Yak weight prediction method based on CNN-LSTM neural network

A technology of neural network and prediction method, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., to achieve the effect of avoiding stress response

Pending Publication Date: 2021-01-05
CHENGDU XIMENG TEKE TECH DEV CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of predicting the body weight of yaks, and proposes a method for predicting body weight of yaks based on CNN-LSTM neural network

Method used

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  • Yak weight prediction method based on CNN-LSTM neural network
  • Yak weight prediction method based on CNN-LSTM neural network
  • Yak weight prediction method based on CNN-LSTM neural network

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Embodiment Construction

[0052] Embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0053] Such as figure 1 Shown, the present invention provides a kind of yak body weight prediction method based on CNN-LSTM neural network, comprises the following steps:

[0054] S1: Use the camera to collect the side view of the yak, and obtain and store the point cloud data of the yak;

[0055] S2: Preprocess the stored yak point cloud data to obtain point cloud data without horizontal railings;

[0056] S3: Use the cubic B-spline curve method to repair the point cloud data without the horizontal railings, and obtain the repaired yak point cloud data;

[0057] S4: Take the repaired yak point cloud data as input, and use the CNN-LSTM neural network to predict the weight of the yak.

[0058] In the embodiment of the present invention, such as figure 1 As shown, step S1 includes the following sub-steps:

[0059] S11: Install the camera at a distance...

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Abstract

The invention discloses a yak weight prediction method based on a CNN-LSTM neural network, and the method comprises the following steps: S1, collecting a yak side view through a camera, obtaining yakpoint cloud data, and storing the yak point cloud data; S2, preprocessing the stored yak point cloud data to obtain point cloud data without horizontal railings; S3, repairing the point cloud data without the horizontal railing to obtain repaired yak point cloud data; and S4, taking the repaired yak point cloud data as input, and performing yak weight prediction by using the CNN-LSTM neural network. According to the yak weight prediction method based on the CNN-LSTM neural network, the neural network model and the three-dimensional visualization technology are utilized to construct the yak weight prediction model, non-contact measurement of yak weight is achieved, and convenience is provided for large-scale and standardized yak breeding.

Description

technical field [0001] The invention belongs to the technical field of animal husbandry, and in particular relates to a weight prediction method for yaks based on a CNN-LSTM neural network. Background technique [0002] Precision animal husbandry is one of the important research directions of modern agriculture and an important part of smart agriculture. Precision animal husbandry mainly refers to a set of scientific breeding and management methods that are implemented regularly and quantitatively on individual animals by using animal science and information technology to ensure the quality and safety of animal husbandry products and promote high-efficiency, low-cost and sustainable development of animal husbandry. [0003] The body weight of a yak is an important part of its body shape assessment. Determining the weight of a yak plays an important role in breeding, feed ration, determining the dosage of treatment and judging the health status of yaks; the traditional method...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/50G01B11/00G06N3/04G06N3/08G06T5/00
CPCG06T7/50G06N3/049G06N3/08G01B11/00G06T2207/10028G06T2207/10048G06N3/044G06N3/045G06T5/77
Inventor 彭飞陈颖周齐朋廖勇
Owner CHENGDU XIMENG TEKE TECH DEV CO LTD
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