Cite this DOI

JOHN BENNET, J. B. (2022). BIRD SPECIES IDENTIFICATION USING DEEP LEARNING. *Journal of Science & Technology*, *7*(9), 32–40. https://doi.org/10.46243/jst.2022.v7.i09.pp32-40

@article{johnbennet2022bird,
  author    = {JOHN BENNET, JOHN BENNET},
  title     = {{BIRD SPECIES IDENTIFICATION USING DEEP LEARNING}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {may},
  volume    = {7},
  number    = {9},
  pages     = {32--40},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i09.pp32-40},
  url       = {https://doi.org/10.46243/jst.2022.v7.i09.pp32-40},
  language  = {en},
  abstract  = {Now a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios appear in different sizes, shapes, colors, and angles from human perspective. Besides, the images present strong variations to identify the bird species more than audio classification. Also, human ability to recognize the birds through the images is more understandable. So this m ethod uses the Caltech-UCSD Birds 200 [CUB -200-2011] dataset for training as well as testing purpose. By using deep convolutional neural network (DCNN) algorithm an image converted into grey scale format to generate autograph by using tensor flow, where th e multiple nodes of comparison are generated. These different nodes are compared with the testing dataset and score sheet is obtained from it. After analyzing the score sheet it can predicate the required bird species by using highest score. Experimental a nalysis on dataset (i.e. Caltech -UCSD Birds 200 [CUB -2002011]) shows that algorithm achieves an accuracy of bird identification between 80\% and 90\%.The experimental study is done with the Ubuntu 16.04 operating system using a Tensor flow library}
}

TY  - JOUR
TI  - BIRD SPECIES IDENTIFICATION USING DEEP LEARNING
AU  - JOHN BENNET, JOHN BENNET
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/05/11/
VL  - 7
IS  - 9
SP  - 32
EP  - 40
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Now a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios appear in different sizes, shapes, colors, and angles from human perspective. Besides, the images present strong variations to identify the bird species more than audio classification. Also, human ability to recognize the birds through the images is more understandable. So this m ethod uses the Caltech-UCSD Birds 200 [CUB -200-2011] dataset for training as well as testing purpose. By using deep convolutional neural network (DCNN) algorithm an image converted into grey scale format to generate autograph by using tensor flow, where th e multiple nodes of comparison are generated. These different nodes are compared with the testing dataset and score sheet is obtained from it. After analyzing the score sheet it can predicate the required bird species by using highest score. Experimental a nalysis on dataset (i.e. Caltech -UCSD Birds 200 [CUB -2002011]) shows that algorithm achieves an accuracy of bird identification between 80% and 90%.The experimental study is done with the Ubuntu 16.04 operating system using a Tensor flow library
DO  - 10.46243/jst.2022.v7.i09.pp32-40
UR  - https://doi.org/10.46243/jst.2022.v7.i09.pp32-40
ER  -

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    "URL": "https://doi.org/10.46243/jst.2022.v7.i09.pp32-40",
    "title": "BIRD SPECIES IDENTIFICATION USING DEEP LEARNING",
    "source": "Smart Scholars DOI Registry",
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Now a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios appear in different sizes, shapes, colors, and angles from human perspective. Besides, the images present strong variations to identify the bird species more than audio classification. Also, human ability to recognize the birds through the images is more understandable. So this m ethod uses the Caltech-UCSD Birds 200 [CUB -200-2011] dataset for training as well as testing purpose. By using deep convolutional neural network (DCNN) algorithm an image converted into grey scale format to generate autograph by using tensor flow, where th e multiple nodes of comparison are generated. These different nodes are compared with the testing dataset and score sheet is obtained from it. After analyzing the score sheet it can predicate the required bird species by using highest score. Experimental a nalysis on dataset (i.e. Caltech -UCSD Birds 200 [CUB -2002011]) shows that algorithm achieves an accuracy of bird identification between 80% and 90%.The experimental study is done with the Ubuntu 16.04 operating system using a Tensor flow library",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i09.pp32-40 gives all four in one JSON answer.