Distribution of sika deer (Cervus nippon) and the bioclimatic impact on their habitats in South Korea

Dhakal, Thakur; Kim, Tae-Su; Kim, Seong-Hyeon; Tiwari, Shraddha; Kim, Jun-Young; Jang, Gab-Sue; Lee, Do-Hun

doi:10.1038/s41598-023-45845-2

Cited by 1 publication

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References 62 publications

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“…Yet, while significant research has focused on Endangered species such as the Bengal tiger, lion, and zebra listed on the IUCN Red List of Threatened Species [7][8][9], there has been a notable oversight concerning least-concern species like the Sika deer. This neglect is concerning as it contributes to both economic and biodiversity losses due to the unchecked growth of its population [10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Sika Deer Identification: Integrating CNN-Based Siamese Networks with SVM Classification

Sharma,

Timilsina,

Gautam

et al. 2024

Electronics

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Accurately identifying individual wildlife is critical to effective species management and conservation efforts. However, it becomes particularly challenging when distinctive features, such as spot shape and size, serve as primary discriminators, as in the case of Sika deer. To address this challenge, we employed four different Convolutional Neural Network (CNN) base models (EfficientNetB7, VGG19, ResNet152, Inception_v3) within a Siamese Network Architecture that used triplet loss functions for the identification and re-identification of Sika deer. Subsequently, we then determined the best-performing model based on its ability to capture discriminative features. From this model, we extracted embeddings representing the learned features. We then applied a Support Vector Machine (SVM) to these embeddings to classify individual Sika deer. We analyzed 5169 image datasets consisting of images of seven individual Sika deers captured with three camera traps deployed on farmland in Hokkaido, Japan, for over 60 days. During our analysis, ResNet152 performed exceptionally well, achieving a training accuracy of 0.97, and a validation accuracy of 0.96, with mAP scores for the training and validation datasets of 0.97 and 0.96, respectively. We extracted 128 dimensional embeddings of ResNet152 and performed Principal Component Analysis (PCA) for dimensionality reduction. PCA1 and PCA2, which together accounted for over 80% of the variance collectively, were selected for subsequent SVM analysis. Utilizing the Radial Basis Function (RBF) kernel, which yielded a cross-validation score of 0.96, proved to be most suitable for our research. Hyperparameter optimization using the GridSearchCV library resulted in a gamma value of 10 and C value of 0.001. The OneVsRest SVM classifier achieved an impressive overall accuracy of 0.97 and 0.96, respectively, for the training and validation datasets. This study presents a precise model for identifying individual Sika deer using images and video frames, which can be replicated for other species with unique patterns, thereby assisting conservationists and researchers in effectively monitoring and protecting the species.

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