The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals

Abstract

Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human-machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs' feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors.

Keywords: cameras; early disease detection; pig behavior; pig identification; precision livestock farming.

Figure 1

Camera sensors in pig barn for identification, activity detection, and early disease detection; a schematic of the current review.

Figure 2

(a) Facial identification system proposed by Hansen et al. in 2018 [43]; (b) live pig weight estimation using a binocular stereo system based on LabVIEW by Shi et al. [53]; (c) heart girth measurement and detection of a pig using point cloud data, proposed by Zhang et al. in 2020 [32].

Figure 3

The extraction process of aggression-associated moving pixels: (a) former infrared frame, (b) former depth frame, (c) latter infrared frame, (d) latter depth frame, (e) latter minus former depth frame (I1), (f) former minus latter depth frame (I2), (g) extraction result for moving pixels (I1 + I2), (h) binarization result, and (i) extraction result for aggression-associated moving pixels after applying the threshold setting created by Chen et al. [95].

Figure 4

(a) Thermal images of control, Salmonella enterica serovar Typhimurium-infected, and Escherichia coli-infected pigs taken by Islam [102]; (b) Delaunay triangulation method to identify pig lying patterns by Nasirahmadi et al. [78].