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dc.contributor.authorWang, Jintao
dc.contributor.authorLiu, Longshen
dc.contributor.authorLu, Mingzhou
dc.contributor.authorOkinda, Cedric
dc.contributor.authorLovarelli, Daniela
dc.contributor.authorGuarino, Marcella
dc.contributor.authorShen, Mingxia
dc.date.accessioned2023-01-10T05:47:06Z
dc.date.available2023-01-10T05:47:06Z
dc.date.issued2022-12-21
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fphy.2022.1047077/full
dc.identifier.urihttps://doi.org/10.3389/fphy.2022.1047077
dc.identifier.urihttp://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/2152
dc.description.abstractRespiratory rate is an indicator of a broilers’ stress and health status, thus, it is essential to detect respiratory rate contactless and stress-freely. This study proposed an estimation method of broiler respiratory rate by deep learning and machine vision. Experiments were performed at New Hope (Shandong Province, P. R. China) and Wen’s group (Guangdong Province, P. R. China), and a total of 300 min of video data were collected. By separating video frames, a data set of 3,000 images was made, and two semantic segmentation models were trained. The single-channel Euler video magnification algorithm was used to amplify the belly fluctuation of the broiler, which saved 55% operation time compared with the traditional Eulerian video magnification algorithm. The contour features significantly related to respiration were used to obtain the signals that could estimate broilers’ respiratory rate. Detrending and band-pass filtering eliminated the influence of broiler posture conversion and motion on the signal. The mean absolute error, root mean square error, average accuracy of the proposed respiratory rate estimation technique for broilers were 3.72%, 16.92%, and 92.19%, respectively.en_US
dc.language.isoenen_US
dc.publisherFrontiers in Physicsen_US
dc.subjectThe estimation of broiler respiration rate based on the semantic segmentation and video amplificationen_US
dc.titleThe estimation of broiler respiration rate based on the semantic segmentation and video amplificationen_US
dc.typeArticleen_US


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