Automatic Identification of Marked Pigs in a Pen Using Image Pattern Recognition

Kashiha, Mohammadamin; Bahr, Claudia; Ott, Sanne; Moons, Christel; Niewold, Theodoor; Ödberg, Frank; Berckmans, Daniël

doi:10.1007/978-3-642-38628-2_24

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By applying marks on animals, computer vision can identify individuals in a group (Kashiha et al, 2013). However, it is not practical to mark a large group of birds with different colors or patterns.…”

Section: Introductionmentioning

confidence: 99%

An RFID-Based Automated Individual Perching Monitoring System for Group-Housed Poultry

KaiLao¹,

Liu²,

Xin³

et al. 2019

Transactions of the ASABE

View full text Add to dashboard Cite

Abstract. Perching is a natural behavior of poultry. Considerable research has been done to explore the relationship between group overall perch usage and well-being of laying hens. To quantify the potential cause-effect relationship on individual hens with different health or well-being status (e.g., keel bone deformation, foot pad lesion, social ranking) in a group, it is necessary to identify the perching behavior of individual birds. However, continuously monitoring individual birds in a group poses considerable challenges. To enable such research and potential commercial application, this study developed and validated a radio frequency identification (RFID) based automated perching monitoring system (APMS) for characterizing individual perching behaviors of group-housed poultry. The APMS consisted of an RFID module, a load cell module, and a round wooden perch. The RFID module was comprised of a high-frequency RFID reader, three customized rectangular antennas placed under the perch, and RFID transponders attached to the birds. The load cell module was comprised of a data acquisition system and two load cells supporting both ends of the perch. The daily number of perch visits (PV) and perching duration (PD) for individual birds were used to delineate perching behavior. Three identical experimental pens, five hens per pen, were equipped with the monitoring system. Two RFID transponders were attached to each hen (one per leg), and a distinct color was marked on the bird’s head for video or visual identification and validation. Performance of the APMS was validated by comparing the system outputs with manual observation and labeling over an entire day. Sensitivity and specificity of the system were shown to improve from 97.77% and 99.88%, respectively when using only the RFID module to 99.83% and 99.93% when incorporating weight information from the load cell module. Using this system, we conducted a preliminary trial on the relationship of perching behavior and body weight of laying hens, which revealed little effect of body weight but considerable variability in perching behavior among the individual hens. The study demonstrated that the APMS had excellent performance in measuring perching behaviors of individual birds in a group. The APMS offers great potential for delineating individual differences in perching behavior among hens with different social status or health conditions in a group setting. Keywords: Individual perching behavior, Laying hen, Load cell, Precision livestock farming, RFID, Welfare.

show abstract

Section: Introductionmentioning

confidence: 99%

An RFID-Based Automated Individual Perching Monitoring System for Group-Housed Poultry

KaiLao¹,

Liu²,

Xin³

et al. 2019

Transactions of the ASABE

View full text Add to dashboard Cite

show abstract

“…In dynamic problems, there is an additional important requirement relating to the algorithm's speed. This is a key parameter that determines the effectiveness of a given approach for permanent monitoring of a group of objects in real time [11]. A broader description of applications may be found in, e.g.…”

Section: Methodsmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rusek,

Tajduś

2020

View full text Add to dashboard Cite

The paper presents the results of analyses concerning a new approach to approximating trajectory of mining-induced horizontal displacements. Analyses aimed at finding the most effective method of fitting data to the trajectory of mining-induced horizontal displacements. Two variants were made. In the first, the direct least square fitting (DLSF) method was applied based on the minimization of the objective function defined in the form of an algebraic distance. In the second, the effectiveness of differential-free optimization methods (DFO) was verified. As part of this study, the following methods were tested: genetic algorithms (GA), differential evolution (DE) and particle swarm optimization (PSO). The data for the analysis were measurements of on the ground surface caused by the mining progressive work at face no. 698 of the German Prospel-Haniel mine. The results obtained were compared in terms of the fitting quality, the stability of the results and the time needed to carry out the calculations. Finally, it was found that the direct least square fitting (DLSF) approach is the most effective for the analyzed registration data base. In the authors' opinion, this is dictated by the angular range in which the measurements within a given measuring point oscillated.

show abstract

“…Applications to improve welfare and automate pig monitoring include video images to measure pen level activity [94,95] such as antagonistic behaviors, chasing, tail and flank biting, fighting, head-to-head knocks between pigs [30,96,97,98,99]. Sensor data differentiates lying patterns of pigs (thermal comfort behavior) [100,101], standing pigs from moving pigs [38], and lateral from sternal recumbency [102].…”

Section: Summary Of Remote Monitoring Technology Applications For mentioning

confidence: 99%

Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners

Benjamin

Yik

2019

Animals

140

132

View full text Add to dashboard Cite

The burgeoning research and applications of technological advances are launching the development of precision livestock farming. Through sensors (cameras, microphones and accelerometers), images, sounds and movements are combined with algorithms to non-invasively monitor animals to detect their welfare and predict productivity. In turn, this remote monitoring of livestock can provide quantitative and early alerts to situations of poor welfare requiring the stockperson’s attention. While swine practitioners’ skills include translation of pig data entry into pig health and well-being indices, many do not yet have enough familiarity to advise their clients on the adoption of precision livestock farming practices. This review, intended for swine veterinarians and specialists, (1) includes an introduction to algorithms and machine learning, (2) summarizes current literature on relevant sensors and sensor network systems, and drawing from industry pig welfare audit criteria, (3) explains how these applications can be used to improve swine welfare and meet current pork production stakeholder expectations. Swine practitioners, by virtue of their animal and client advocacy roles, interpretation of benchmarking data, and stewardship in regulatory and traceability programs, can play a broader role as advisors in the transfer of precision livestock farming technology, and its implications to their clients.

show abstract

Automatic Identification of Marked Pigs in a Pen Using Image Pattern Recognition

Cited by 7 publications

References 12 publications

An RFID-Based Automated Individual Perching Monitoring System for Group-Housed Poultry

An RFID-Based Automated Individual Perching Monitoring System for Group-Housed Poultry

Bulletin of the Polish Academy of Sciences: Technical Sciences

Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners

Contact Info

Product

Resources

About