Piglet pressed detection method based on deep learning model

A detection method and deep learning technology, applied in the field of breeding, can solve problems such as time-consuming and labor-intensive, inability to recognize 100% accuracy, and inability to effectively solve pain point problems, so as to reduce the death rate, low long-term operation cost, and good application effect Effect

Pending Publication Date: 2021-08-31
北京新希望六和生物科技产业集团有限公司 +7
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When this kind of incident occurs, the traditional method is that when the breeder finds that the piglet is being crushed, he rushes to the sow and makes it stand up and take the piglet by patting it. Stand up again, so this method of relying on human subjective will and behavior to rescue piglets is time-consuming and labor-intensive, and cannot effectively solve this pain point
[0003] In addition, the complexity of the livestock breeding environment also poses a huge challenge to the stability of some automated farming equipment. For example, the high temperature, high humidity, and high dust farming environment will have a greater impact on some automated measurement sensors. Some studies have used images or Sound technology solves this problem. It is hoped that by collecting the sound near the delivery bed where the sow is lying in real-time and non-contact, using pattern recognition technology to identify whether the p

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  • Piglet pressed detection method based on deep learning model
  • Piglet pressed detection method based on deep learning model
  • Piglet pressed detection method based on deep learning model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] The present invention provides a method for detecting piglets being crushed based on a deep learning model. S1. A microphone array 1 is set in the stall of the pig delivery room. 1. Set in the middle of the column wall, the microphone array includes an even number of microphones, and the microphones are symmetrically distributed toward the two columns;

[0052] S2, collecting the sounds of the two fields through the microphone;

[0053] S3. According to the sound collected in S2, determine the position of the sound source;

[0054] S4. Combining the piglet's vocalization model based on CNN and spectrogram to determine whether the piglet is crushed;

[0055] The sound signal is short-time Fourier transformed into linear frequency scale features, the linear frequency scale is converted into MEL frequency scale, and the MEL frequency scale is used as the input parameter of the CNN model.

[0056] The method of S3 judging the location of the sound source based on the soun...

Embodiment 2

[0085] On the basis of Example 1, the hardware selection of the microphone array 1 in this solution is a professional recording microphone K053, with a sensitivity of -38±3dB, a cardioid directivity, a 3.5mm or USB adapter, and a frequency response of 50Hz to 16kHz. Output impedance ≤ 680Ω, signal-to-noise ratio ≥ 70dB. The role of the microphone array is to predict the specific pen where the piglet is pressed through the sound source positioning technology, such as figure 2 as shown,

[0086] figure 2 The installation position of the middle microphone array 1 is the center of two adjacent delivery room columns, and the direction of the sound is judged by comparing the spectrum energy or time domain energy of the sound source from the two columns. If the number of microphones is increased from 2 (default) to 4, you can further know the position of the sound source inside the same column, for example, figure 2 The area positions of positive 2 (15-29° sound source angle) a...

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Abstract

The invention discloses a piglet pressed detection method based on a deep learning model, and relates to the technical field of breeding, and the technical scheme comprises steps: arranging a microphone array in a pigsty of a pig delivery room; collecting sound of two columns through the microphone; judging the position of a sound source according to the collected sound; and judging whether the piglet is pressed or not by combining a pressed piglet sounding model established based on the CNN and a spectrogram. The beneficial effects of the invention are that the method provided by the scheme liberates the demand of a delivery room for labor force, reduces the production management cost of an enterprise, and improves the digital intelligence level of livestock breeding. Besides, from the perspective of production, by means of the scheme and method, the death and culling rate of the piglets can be reduced, more piglets can be rescued, the piglets can be converted into growing-finishing pigs which continue to be bred, and great help is provided for further improving the operating profit of animal husbandry enterprises.

Description

technical field [0001] The invention relates to the field of breeding technology, in particular to a method for detecting piglets being crushed based on a deep learning model. Background technique [0002] According to statistics, in the process of sow farrowing, 1 out of every 10 piglets will be crushed to death by the sow. Nearly half of the deaths occurred in the first 3 days after birth. If the sow stands up within 3 minutes of being on the piglet, the piglet has a chance of survival. When this kind of incident occurs, the traditional method is that when the breeder finds that the piglet is being crushed, he rushes to the sow and makes it stand up and take the piglet by patting it. Stand up again, so this method of relying on human subjective will and behavior to rescue piglets is time-consuming and labor-intensive, and cannot effectively solve this pain point. [0003] In addition, the complexity of the livestock breeding environment also poses a huge challenge to th...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214
Inventor 杜晓冬樊士冉张瑞雪张志勇陈麒麟闫雪冬赵铖
Owner 北京新希望六和生物科技产业集团有限公司
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