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Rice seedling row line extraction method, device, computer device and storage medium

An extraction method and seedling technology, applied in the field of image processing, can solve the problem of inaccurate identification of rice seedling line and achieve the effect of eliminating the interference of environmental factors

Active Publication Date: 2019-02-15
SOUTH CHINA AGRI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] Based on this, it is necessary to provide a rice seedling row line extraction method, device, computer equipment and storage medium for the problem of inaccurate identification of rice seedling row lines

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  • Rice seedling row line extraction method, device, computer device and storage medium
  • Rice seedling row line extraction method, device, computer device and storage medium
  • Rice seedling row line extraction method, device, computer device and storage medium

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Embodiment Construction

[0048] In order to make the purpose, technical solutions, and advantages of this application clearer, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and not used to limit the application.

[0049] Such as figure 1 As shown, the embodiment of the present invention provides a method for extracting rows of rice seedlings, including:

[0050] Step S30, acquiring a field image of rice seedlings;

[0051] Step S40: Recognizing the rice seedling field image through the pre-trained deep convolutional neural network model to obtain the coordinates of each seedling in the rice seedling field image;

[0052] Step S50, clustering multiple seedling coordinates, and identifying the seedling row of the current operation;

[0053] In step S60, linear regression is performed on the seedling coordinates on the currently op...

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Abstract

The invention relates to a method for extracting rice seedling line, which comprises the following steps: obtaining rice seedling field image; recognizing The field images of rice seedlings by the trained depth convolution neural network model, and obtaining the coordinates of each seedling in the field images of rice seedlings. Clustering multiple seedling coordinates and recognizing the currentseedling row; obtaining The current seedling row line by linear regression of the seedling coordinates of the current operation seedling row. Through the application of the pre-trained deep convolution neural network and clustering algorithm, the interference of environmental factors in rice field can be eliminated better, the current seedling line can be identified more accurately, and the automatic driving of transplanter can be guided.

Description

Technical field [0001] The invention relates to the field of image processing, in particular to a method, device, computer equipment and storage medium for extracting rows of rice seedlings. Background technique [0002] Agricultural field navigation technology provides navigation information for the operation of modern agricultural production machinery, and has been applied to various agricultural machinery and equipment. Among the agricultural equipment used for seeding, the importance of agricultural field navigation is particularly obvious. [0003] At present, the research of agricultural field navigation at home and abroad mainly focuses on machine vision navigation. Meng Qingkuan and others constructed Cg components that were not related to illumination, selected 2Cg-Cr-Cb feature factors to gray-scale the image, established the constraint model of the corn crop line equation according to the characteristics of the crop line in the image, and used particle swarm algorithm t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V10/457G06V10/462G06N3/045G06F18/231
Inventor 齐龙林少敏马旭蒋郁邓向武李帅刘海云曹聪龚浩陈林涛
Owner SOUTH CHINA AGRI UNIV