Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking
Abstract
:Simple Summary
Abstract
1. Introduction
2. Understanding Feather Pecking Through “-Omics” Approaches
2.1. Genomics Approach
2.2. Other “-Omics” Approaches
3. Sensor Technologies
3.1. Radio Frequency Identification (RFID)
3.1.1. General Introduction to RFID
3.1.2. Passive RFID
3.1.3. Active RFID Systems (Ultra-Wideband)
3.1.4. Application of RFID to FP
3.2. Computer Vision
3.2.1. General Introduction to The Computer Vision Approach and Its Key-Aspects
- Scene-based: works better in controlled environments with uniform illumination and scene parameters that could be easily altered;
- Shape-based: utilizes geometrical characteristics of the object-to-find for example lines, points, edges and is usually applied as the number of filters scanning the image in a sliding-window manner. Heavily relies on image acquisition method, image quality, and number of objects to detect;
- Motion-based: uses the temporal difference between corresponding frames to find changes in pixel velocity, creating “shadow maps” with object’s positions or activity indices; mostly used for area supervision;
- Appearance-based: utilizes such image properties as the number of channels, color intensity, hue, to detect the desired object. Heavily relies on image acquisition method and number of objects to detect.
3.2.2. Overview of Recent Developments in CV Related to Monitoring Larger Groups of Individuals
- The Field of View (FOV) of the Kinect V1 camera is relatively small (for example, 1.5 × 1.5 m at 2-m height). For large rooms and areal scanning, the camera would need to be moved around at a pre-defined speed to capture the full area of interest, which could affect both scene reconstruction from depth data as well as object detection. An alternative would be to have several cameras with overlapping FOV. However, linking the cameras together is still a challenge;
- Computational costs (due to image size and algorithm complexity) and limits to cable extension (as the camera requires a stable bandwidth for data flow) could be a problem if used on farm;
- The features defining welfare/health-related issues should be consistent across variable environments (e.g., different lightning conditions, levels of dust) and be of a type that allow fast processing in scenes with high stocking density.
4. Use of Sensor Technologies in Different Applications
5. Identification of Indicator Traits from Sensor and “-Omics” Technologies
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Type of Camera | Camera Subtype | Factors to Consider during the Recording | Factors Potentially Affecting Data Quality |
---|---|---|---|
Infrared (IR) | Operating range (affects the sensitivity of measurement) Near-IR; Short wave (SWIR); Medium wave (MWIR); Long wave (LWIR); | Environmental factors (e.g., temperature, humidity, dust particles), angle and distance of measurement, reflective properties of the measured object (e.g., dry or wet feathers) | |
2D | Network/IP cameras Cheap, used for surveillance, often robust and have protected casing, compress images, great range of in-built functions, embedded hardware is capable of some processing, remote access for multiple users; | Sensor type CMOS: Less expensive, high dynamic range (HDR), no blooming, making it perfect for varying objects and scenes with varying illumination, low power consumption; CCD: More expensive, can capture more light, lower noise factor, higher fill and color reliability, making it perfect for less dynamic tasks and low-light conditions; Type of image Monochrome: Higher sensitivity and detail level, more difficult to process and analyze if the scene illumination varies; Color: RGB channels, possible to enhance based on pixel values, good for scenes with varying conditions; Frames Rate (FPS) capacity High Frame Rate = more images captured per second = faster sensor = higher data volume; Trade-off between desired complexity of behavior/parameter and image size; Resolution Resolution = (Object Size/Detail size)² | Camera calibration and scene reconstruction Scene Illumination Number of animals and background clutter Lens properties Occlusion Scale Object “deformation” |
Industrial/Computer vision (CV) cameras Area Scan: Allows in-depth scene inspection as the image is recorded and processed “at once”; Line Scan: High-speed tasks, quality control, data are captured line-by-line and then reconstructed into the whole image; Expensive, images recorded in “raw” format and transferred to PC for processing, complex infrastructure for setup; | |||
3D | Stereo cameras Cheap, two or more lenses, depth range for recording depends on the distance between lenses, limited extensibility; | ||
Continuous Wave Time of Flight (ToF) cameras Relatively cheap, emits continuous wave modulated light which returns back with depth data, usually lower sensor resolution, a wide range of functions (e.g., motion capture, scene reconstruction, object scanning); | |||
Structured Light Cameras Cheapest, use an active stereovision approach (the known IR-pattern is projected onto the object of interest, and the depth data is calculated based on distortion occurring on the collision of IR-pattern and objects’ shape) |
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Ellen, E.D.; van der Sluis, M.; Siegford, J.; Guzhva, O.; Toscano, M.J.; Bennewitz, J.; van der Zande, L.E.; van der Eijk, J.A.J.; de Haas, E.N.; Norton, T.; et al. Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking. Animals 2019, 9, 108. https://doi.org/10.3390/ani9030108
Ellen ED, van der Sluis M, Siegford J, Guzhva O, Toscano MJ, Bennewitz J, van der Zande LE, van der Eijk JAJ, de Haas EN, Norton T, et al. Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking. Animals. 2019; 9(3):108. https://doi.org/10.3390/ani9030108
Chicago/Turabian StyleEllen, Esther D., Malou van der Sluis, Janice Siegford, Oleksiy Guzhva, Michael J. Toscano, Jörn Bennewitz, Lisette E. van der Zande, Jerine A. J. van der Eijk, Elske N. de Haas, Tomas Norton, and et al. 2019. "Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking" Animals 9, no. 3: 108. https://doi.org/10.3390/ani9030108