A method for establishing an image evaluation index for non-destructive diagnosis of crop nitrogen nutrition

Abstract

The invention discloses an image evaluation index establishing method for nondestructive diagnosis of crop nitrogen nutrition. The method comprises the steps that wheat canopy images and the nitrogen content of leaves are obtained; the wheat canopy images are subjected to partition extraction, average pixel values of R, G, B, H, S and I components of all non-zero pixel points of leaves in each wheat canopy image are calculated to serve as basic color characteristic parameters r, g, b, h, s and i; nine image characteristic parameters r, g, b, r-g-b, r-g, r-b, (r-g-b) / (r+g+b), (r-g) / (r+g+b), and (r-b) / (r+g+b) are calculated; CMI=(xr-yg-zb) is established, fitting with the nitrogen content of the leaves is carried out, weight coefficients x, y and z are determined, CMI is subjected to standardized processing, and it is determined that NCMI=(xr-yg-zb) / (r+g+b). The NCMI established in the method is a relatively-proper winter wheat nitrogen nutrition evaluation index, accuracy and stability are achieved, and the crop nitrogen content can be quantitatively evaluated.

Description

technical field [0001] The invention relates to a method for establishing an image evaluation index for crop nitrogen nutrition nondestructive diagnosis. Background technique [0002] With the rapid development of remote sensing and image processing technology, extracting canopy image parameters for crop non-destructive nitrogen nutrition diagnosis is becoming a current research hotspot. [1-5] . However, the acquisition of canopy reflectance spectrum equipment is expensive and the data analysis involved is complicated, which limits the wide application of remote sensing technology in agricultural production; while the digital camera is easy and fast to operate, and has a certain application prospect in the non-destructive nitrogen nutrition diagnosis of crops [2] . The leaf color of crops changes with the change of internal nutrition, and the determination and acquisition of leaf image characteristic parameters is the key to accurately diagnose the nitrogen nutrition statu...

AI Technical Summary

Problems solved by technology

It can be seen that the leaf color of the wheat canopy is a comprehensive reflection of the three primary colors of blue, red and green. Any single color change will cause changes in the overall canopy leaf color to varying degrees. The typical image feature parameters NRI, NGI, NBI, G / R et al. did not consider the overall impact of the remaining monochromatic components on nutrition detection when highlighting the monochromatic components

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