Literature DB >> 25071227

Performance of an image analysis processing system for hen tracking in an environmental preference chamber.

Mohammad Amin Kashiha1, Angela R Green2, Tatiana Glogerley Sales2, Claudia Bahr3, Daniel Berckmans3, Richard S Gates2.   

Abstract

Image processing systems have been widely used in monitoring livestock for many applications, including identification, tracking, behavior analysis, occupancy rates, and activity calculations. The primary goal of this work was to quantify image processing performance when monitoring laying hens by comparing length of stay in each compartment as detected by the image processing system with the actual occurrences registered by human observations. In this work, an image processing system was implemented and evaluated for use in an environmental animal preference chamber to detect hen navigation between 4 compartments of the chamber. One camera was installed above each compartment to produce top-view images of the whole compartment. An ellipse-fitting model was applied to captured images to detect whether the hen was present in a compartment. During a choice-test study, mean ± SD success detection rates of 95.9 ± 2.6% were achieved when considering total duration of compartment occupancy. These results suggest that the image processing system is currently suitable for determining the response measures for assessing environmental choices. Moreover, the image processing system offered a comprehensive analysis of occupancy while substantially reducing data processing time compared with the time-intensive alternative of manual video analysis. The above technique was used to monitor ammonia aversion in the chamber. As a preliminary pilot study, different levels of ammonia were applied to different compartments while hens were allowed to navigate between compartments. Using the automated monitor tool to assess occupancy, a negative trend of compartment occupancy with ammonia level was revealed, though further examination is needed. ©2014 Poultry Science Association Inc.

Entities:  

Keywords:  animal behavior; choice test; image processing; laying hen; occupancy analysis

Mesh:

Substances:

Year:  2014        PMID: 25071227     DOI: 10.3382/ps.2014-04078

Source DB:  PubMed          Journal:  Poult Sci        ISSN: 0032-5791            Impact factor:   3.352


  6 in total

1.  Accuracy of image analysis for linear zoometric measurements in dromedary camels.

Authors:  Djalel Eddine Gherissi; Ramzi Lamraoui; Faycel Chacha; Semir Bechir Suheil Gaouar
Journal:  Trop Anim Health Prod       Date:  2022-07-20       Impact factor: 1.893

2.  Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine.

Authors:  Cheng Wang; Hongqian Chen; Xuebin Zhang; Chaoying Meng
Journal:  J Anim Sci Biotechnol       Date:  2016-10-12

3.  Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations.

Authors:  Mauro Zaninelli; Veronica Redaelli; Fabio Luzi; Malcolm Mitchell; Valentino Bontempo; Donata Cattaneo; Vittorio Dell'Orto; Giovanni Savoini
Journal:  Sensors (Basel)       Date:  2018-01-05       Impact factor: 3.576

4.  A Monitoring System for Laying Hens That Uses a Detection Sensor Based on Infrared Technology and Image Pattern Recognition.

Authors:  Mauro Zaninelli; Veronica Redaelli; Fabio Luzi; Valentino Bontempo; Vittorio Dell'Orto; Giovanni Savoini
Journal:  Sensors (Basel)       Date:  2017-05-24       Impact factor: 3.576

5.  Computer-Vision-Based Indexes for Analyzing Broiler Response to Rearing Environment: A Proof of Concept.

Authors:  Juliana Maria Massari; Daniella Jorge de Moura; Irenilza de Alencar Nääs; Danilo Florentino Pereira; Tatiane Branco
Journal:  Animals (Basel)       Date:  2022-03-28       Impact factor: 2.752

6.  Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review.

Authors:  Kaitlin Wurtz; Irene Camerlink; Richard B D'Eath; Alberto Peña Fernández; Tomas Norton; Juan Steibel; Janice Siegford
Journal:  PLoS One       Date:  2019-12-23       Impact factor: 3.240

  6 in total

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