System Design of Optimal Pig Shipment Schedule through Prediction Model

Jin-Wook Jang (), Jong-Hee Lee, Gi-Pou Nam and Sung-Ho Lee
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Jin-Wook Jang: Department of Cooperative Digital Management, Agricultural Cooperative University, Goyang-si 10292, Gyeonggi-do, Republic of Korea
Jong-Hee Lee: Department of Digital Agricultural Promotion, Agricultural Cooperative University, Goyang-si 10292, Gyeonggi-do, Republic of Korea
Gi-Pou Nam: Department of Cooperative Digital Management, Agricultural Cooperative University, Goyang-si 10292, Gyeonggi-do, Republic of Korea
Sung-Ho Lee: Hohyun F&C, Suwon-si 16432, Gyeonggi-do, Republic of Korea

Agriculture, 2023, vol. 13, issue 8, 1-10

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.

Keywords: machine learning; shipping; prediction model; shipping timing; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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