A method for identifying sugarcane stem nodes based on computer vision

Abstract

The invention discloses a sugarcane stalk node recognition method based on computer vision, and the sugarcane stalk node recognition method comprises the following operation steps of (1) processing a collected sugarcane stalk node image by utilizing MATLAB software, and preprocessing sugarcane stalk nodes by adopting grayscale processing and Sobel edge extraction to obtain a Sobel edge image; (2) expanding, corroding and reexpanding discontinuous and small edges after the preprocessing in combination with mathematical morphology, eliminating sugarcane stalk edges and small useless edges to acquire the sugarcane stalk node edge linear image; and (3) linearly extracting the sugarcane stalk node edge linear image by using a Radon function in the MATLAB, solving the linear distance from the stalk node to a coordinate center, and thereby determining an accurate position of the sugarcane stalk node. Due to adoption of the method, an accurate signal can be provided for controlling and researching a sugarcane seed stem cutting and damage-preventing device.

Description

technical field

[0001] The invention relates to a recognition method, in particular to a computer vision-based recognition method for sugarcane stem nodes. Background technique

[0002] The sugarcane industry, which uses sugarcane as the main raw material, is an advantageous and characteristic industry in Guangxi. It plays a pivotal role in Guangxi's economic development. It is an important pillar of Guangxi's economic development and an economic source to help sugarcane farmers get rid of poverty and become rich. Most of the sugarcane producing areas in the world have realized the mechanization of sugarcane cultivation to a certain extent. Foreign planters have good performance and perfect functions, but they have not yet been equipped with a professional anti-injury bud cutting device. It is more difficult for domestic planters to realize the purpose of automatically preventing bud damage during the cutting of sugarcane seeds. In agriculture, computer vision has a wide r...

Examples

Embodiment 1

[0046] figure 1 Shown is the black sugarcane stem node image that needs to be identified in this embodiment.

[0047] First, the original image is converted to grayscale, and then the Sobel operator is used to detect the image edge in the vertical direction, such as figure 2 shown.

[0048] I=imread(' figure 1.jpg');% read in the image

[0049] I=rgb2gray(I);% image grayscale conversion

[0050] [VSFAT Threshold]=edge(I,'sobel',0.07,'vertical');% edge detection, operator is vertical sobel

[0051] figure,imshow(VSFAT),title('vertical');% display edge detection image

[0052] The image extracted from the Sobel edge is dilated to connect and widen the edges of small intermittent stem nodes. Use dilation and pass a structuring element se. se represents a circle with a radius of 2 pixels.

[0053] se=strel('disk',2);

[0054] WEIGHT=double(imdi late(VSFAT,se));

[0055] At the same time, pixels near the border are assigned a value of 0. The result of running the progra...

technical field

[0001] The invention relates to a recognition method, in particular to a computer vision-based recognition method for sugarcane stem nodes. Background technique

[0002] The sugarcane industry, which uses sugarcane as the main raw material, is an advantageous and characteristic industry in Guangxi. It plays a pivotal role in Guangxi's economic development. It is an important pillar of Guangxi's economic development and an economic source to help sugarcane farmers get rid of poverty and become rich. Most of the sugarcane producing areas in the world have realized the mechanization of sugarcane cultivation to a certain extent. Foreign planters have good performance and perfect functions, but they have not yet been equipped with a professional anti-injury bud cutting device. It is more difficult for domestic planters to realize the purpose of automatically preventing bud damage during the cutting of sugarcane seeds. In agriculture, computer vision has a wide r...

Examples

Embodiment 1

[0046] figure 1 Shown is the black sugarcane stem node image that needs to be identified in this embodiment.

[0047] First, the original image is converted to grayscale, and then the Sobel operator is used to detect the image edge in the vertical direction, such as figure 2 shown.

[0048] I=imread(' figure 1.jpg');% read in the image

[0049] I=rgb2gray(I);% image grayscale conversion

[0050] [VSFAT Threshold]=edge(I,'sobel',0.07,'vertical');% edge detection, operator is vertical sobel

[0051] figure,imshow(VSFAT),title('vertical');% display edge detection image

[0052] The image extracted from the Sobel edge is dilated to connect and widen the edges of small intermittent stem nodes. Use dilation and pass a structuring element se. se represents a circle with a radius of 2 pixels.

[0053] se=strel('disk',2);

[0054] WEIGHT=double(imdi late(VSFAT,se));

[0055] At the same time, pixels near the border are assigned a value of 0. The result of running the progra...

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