Infrared proximity measurement system development and validation for classifying sow posture

dc.contributor.author Ramirez, Brett
dc.contributor.author Smith, Benjamin
dc.contributor.author Hoff, Steven
dc.contributor.author Ramirez, Brett
dc.contributor.author Hoff, Steven
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2019-09-21T02:09:51.000
dc.date.accessioned 2020-06-29T22:35:33Z
dc.date.available 2020-06-29T22:35:33Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.embargo 2018-01-01
dc.date.issued 2019-01-01
dc.description.abstract <p>The rapidly progressing field of precision livestock farming is becoming increasingly dependent on the utilization of camera technology. Integration of camera technology involves substantial intellectual input and computational power to acquire, process, and interpret images in real-time. Further, cameras and the necessary computational power can be cost-prohibitive and subsequently, become a constraint for application in a commercial livestock and poultry production systems. The purpose of this study is to develop an infrared proximity sensor based system to serve as a substitute a camera system to perform real-time monitoring of sow posture in farrowing stalls for a potentially lower cost and computational power. Monitoring sow posture can provide producers an indicator of farrowing and aid in evaluating sow demeanor during lactation. During the development of this system the long range infrared (IR) proximity sensors were individually calibrated, a sow posture algorithm was developed, and the IR-Sow Posture Detection System (IR-SoPoDS) system was evaluated in a commercial setting to a Kinect V2® camera for a range of sow postures. Average accuracy of the sow posture algorithm on the training data was found to be 96%. The overall accuracy of the IR-SoPoDS system across the three sow frame sizes were:87% (small), 90% (medium), and 89% (large). This IR-SoPoDS system shows a strong promise for further development for sow posture and behavior detection in the farrowing stall environment.</p>
dc.description.comments <p>This presentation is published as Smith, Benjamin C., Brett C. Ramirez, and Steven J. Hoff. "Infrared proximity measurement system development and validation for classifying sow posture." ASABE Annual International Meeting. Boston, MA. July 7-10, 2019. Paper No. 1900327. DOI: <a href="http://dx.doi.org/10.13031/aim.201900327" target="_blank">10.13031/aim.201900327</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_conf/581/
dc.identifier.articleid 1584
dc.identifier.contextkey 15040785
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/581
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/628
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/581/2019_RamirezBrett_InfraredProximity.pdf|||Sat Jan 15 01:01:44 UTC 2022
dc.source.uri 10.13031/aim.201900327
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords camera
dc.subject.keywords farrowing
dc.subject.keywords precision livestock farming
dc.subject.keywords swine
dc.title Infrared proximity measurement system development and validation for classifying sow posture
dc.type article
dc.type.genre presentation
dspace.entity.type Publication
relation.isAuthorOfPublication c146c15d-23b3-4d1f-b658-9490f1bbc761
relation.isAuthorOfPublication 98b46d48-66a2-4458-9b42-8c4aa050664d
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
File
Original bundle
Now showing 1 - 1 of 1
Name:
2019_RamirezBrett_InfraredProximity.pdf
Size:
100.75 KB
Format:
Adobe Portable Document Format
Description: