Real-time on-line system-based field leaf image edge extraction method and system

Abstract

The invention relates to a real-time on-line system-based field leaf image edge extraction method and a real-time on-line system-based field leaf image edge extraction system. The method comprises the following steps of: 1, performing gray-level mapping processing on segmentation area sub-images; 2, performing smoothening and noise removing, thresholding, edge detection and preliminary optimization on the images processed by the step 1, and judging whether a target area has a leaf shape; 3, performing smoothening and noise removing, thresholding negation, edge detection and preliminary optimization on the images processed by the step 1, and judging whether the target area has a leaf shape; 4, performing smoothening and noise removing, thresholding, thresholding negation, edge detection, preliminary optimization and area combination on the images processed by the step 1, and judging whether the target area has a leaf shape; and 5, performing further optimization and closed operation onthe images processed by the steps 2, 3 and 4 to obtain edge-closed edge images. Through the method and the system, complete, enclosed and accurately positioned target boundary curve images can be acquired, and the segmentation success rate is high.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a real-time online system-based edge extraction method and system for blade field image. Background technique [0002] Machine vision, also known as computer vision, refers to the technology designed by humans and implemented in a computer environment to simulate or reproduce certain intelligent behaviors related to human vision. The research and application of machine vision technology in agriculture began in the late 1970s. The main research focuses on the use of machine vision in agricultural product sorting machinery to inspect and classify the quality of agricultural products. In recent years, image processing technology based on machine vision has developed rapidly, and image processing technology itself has made major breakthroughs in theory and practice. In addition to agricultural product sorting machinery, this technology has now penetrated into crop gr...

AI Technical Summary

Problems solved by technology

In the actual application of plant leaf images, it is particularly difficult to automatically select an appropriate threshold due to noise, light brightness, background and other factors in the field image

At the same time, since field images are usually processed by the image compression algorithm H.264 of the video server equipment in the real-time online system, the gray level of the image is much lower than that obtained by ordinary non-network-transmitted camera equipment, which will give Image segmentation causes greater difficulties (mainly the adhesion between the target area and the background)

Automatic thresholding is effective for simple image processing that is either one or the other, such as the processing of some binary images, but for leaf images with significant grayscale differences in the target area in the image, there is no obvious grayscale difference in the image, or each object The image segmentation problem with a large overlapping gray value range, it is difficult to obtain a satisfactory segmentation effect by simply combining common threshold operators and edge detection operators

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