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  1. Article: Short Communication: The effect of age on young sheep biometric identification

    Hitelman, A. / Edan, Y. / Godo, A. / Berenstein, R. / Lepar, J. / Halachmi, I.

    Animal. 2022 Feb., v. 16, no. 2

    2022  

    Abstract: Biometric identification provides an important tool for precision livestock farming. This study investigates the effect of weight gain and sheep maturation on recognition performance. Sheep facial identification was implemented using two convolutional ... ...

    Abstract Biometric identification provides an important tool for precision livestock farming. This study investigates the effect of weight gain and sheep maturation on recognition performance. Sheep facial identification was implemented using two convolutional neural network (CNN) called Faster R-CNN, and ResNet50V2, equipped with the state-of-art Additive Angular Margin (ArcFace) loss function. The identification model was tested on 47 young sheep at different stages, during a 3-month growth period, when they were between 2 and 5 months old, throughout which the sheep gained approximately 30 kilograms in weight. Results revealed that when the model was trained and tested on images of sheep aged 2 months, the average accuracy of the group was 95.4%, compared with 91.3% when trained on images of sheep aged 2 months but tested on images of sheep aged 5 months.
    Keywords biometry ; neural networks ; sheep ; weight gain
    Language English
    Dates of publication 2022-02
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 2257920-5
    ISSN 1751-732X ; 1751-7311
    ISSN (online) 1751-732X
    ISSN 1751-7311
    DOI 10.1016/j.animal.2021.100452
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 7718: Show issues

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  2. Article ; Online: Short Communication: The effect of age on young sheep biometric identification.

    Hitelman, A / Edan, Y / Godo, A / Berenstein, R / Lepar, J / Halachmi, I

    Animal : an international journal of animal bioscience

    2022  Volume 16, Issue 2, Page(s) 100452

    Abstract: Biometric identification provides an important tool for precision livestock farming. This study investigates the effect of weight gain and sheep maturation on recognition performance. Sheep facial identification was implemented using two convolutional ... ...

    Abstract Biometric identification provides an important tool for precision livestock farming. This study investigates the effect of weight gain and sheep maturation on recognition performance. Sheep facial identification was implemented using two convolutional neural network (CNN) called Faster R-CNN, and ResNet50V2, equipped with the state-of-art Additive Angular Margin (ArcFace) loss function. The identification model was tested on 47 young sheep at different stages, during a 3-month growth period, when they were between 2 and 5 months old, throughout which the sheep gained approximately 30 kilograms in weight. Results revealed that when the model was trained and tested on images of sheep aged 2 months, the average accuracy of the group was 95.4%, compared with 91.3% when trained on images of sheep aged 2 months but tested on images of sheep aged 5 months.
    MeSH term(s) Aging ; Animals ; Biometric Identification ; Neural Networks, Computer ; Sheep
    Language English
    Publishing date 2022-01-27
    Publishing country England
    Document type Journal Article
    ZDB-ID 2257920-5
    ISSN 1751-732X ; 1751-7311
    ISSN (online) 1751-732X
    ISSN 1751-7311
    DOI 10.1016/j.animal.2021.100452
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 7718: Show issues

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  3. Article: Biometric identification of sheep via a machine-vision system

    Hitelman, Almog / Edan, Yael / Godo, Assaf / Berenstein, Ron / Lepar, Joseph / Halachmi, Ilan

    Computers and electronics in agriculture. 2022 Mar., v. 194

    2022  

    Abstract: This paper describes a sheep biometric identification system based on facial images. A machine vision system and deep learning model were developed and applied for animal identification. The system included two 8-MegaPixels cameras installed in a ... ...

    Abstract This paper describes a sheep biometric identification system based on facial images. A machine vision system and deep learning model were developed and applied for animal identification. The system included two 8-MegaPixels cameras installed in a controlled water trough adapted to work with NVIDIA Jetson Nano-embedded system-on-module (SoM). Data from 81 Assaf breed sheep, aged two to three months, from two different groups of sheep, were collected over a period of two weeks. The biometric identification model included two steps: face detection and classification. In order to locate and localize the sheep's face in an image, the Faster R-CNN deep learning object detection algorithm was applied. The detected face was provided as input to seven different classification models. Different transfer learning methods were examined. The best performance was obtained using a ResNet50V2 model with the state-of-art ArcFace loss function. The identification system resulted in average accuracies of 95% for the two groups tested. When applying transfer learning methods, average identification accuracies improved to 97% in both groups, and the training process was accomplished in half the time. The newly developed system proves the feasibility of individual biometric identification of sheep on commercial farms.
    Keywords agriculture ; algorithms ; animal identification ; biometry ; computer vision ; electronics ; face ; models ; sheep ; water troughs
    Language English
    Dates of publication 2022-03
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 395514-x
    ISSN 0168-1699
    ISSN 0168-1699
    DOI 10.1016/j.compag.2022.106713
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 4541: Show issues

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  4. Article: Chromosomal diseases in "Drosophila willistoni" Sturtevant.

    da Cunha, A B / França, Z M / Gonçalves, A M / Hitelman, A / Garrido, M

    Revista brasileira de biologia

    1967  Volume 27, Issue 2, Page(s) 113–123

    MeSH term(s) Chromosome Aberrations ; Drosophila/cytology
    Language English
    Publishing date 1967-08
    Publishing country Brazil
    Document type Journal Article
    ZDB-ID 421594-1
    ISSN 0034-7108
    ISSN 0034-7108
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 652: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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