Development of a computer vision system to detect inactivity in group-housed pigs

Authors

  • Christopher Chijioke Ojukwu 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China;
  • Yaoze Feng 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
  • Guifeng Jia 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
  • Haitao Zhao 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China;
  • Hequn Tan 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China

Keywords:

Matlab, computer vision, sows, machine vision, depth image, pigs, inactivity

Abstract

Excessive inactivity in farm animals can be an early indication of illness. Traditional way for detecting excessive inactivity in pigs relies on manual inspection which can be laborious and especially time-consuming. This paper proposed a computer vision system that could detect inactivity of individual pigs housed in group pens which is potential in alarming the farmer of the animals concerned. The system recorded sequential depth images for the animals in a pen and implemented a proposed image processing and logic analysis scheme named as ‘DepInact’ to keep track of the inactive time of group-housed individual pigs over time. To verify the robustness and accuracy of the developed system, a total of 656 pairs of corresponding depth data and color images, consecutively taken 4 s apart from each other, were attained. The verification process involved manually identifying all pigs using the color images captured. The results of identification of all pigs that were inactive for more than the preset period of time by DepInact were compared to those by manual inspection through the color images captured. An accuracy of 85.7% was achieved using the verification data, thus demonstrating that the developed system is a viable alternative to manual detection of inactivity of group-housed pigs. Nevertheless, more research is still needed to improve the accuracy of the developed system. Keywords: Matlab, computer vision, sows, machine vision, depth image, pigs, inactivity DOI: 10.25165/j.ijabe.20201301.5030 Citation: Ojukwu C C, Feng Y Z, Jia G F, Zhao H T, Tan H Q. Development of a computer vision system to detect inactivity in group-housed pigs. Int J Agric & Biol Eng, 2020; 13(1): 42–46.

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Published

2020-03-02

How to Cite

Ojukwu, C. C., Feng, Y., Jia, G., Zhao, H., & Tan, H. (2020). Development of a computer vision system to detect inactivity in group-housed pigs. International Journal of Agricultural and Biological Engineering, 13(1), 42–46. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5030

Issue

Vol. 13 No. 1 (2020): IJABE

Section

Animal, Plant and Facility Systems

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