RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review

Abstract

The growing demands of sustainable, efficient, and welfare-conscious pig husbandry have necessitated the adoption of advanced technologies. Among these, RGB imaging and machine vision technology may offer a promising solution for early disease detection and proactive disease management in advanced pig husbandry practices. This review explores innovative applications for monitoring disease symptoms by assessing features that directly or indirectly indicate disease risk, as well as for tracking body weight and overall health. Machine vision and image processing algorithms enable for the real-time detection of subtle changes in pig appearance and behavior that may signify potential health issues. Key indicators include skin lesions, inflammation, ocular and nasal discharge, and deviations in posture and gait, each of which can be detected non-invasively using RGB cameras. Moreover, when integrated with thermal imaging, RGB systems can detect fever, a reliable indicator of infection, while behavioral monitoring systems can track abnormal posture, reduced activity, and altered feeding and drinking habits, which are often precursors to illness. The technology also facilitates the analysis of respiratory symptoms, such as coughing or sneezing (enabling early identification of respiratory diseases, one of the most significant challenges in pig farming), and the assessment of fecal consistency and color (providing valuable insights into digestive health). Early detection of disease or poor health supports proactive interventions, reducing mortality and improving treatment outcomes. Beyond direct symptom monitoring, RGB imaging and machine vision can indirectly assess disease risk by monitoring body weight, feeding behavior, and environmental factors such as overcrowding and temperature. However, further research is needed to refine the accuracy and robustness of algorithms in diverse farming environments. Ultimately, integrating RGB-based machine vision into existing farm management systems could provide continuous, automated surveillance, generating real-time alerts and actionable insights; these can support data-driven disease prevention strategies, reducing the need for mass medication and the development of antimicrobial resistance.

Keywords: Artificial intelligence; Machine vision; Pig behavior pattern; Pig health monitoring; Smart livestock production.

Fig. 1.. A schematic showing the fundamental processing steps in machine vision from initial image acquisition through to final image interpretation and analysis.

Fig. 2.. An illustration of electromagnetic bands ranging from radio waves to gamma rays, highlighting the visible light spectrum.

Fig. 3.. Various commercial RGB imaging sensors used in pig farms for disease symptom detection and behavior and activity monitoring.

(A) Raspberry Pi camera module V2, (B) Mi 360 webcam, (C) EXview HAD CCD, (D) Microsoft Kinect v1, (E) Microsoft Kinect v2, (F) VIVOTEK IB836BA-HF3, (g) Hikevision DS-2CD2142FWD-I, (H) Microsoft OEM Life Cam, (I) Intel RealSense, (J) FL3-U3-88S2C-C (K) IFM O3D313, (L) IPC-HFW1230S-S4, (M) TOF 640, (N) Hero 4, and (O) Nikon D5100.

Fig. 4.. A schematic representation of image pre-processing, feature extraction, segmentation, and classification techniques used for the analysis of data in the context of RGB images.

Fig. 5.. Various image processing techniques applied to RGB images captured in pig farm environments.

(A) Image binarization, (B) background segmentation, (C) image masking, (D) thresholding of pig image, (E) masking and cropping, (F) color space conversions (RGB, HSV, LAB, YCbCr, xyz, and YUV) for feature extraction, (G) Otsu segmentation, (H) histogram equalization, and (I) pig body skeleton analysis.

Fig. 6.. Pig behavior detection and analysis using image processing fusion techniques.

(A) Ellipse-based segmentation, (B) Delaunay triangulation on pig body, and (C) combination of ellipse and Delaunay triangulation for detecting laying patterns.

Fig. 7.. Detailed representation of 3D reconstruction of pig point cloud from RGB images for body weight and movement analysis.

Fig. 8.. Monitoring, detection, and tracking of pig diseases and behavior using machine learning applications in pig farms.

(A) Posture detection and segmentation, (B) detection and tracking, (C) recognition of different body parts, (D) detection of disease conditions, and (E) tracking and counting in farm settings using RGB image data.