Rice test device based on a convolutional neural network and a use method thereof

A convolutional neural network and rice technology, which is applied in the direction of instruments, calculations, character and pattern recognition, etc., can solve the problem of incomplete classification and statistics of seeds, and achieve the effect of improving accuracy and statistical accuracy

Pending Publication Date: 2019-06-21
AGRO BIOLOGICAL GENE RES CENT GUANGDONG ACADEMY OF AGRI SCI
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[0004] The purpose of the present invention is to provide a rice seed testing device based on convolutional neural network and its use method, to

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  • Rice test device based on a convolutional neural network and a use method thereof
  • Rice test device based on a convolutional neural network and a use method thereof
  • Rice test device based on a convolutional neural network and a use method thereof

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[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0030] See Figure 1-4 An embodiment provided by the present invention: a rice test device based on a convolutional neural network, comprising a housing 1, a camera 2 is installed on the upper end of the inner wall of the housing 1, and two cameras 2 are provided. A movable support plate 3 is provided below, and two movable support plates 3 are provided, a support partition 8 is provided between the two movable support plates 3, and two opposite inner walls of the housing 1 are provided with hydraulic cylinders 4, and the hydraulic cylinders The upper end of 4 and the lower end of the movable support plate 3 are rotationally connected by a ...

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Abstract

The invention discloses a rice seed testing device based on a convolutional neural network and a use method thereof, and the use method comprises the following steps: separating a single seed from a rice seed photo, and extracting a seed contour by using a computer vision algorithm; classifying the separated kernels according to the outline pictures of the kernels, and dividing the kernels into closed kernels, closed kernels, open kernels, open kernels, closed kernels, closed kernels, closed kernels, open kernels and open kernels; training the deep convolutional neural network by using variousgrain contour photograph samples; verifying the deep convolutional neural network by using a verification photo; and inputting a to-be-processed rice seed photo into the deep convolutional neural network for test. Compared with the prior art, the method has the advantages that the rice is effectively and quickly divided into eight types, the eight types of rice are counted at the same time, and the problem that seed classification and statistics are not detailed in the test process is solved.

Description

technical field [0001] The invention relates to the field of rice seed testing, in particular to a rice seed testing device based on a deep learning convolutional neural network and a method for using the same. Background technique [0002] In the prior art, generally use machine equipment or a combination of machine equipment and machine vision to test seeds, such as CN201010234207 (digital rice test plant) discloses a digital test plant, which separates solid grains and empty grains by air separation Kernels were classified into intact and damaged kernels using the Bayesian classification method in Machine Vision Technology Dynamics. But there are the following deficiencies: one is that in the process of seed testing, the classification and statistics of seeds are not detailed, such as only being divided into two kinds of solid grains and grains, and there is no classification of seeds such as solid grains, semi-solid grains, batch grains, and half grains. Statistics; the...

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Application Information

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IPC IPC(8): G06K9/62G06K9/34
CPCY02P90/30
Inventor 易春付华张友胜高家东
Owner AGRO BIOLOGICAL GENE RES CENT GUANGDONG ACADEMY OF AGRI SCI
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