Live pig behavior classification method based on BP neural network

A BP neural network and classification method technology, applied in the field of animal health monitoring in the livestock and poultry industry, can solve the problems of high equipment cost, low accuracy rate, failure to realize individual all-day behavior monitoring and statistics, etc.

Inactive Publication Date: 2017-01-11
NORTHWEST A & F UNIV
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In summary, the above-mentioned studies are all partial identification, and the monitoring and statistics of individual behavio...

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  • Live pig behavior classification method based on BP neural network
  • Live pig behavior classification method based on BP neural network
  • Live pig behavior classification method based on BP neural network

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[0051] The implementation of the present invention will be described in detail below in conjunction with the drawings and examples.

[0052] The overall design of the present invention is as follows figure 1 As shown, it consists of four parts: data collection, data processing, data sending and receiving, model building and classification. Among them, the data acquisition end uses the micro inertial sensor MPU-6050 module with a sampling frequency of 10Hz to dynamically obtain the acceleration and angular velocity information generated by the pig movement in real time, and uses the magnetometer sensor HMC5833 to dynamically obtain the geomagnetic intensity information in real time. In the data processing part, the STC12C5A60S2 single-chip microcomputer fuses and filters the data and outputs the attitude angle information accurately, which provides an important reference for the classification of different behaviors of pigs. In the data sending and receiving part, the HC-05 Bl...

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Abstract

The invention discloses a live pig behavior classification method based on a BP neural network, and the method comprises the steps: collecting live pig acceleration, angular speed and attitude angle information in real time as input; obtaining a classification result according to a pre-built BP neural network model; carrying out the matching of four behavior manners of live pigs through video segment information: standing, walking, groveling and lying; jointly obtaining 6000 groups of data, and carrying out the Z-score normalization processing; selecting an LM training method for the training of a discrimination model. The method considers the attitude angle as the input variable of the BP neural network, is high in network convergence rate, and meets the requirements of instantaneity. Moreover, a local flat region can be effectively surpassed in a training process, and an expected error level is reached. The model classification precision is high. A verification result indicates that the live pig behavior discrimination model considering the attitude angle building is in highly linear relation with the actual behaviors, and the correlation coefficient is 0.992. The overall discrimination accuracy is 92.64%, and the accuracy of the discrimination model built under the condition that only the acceleration and angular speed data is considered is 86.38%, which indicates that the live pig behavior discrimination model based on the attitude angle building can provide data support for the discrimination of the health condition of the live pigs.

Description

technical field [0001] The invention belongs to the technical field of animal health status monitoring in the livestock and poultry industry, in particular to a pig behavior classification method based on BP neural network. Background technique [0002] Animal behavior is an external manifestation of the physiological health of animals. It can reflect the adaptation of the animal body to the environment to a certain extent, affect animal quality, slaughter rate, estrus, and disease monitoring. It is one of the important indicators for evaluating animal welfare. With the development of large-scale farming, relying on traditional methods to monitor the behavior of individual pigs is inefficient, and breeders cannot find abnormal individual pigs in time, thus failing to make scientific and reasonable breeding decisions, resulting in low breeding efficiency in pig farms. Therefore, in order to achieve automatic management and save labor costs, automatic monitoring of pig-related...

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Application Information

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IPC IPC(8): G06K9/62G06N3/02
CPCG06N3/02G06F18/25G06F18/24
Inventor 张海辉王传哲王东张阳邵志成张子儒
Owner NORTHWEST A & F UNIV
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