Deep fully-convolutional neural network-based fast segmentation method of field rice panicles

A convolutional neural network, deep technology, applied in the field of agricultural automation, can solve the problems of long time and low segmentation accuracy

Active Publication Date: 2018-08-17
HUAZHONG AGRI UNIV
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Problems solved by technology

[0006] In order to overcome the problem of low segmentation accuracy and long time-consuming in the prior art for different varieties and growth stages of the field rice ear segmentation method, the present inventio...

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  • Deep fully-convolutional neural network-based fast segmentation method of field rice panicles
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  • Deep fully-convolutional neural network-based fast segmentation method of field rice panicles

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Embodiment Construction

[0031] The technical solution adopted by the present invention in order to solve the technical problems provides a method for fast segmentation of field rice ears based on deep full convolutional neural network. The overall technical process of the method is shown in figure 1 .

[0032] A method for rapidly segmenting paddy ears based on a deep full convolutional neural network, characterized in that it includes:

[0033] Step A, the image edge of the original image to be divided is filled with black, and becomes an image that can be cut into an integer number of sub-images (360 * 480, height * width);

[0034] Step B, cropping the image into several sub-images without intervals and overlaps, and recording the position index of the sub-images, so that the subsequent image stitching can restore the original image;

[0035] Step C, performing pixel-level semantic segmentation on each sub-image based on a deep fully convolutional neural network;

[0036] Step D, splicing the di...

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Abstract

The invention discloses a deep fully-convolutional neural network-based fast segmentation method of field rice panicles. According to the method, a deep fully-convolutional neural network-based segmentation network of the field rice panicles is designed. A to-be-segmented field image is firstly divided into a plurality of sub-graphs which are suitable for an input size of the deep fully-convolutional neural network, pixel-level semantics segmentation is carried out on all the sub-graphs by the deep fully-convolutional neural network, and then all the sub-graphs are spliced for obtaining a segmentation result of the same size as the input image. According to the method, impacts of huge differences of colors, shapes, sizes, postures and textures of the rice panicles of different varieties and growth stages, severe irregularity of rice panicle edges, panicle leaf color aliasing and uneven and varying illumination, occlusion and wind blowing factors in a field can be overcome, and fast andaccurate segmentation on the field rice panicles of the different varieties and growth stages can be realized. Compared with the prior art, the method has the technical advantages of high precision,high applicability and high processing speed.

Description

technical field [0001] The invention belongs to the field of agricultural automation, and in particular relates to automatic measurement of rice phenotypic parameters, in particular to a method for rapidly segmenting rice spikes in a field based on a deep fully convolutional neural network. Background technique [0002] The production and distribution of rice is related to the food security of more than half of the world's population. High yield has always been one of the important goals of rice breeding and cultivation. In the field of rice breeding and cultivation, it is necessary to measure the yield of a large number of candidate samples in different environments, so as to provide a scientific basis for cultivating high-yield, high-quality, and stress-resistant rice varieties. Rice panicle is the organ where rice grains grow, and the traits of panicle are directly related to rice yield. Rice ears also play a very important role in the detection of rice diseases and ins...

Claims

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Application Information

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IPC IPC(8): G06K9/34G06K9/62
CPCG06V10/267G06F18/214
Inventor 段凌凤杨万能叶军立冯慧黄成龙周风燃熊立仲陈国兴
Owner HUAZHONG AGRI UNIV
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