Wildlife Species Classification from Camera Trap Images Using Fine-tuning EfficientNetV2.

Camera traps are a valuable tool for wildlife research and conservation, but wildlife species classification in camera trap imagery is challenging due to the variation in species appearance, pose, and lighting conditions. This study explores the use of transfer learning and fine-tuning to develop a robust deep convolutional neural network model for wildlife species classification from camera trap images. To prevent overfitting, data augmentation techniques were applied during the image pre-processing stage. ResNet-50 and various EfficientNetV2 variants have been evaluated, and the EfficientNetV2-L model emerged as the top performer. Fine-tuning methods were then applied to the EfficientNetV2-L model to further improve its performance. Experimental results show that the fine-tuned EfficientNetV2-L model outperformed other methods with an accuracy of 88.822%, a precision of 86.941%, a recall of 87.638%, and an F1-score of 87.193% on a held-out test set, demonstrating its effectiveness for wildlife species classification from camera trap images. [ABSTRACT FROM AUTHOR]