The Development of a Weight Prediction System for Pigs Using Raspberry Pi

Myung Hwan Na, Wan Hyun Cho, Sang Kyoon Kim and In Seop Na ()
Additional contact information
Myung Hwan Na: Department of Statistics, Chonnam National University, Gwangju 61186, Republic of Korea
Wan Hyun Cho: Department of Statistics, Chonnam National University, Gwangju 61186, Republic of Korea
Sang Kyoon Kim: Department of Statistics, Chonnam National University, Gwangju 61186, Republic of Korea
In Seop Na: Division of Culture Contents, Chonnam National University, Yeosu 59626, Republic of Korea

Agriculture, 2023, vol. 13, issue 10, 1-12

Abstract: Generally, measuring the weight of livestock is difficult; it is time consuming, inconvenient, and stressful for both livestock farms and livestock to be measured. Therefore, these problems must be resolved to boost convenience and reduce economic costs. In this study, we develop a portable prediction system that can automatically predict the weights of pigs, which are commonly used for consumption among livestock, using Raspberry Pi. The proposed system consists of three parts: pig image data capture, pig weight prediction, and the visualization of the predicted results. First, the pig image data are captured using a three-dimensional depth camera. Second, the pig weight is predicted by segmenting the livestock from the input image using the Raspberry Pi module and extracting features from the segmented image. Third, a 10.1-inch monitor is used to visually show the predicted results. To evaluate the performance of the constructed prediction device, the device is learned using the 3D sensor dataset collected from specific breeding farms, and the efficiency of the system is evaluated using separate verification data. The evaluation results show that the proposed device achieves approximately 10.702 for RMSE, 8.348 for MAPE, and 0.146 for MASE predictive power.

Keywords: livestock measurement device; computer vision techniques; RGB-D sensor data; pig segmentation; body and shape feature extraction; prediction of pig weight (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
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/10/2027/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/10/2027/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:10:p:2027-:d:1263354

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().