Birds have an enormous impact on the ecosystem and various information about an area can be produced from the bird species prevalent in it. Ornithologists have been monitoring many bird species in order to gain a better understanding of their habitats. Image classification is a popular technique that can be used in order to classify different species of birds. An image classification approach for bird species identification has been proposed in this work. The approach is based on transfer learning using the pre-trained CNN architecture VGG16. The model is trained on more than 45000 images of 300 different species of birds, many of which are endangered. The model is able to achieve 96.44% accuracy on the training dataset while achieving a maximum accuracy of 96.8% on the test dataset.

Topics
Learning and learning models, Birds, Ecology
1.
G.
Rane
,
P. P.
Rege
 and
R.
Patole
, “
Bird Classification based on Bird Sounds
,” 2021
8th International Conference on Signal Processing and Integrated Networks (SPIN)
,
2021
, pp.
1143
–
1147
,
https://doi.org/10.1109/SPIN52536.2021.9566071
.
Google Scholar
 
2.
S.
Debnath
,
P. P.
Roy
,
A. A.
Ali
 and
M. A.
Amin
, “
Identification of bird species from their singing
,” 2016
5th International Conference on Informatics, Electronics and Vision (ICIEV)
,
2016
, pp.
182
–
186
,
https://doi.org/10.1109/ICIEV.2016.7759992
.
Google Scholar
 
3.
Kahl
,
Stefan
 &
Clapp
,
Mary
 &
Hopping
,
W.
 &
Goëau
,
Hervé
 &
Glotin
,
Hervé;
 &
Planqué
,
Robert
 &
Vellinga
,
Willem-Pier
 &
Joly
,
Alexis
. (
2020
).
Overview of BirdCLEF 2020
:
Bird Sound Recognition in Complex Acoustic Environments.
Google Scholar
 
4.
Fritzler
,
Andreas
 &
Koitka
,
Sven
 &
Friedrich
,
Christoph
. (
2017
).
Recognizing Bird Species in Audio Files Using Transfer Learning
.
Google Scholar
 
5.
K. M.
Ragib
,
R. T.
Shithi
,
S. A.
Haq
,
M.
Hasan
,
K. M.
Sakib
 and
T.
Farah
, “
PakhiChini: Automatic Bird Species Identification Using Deep Learning
,” 2020
Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4
), 2020, pp.
1
–
6
,
https://doi.org/10.1109/WorldS450073.2020.9210259
.
Crossref
Search ADS
 
6.
A.
Marini
,
J.
Facon
 and
A. L.
Koerich
, “
Bird Species Classification Based on Color Features
,” 2013
IEEE International Conference on Systems, Man, and Cybernetics
, 2013, pp.
4336
–
4341
,
https://doi.org/10.1109/SMC.2013.740
.
7.
B.
Qiao
,
Z.
Zhou
,
H.
Yang
 and
J.
Cao
, “
Bird species recognition based on SVM classifier and decision tree
,” 2017
First International Conference on Electronics Instrumentation & Information Systems (EIIS),
2017
, pp.
1
–
4
,
https://doi.org/10.1109/EIIS.2017.8298548
.
Google Scholar
 
8.
A.
Marini
,
A. J.
Turatti
,
A. S.
Britto
 and
A. L.
Koerich
, “
Visual and acoustic identification of bird species
,”
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
,
2015
, pp.
2309
–
2313
,
https://doi.org/10.1109/ICASSP.2015.7178383
.
Google Scholar
 
9.
P.
Datar
,
K.
Jain
 and
B.
Dhedhi
, “
Detection of Birds in the Wild using Deep Learning Methods
,” 2018
4th International Conference for Convergence in Technology (I2CT)
,
2018
, pp.
1
–
4
,
https://doi.org/10.1109/I2CT42659.2018.9057933
.
Google Scholar
 
10.
Á.
Incze
,
H.
Jancsó
,
Z.
Szilágyi
,
A.
Farkas
 and
C.
Sulyok
, “
Bird Sound Recognition Using a Convolutional Neural Network
,” 2018
IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY)
,
2018
, pp.
000295
–
000300
,
https://doi.org/10.1109/SISY.2018.8524677
.
Google Scholar
 
11.
B.
Chandu
,
A.
Munikoti
,
K. S.
Murthy
,
G.
Murthy
 V. and
C.
Nagaraj
, “
Automated Bird Species Identification using Audio Signal Processing and Neural Networks
,” 2020
International Conference on Artificial Intelligence and Signal Processing (AISP)
,
2020
, pp.
1
–
5
,
https://doi.org/10.1109/AISP48273.2020.9073584
.
Google Scholar
 
