Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Wang, Zhaojun; Lin, Congtian; Yan, Han; Wang, Zhaosheng; Ji, Liqiang

doi:10.3390/ani11051263

Cited by 8 publications

(5 citation statements)

References 70 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a huge amount of decrease in the bird species, and it has a negative impact on both biodiversity and human lives. [18] Habitat Ensemble models are better than DenseNet201 and ResNet152v2 models.…”

Section: Preserve the Bird Speciesmentioning

confidence: 93%

See 1 more Smart Citation

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Manna¹,

Upasani²,

Jadhav³

et al. 2023

IJACSA

View full text Add to dashboard Cite

With the technological progress of human beings, more and more animal and bird species are being endangered and sometimes even going to the verge of extinction. However, the existence of birds is highly beneficial for human civilization as birds help in pollination, destroying harmful insects for crops, etc. To ensure the healthy co-existence of all species along with human beings, almost all advanced countries have taken up some conservation measures for endangered species. To ensure conservation, the first step is to identify the species of birds found in different locations. Deep learning-based techniques are best suited for the automated identification of bird species from the captured images. In this paper, a Convolutional Neural Network based bird image identification methodology has been proposed. Four different transfer learning-based architectures, namely Resnet152V2, Inception V3, Densenet201, and MobileNetV2 have been used for bird image classification and identification. The models have been trained using 58388 images belonging to 400 species of birds, and the models have been tested using 2000 images belonging to 400 species of birds. Out of these four models, Resnet152V2 and DenseNet201 performed comparatively well. The accuracy of Resnet152V2 was highest at 95.45%, but it faced a large loss of 0.8835. But based on the results, even though DenseNet201 had an accuracy of 95.05%, it faced less loss i.e., of 0.6854. The results show that the DenseNet201 model can further be used for real-life bird image classification.

show abstract

Section: Preserve the Bird Speciesmentioning

confidence: 93%

“…A paper that uses a deep convolutional neural network [18] to identify birds' images was published. The author used habitat elements of bird images.…”

Section: Literature Reviewmentioning

confidence: 99%

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Manna¹,

Upasani²,

Jadhav³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…We design the machine learning model based on a CNN [26]. CNN-based models are widely used in the field of image processing.…”

Section: Signal Processing and Machine Learningmentioning

confidence: 99%

Forest quality assessment based on bird sound recognition using convolutional neural networks

Effendy

Ruhyadi

Pratama

et al. 2022

IJECE

View full text Add to dashboard Cite

Deforestation in Indonesia is in a status that is quite alarming. From year to year, deforestation is still happening. The decline in fauna and the diminishing biodiversity are greatly affected by deforestation. This paper proposes a bioacoustics-based forest quality assessment tool using Nvidia Jetson Nano and convolutional neural networks (CNN). The device, named GamaDet, is a portable physical product based on the microprocessor and equipped with a microphone to record the sounds of birds in the forest and display the results of their analysis. In addition, a Google Collaboratorybased GamaNet digital product is also proposed. GamaNet requires forest recording audio files to be further analyzed into a forest quality index. Testing the forest recording for 60 seconds at an arboretum forest showed that both products could work well. The GamaDet takes 370 seconds, while the GamaNet takes 70 seconds to process the audio data into a forest quality index and a list of detected birds.

show abstract

“…Others, as Chilson et al (2019) and Wang et al (2021), identified birds' habitat elements using radar data and photographic images, respectively. Deneu et al (2021) used CNNs to improve species distribution modeling by capturing complex spatial structures of the environment.…”

Section: Introductionmentioning

confidence: 99%

“…Su et al ( 2018 ) used CNNs (and Support Vector Machine) with satellite images to model the habitat suitability for a migratory geese species. Others, as Chilson et al ( 2019 ) and Wang et al ( 2021 ), identified birds' habitat elements using radar data and photographic images, respectively. Deneu et al ( 2021 ) used CNNs to improve species distribution modeling by capturing complex spatial structures of the environment.…”

Section: Introductionmentioning

confidence: 99%

Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding

Garcia‐Quintas,

Roy,

Barbraud

et al. 2023

Ecology and Evolution

View full text Add to dashboard Cite

The way animals select their breeding habitat may have great impacts on individual fitness. This complex process depends on the integration of information on various environmental factors, over a wide range of spatiotemporal scales. For seabirds, breeding habitat selection integrates both land and sea features over several spatial scales. Seabirds explore these features prior to breeding, assessing habitats' quality. However, the information‐gathering and decision‐making process by seabirds when choosing a breeding habitat remains poorly understood. We compiled 49 historical records of larids colonies in Cuba from 1980 to 2020. Then, we predicted potentially suitable breeding sites for larids and assessed their breeding macrohabitat selection, using deep and machine learning algorithms respectively. Using a convolutional neural network and Landsat satellite images we predicted the suitability for nesting of non‐monitored sites of this archipelago. Furthermore, we assessed the relative contribution of 18 land‐ and marine‐based environmental covariates describing macrohabitats at three spatial scales (i.e. 10, 50 and 100 km) using random forests. Convolutional neural network exhibited good performance at training, validation and test (F1‐scores >85%). Sites with higher habitat suitability (p > .75) covered 20.3% of the predicting area. Larids breeding macrohabitats were sites relatively close to main islands, featuring sparse vegetation cover and high chlorophyll‐a concentration at sea in 50 and 100 km around colonies. Lower sea surface temperature at larger spatial scales was determinant to distinguish the breeding from non‐breeding sites. A more comprehensive understanding of the seabird breeding macrohabitats selection can be reached from the complementary use of convolutional neural networks and random forest models. Our analysis provides crucial knowledge in tropical regions that lack complete and regular monitoring of seabirds' breeding sites.

show abstract

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Cited by 8 publications

References 70 publications

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Forest quality assessment based on bird sound recognition using convolutional neural networks

Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding

Contact Info

Product

Resources

About