Unmanned aerial vehicle-adopted rice canopy image processing method

Abstract

The invention belongs to the field of picture processing, and discloses an unmanned aerial vehicle-adopted rice canopy image processing method. The method comprises the following steps of: fixing a camera on an unmanned aerial vehicle; obtaining an initial shot picture; processing pixel points of the picture; generating a shot picture of the present time; carrying out grey processing on a color image; eliminating image noise; extracting a canopy porosity; and calculating a leaf area index. According to the method, equipment and wildly picked rice leaves are prevented from destructive artificial measurement, and through the unmanned aerial vehicle, rice canopies LAI can be rapidly obtained in real time, so that basic parameters are provided for real-time monitoring of rice growth vigor and remote-sensing yield estimation of rice. Comprehensive calculation is carried out a picture shot by initial exposure and one or more pictures shot by exposure on the basis of a relatively large exposure value, and compensation is carried out by taking the pixel points as units, so that signal to noise ratios of the pictures can be effectively enhanced, shade details of the pictures can be remarkably enhanced, and machine upgrading does not need to be carried out so that the cost is relatively low.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a rice canopy image processing method using an unmanned aerial vehicle. Background technique [0002] Rice is one of the most important food crops in my country. Rice leaves are the main place where rice interacts with the external environment. Many biophysical processes in rice, including photosynthesis, respiration, transpiration, and precipitation interception, take place in leaves. Leaf area index (LAI) reflects the total area of ​​plant leaves per unit surface. It determines the ability of rice to utilize and manage radiation, water, etc. Therefore, LAI is an important indicator to reflect the growth status of rice and estimate the rice yield; however, the effect of obtaining rice canopy pictures is not good, the signal-to-noise ratio is low, and the dark details of the pictures are not clear. [0003] To sum up, the problems existing in the existing ...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the traditional shooting is easy to cause damage to the rice, and at the same time, the image of the rice canopy is not good, the signal-to-noise ratio is low, and the details of the dark part of the image are not clear

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