Field rice ear segmentation method based on depth learning

A deep learning, rice ear technology, applied in image analysis, image data processing, instruments, etc., can solve the problem of low segmentation accuracy

Active Publication Date: 2019-02-19
HUAZHONG AGRI UNIV
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AI Technical Summary

Problems solved by technology

[0006] In order to overcome the problem of low segmentation accuracy of the field rice ear segmentation method for different varieties and growth periods in the prior art, t

Method used

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  • Field rice ear segmentation method based on depth learning
  • Field rice ear segmentation method based on depth learning
  • Field rice ear segmentation method based on depth learning

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Embodiment Construction

[0026]In order to solve the technical solution adopted by the technical problem, the present invention provides a deep learning-based field rice ear segmentation method, which is characterized in that it includes:

[0027] Step A, select varieties with large differences in panicle type, occlusion degree and panicle-leaf aliasing, select original images of different lighting conditions, and use them to build a rice panicle segmentation network model;

[0028] Step B, artificially use Photoshop to carry out pixel-level labeling on these images, the rice ear pixels are marked as 1, and the background pixels are marked as 0;

[0029] Step C, adjust the brightness of each image, specifically keep the H component and S component unchanged, and increase and decrease the V component by 20%, which is used to simulate the illumination changes in the field environment and improve the segmentation network. Generalization;

[0030] Step D, perform simple linear iterative clustering (SLIC)...

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Abstract

The invention discloses a field rice ear segmentation method based on depth learning. In this method, a depth-total convolution neural network model was designed for segmentation of rice panicles. Thefirst half of the network uses ResNet-101 layers is provided with the Squeeze and Excitation Module structure to filter the importance of the feature layer. All conventional convolution layers in theoriginal ResNet-101 network module 4 and module 5 are replaced with void convolution layers, and the step size is changed from 2 to 1. The second half of the network adopts hollow pyramid pooling andpyramid pooling structure. The method can overcome the great differences of rice ear color, shape, size, posture and texture in different varieties and growth stages, and can realize accurate segmentation of rice ear in different varieties and growth stages. The method can also overcome the influence of irregular edge of rice ear, leaf color aliasing, uneven and changing illumination, shielding and wind in the field. Compared with the prior art, the method has the technical advantages of high precision and strong applicability.

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 deep learning-based method for segmenting rice ears in field. 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 insect pests, nutritional diagnosis and growth...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T5/00G06N3/04
CPCG06T5/007G06T7/11G06N3/045
Inventor 段凌凤杨万能冯慧黄成龙叶军立熊立仲陈国兴周风燃杨万里
Owner HUAZHONG AGRI UNIV
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