When the influence of the anisotropic characteristics of canopy structure on the spectrum is ignored, the detection results are not stable enough, and the accuracy is relatively poor [14,15]. It is necessary to combine spectral characteristics and canopy structure characteristics in order to detect the nitrogen nutrition status of crops, because the combination of the two characteristics can have a more positive impact on the predicted results. Since only visible light band information is used, the R, G, and B values of the image are affected by lighting conditions at the time the image is captured, and, since it is difficult for a two-dimensional digital image to reflect the three-dimensional morphological information of the crop canopy, the accuracy of constructing a nitrogen nutrition index estimation model based on two-dimensional digital images is not ideal [19]
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