12.
N.
Bold
,
C.
Zhang
 and
T.
Akashi
, “
Bird Species Classification with Audio-Visual Data using CNN and Multiple Kernel Learning
,” 2019
International Conference on Cyberworlds (CW)
,
2019
, pp.
85
–
88
,
https://doi.org/10.1109/CW.2019.00022
.
Google Scholar
 
13.
Z.
Qiu
,
X.
Zhu
,
D.
Shi
 and
Y.
Kuang
, “
Recognition of Transmission Line Related Bird Species Based on Image Feature Extraction and Support Vector Machine
,” 2020
IEEE International Conference on High Voltage Engineering and Application (ICHVE)
,
2020
, pp.
1
–
4
,
https://doi.org/10.1109/ICHVE49031.2020.9279508
.
Google Scholar
 
14.
Z.
Ge
,
C.
McCool
,
C.
Sanderson
,
A.
Bewley
,
Z.
Chen
 and
P.
Corke
, “
Fine-grained bird species recognition via hierarchical subset learning
,” 2015
IEEE International Conference on Image Processing (ICIP)
,
2015
, pp.
561
–
565
,
https://doi.org/10.1109/ICIP.2015.7350861
.
Google Scholar
 
15.
H.
Seth
,
R.
Bhatia
 and
P.
Rajan
, “
Feature Learning for Bird Call Clustering
,”
2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)
,
2018
, pp.
72
–
76
,
https://doi.org/10.1109/ICIINFS.2018.8721418
.
Google Scholar
 
16.
R.
Roslan
,
N. A.
Nazery
,
N.
Jamil
 and
R.
Hamzah
, “
Color-based bird image classification using Support Vector Machine
,”
2017 IEEE 6th Global Conference on Consumer Electronics (GCCE),
2017
, pp.
1
–
5
,
https://doi.org/10.1109/GCCE.2017.8229492
.
Google Scholar
 
17.
J.
Xie
,
J.
Yang
,
C.
Ding
 and
W.
Li
, “
High Accuracy Individual Identification Model of Crested Ibis (Nipponia Nippon) Based on Autoencoder With Self-Attention
,” in
IEEE Access
, vol.
8
, pp.
41062
–
41070
,
2020
,
https://doi.org/10.1109/ACCESS.2020.2973243
.
Google Scholar
Crossref
Search ADS
 
18.
P.
Rai
,
V.
Golchha
,
A.
Srivastava
,
G.
Vyas
 and
S.
Mishra
, “
An automatic classification of bird species using audio feature extraction and support vector machines
,” 2016
International Conference on Inventive Computation Technologies (ICICT)
,
2016
, pp.
1
–
5
,
https://doi.org/10.1109/INVENTIVE.2016.7823241
.
Google Scholar
 
19.
J.
Xie
,
K.
Hu
,
M.
Zhu
,
J.
Yu
 and
Q.
Zhu
, “
Investigation of Different CNN-Based Models for Improved Bird Sound Classification
,” in
IEEE Access
, vol.
7
, pp.
175353
–
175361
,
2019
,
https://doi.org/10.1109/ACCESS.2019.2957572
.
Google Scholar
Crossref
Search ADS
 
20.
M. S.
Towhid
 and
M. M.
Rahman
, “
Spectrogram segmentation for bird species classification based on temporal continuity
,” 2017
20th International Conference of Computer and Information Technology (ICCIT)
,
2017
, pp.
1
–
4
,
https://doi.org/10.1109/ICCITECHN.2017.8281775
.
Google Scholar
 
21.
Simonyan
,
Karen
 and
Andrew
Zisserman
. “
Very Deep Convolutional Networks for Large-Scale Image Recognition
.” CoRR abs/1409.1556 (
2015
)
Google Scholar
 
22.
Islam
,
Shazzadul
,
Sabit Ibn Ali
Khan
,
Md Minhazul
Abedin
,
Khan Mohammad
Habibullah
, and
Amit Kumar
Das
. “
Bird species classification from an image using VGG-16 network
.” In
Proceedings of the 2019 7th international conference on computer and communications management
, pp.
38
–
42
.
2019
.
Google Scholar
 
23.
Noumida
,
A.
, and
Rajeev
Rajan
. “
Deep Learning-based Automatic Bird Species Identification from Isolated Recordings
.” In 2021
8th International Conference on Smart Computing and Communications (ICSCC)
, pp.
252
–
256
. IEEE,
2021
.
Google Scholar
 
24.
Wei
,
Xiu-Shen
,
Chen-Wei
Xie
,
Jianxin
Wu
, and
Chunhua
Shen
. “
Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization
.”
Pattern Recognition
76
(
2018
):
704
–
714
.
https://doi.org/10.1016/j.patcog.2017.10.002
Google Scholar
Crossref
Search ADS
 
This content is only available via PDF.
©2023 Authors. Published by AIP Publishing.
2023
Author(s)
You do not currently have access to this content.