System Design of Optimal Pig Shipment Schedule through Prediction Model

Author:

Jang Jin-Wook1ORCID,Lee Jong-Hee2,Nam Gi-Pou1,Lee Sung-Ho3

Affiliation:

1. Department of Cooperative Digital Management, Agricultural Cooperative University, Goyang-si 10292, Gyeonggi-do, Republic of Korea

2. Department of Digital Agricultural Promotion, Agricultural Cooperative University, Goyang-si 10292, Gyeonggi-do, Republic of Korea

3. Hohyun F&C, Suwon-si 16432, Gyeonggi-do, Republic of Korea

Abstract

We propose an optimal system for determining the shipping schedule for pigs using a predictive model using machine learning based on big data. This system receives photographic and weight measurement information for each pig from a camera and a weighing machine installed in a pig pen for raising pigs corresponding to a predetermined fattening period. Then, the photographic information of each of these pigs is applied to a predictive model machine-learned in advance to determine whether or not there are candidate pigs for determining the presence or absence of abdominal fat-forming pigs. And if there is a candidate pig, it is determined using a machine-learning model for predicting whether the candidate pig is an abdominal fat-forming pig by analyzing the pattern of weight increase of the abdominal fat-forming pig and changes in weight of a candidate. If the candidate pig is an abdominal fat-forming pig, the timing of shipping is determined by predicting when the weight of the candidate pigs, specifically the abdominal fat-forming pigs, will reach a predetermined minimum shipping weight. This prediction is made using a machine-learning model that considers the weight gain trend pattern of abdominal fat-forming pigs and tracks changes in the weight of the candidate pig. A machine-learning model is used to predict the timing of weight gain in candidate pigs, specifically those that develop abdominal fat, in order to determine the optimal shipping time. By analyzing the weight gain patterns of abdominal fat-forming pigs and monitoring the weight changes in the candidate pig, the model can predict when the candidate pig will reach the minimum weight required for shipping. In this paper, we would like to present a point of view based on the body type and weight of pigs corresponding to the fattening period through this system, whether intramuscular fat has adhered or abdominal fat is excessively formed by the fed feed and appropriate shipment as the fattening status of pigs.

Funder

Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry

Korea Smart Farm R&D Foundation

Ministry of Agriculture, Food and Rural Affairs

Ministry of Science and ICT

Rural Development Administration

The National Research Foundation of Korea

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

Reference20 articles.

1. Development of a model to analyze the relationship between smart pig-farm environmental data and daily weight increase based on decision tree;Han;J. Korea Inst. Inf. Commun. Eng.,2016

2. Prediction of Water Usage in Pig Farm based on Machine Learning;Lee;J. Korea Inst. Inf. Commun. Eng.,2017

3. Effects of the Energy Level of the Finisher Diet on Growth Efficiency and Carcass Traits of ‘High’-Market Weight Pigs;Lee;J. Anim. Sci. Technol.,2007

4. Body weight and backfat deposition in swine: Curves and correction factors;Quijandria;J. Anim. Sci.,1971

5. The effects of gender and slaughter weight on the growth performance, carcass traits and meat quality characteristics of heavy pigs;Latorre;J. Anim. Sci.,2004

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