AI-Driven Wildlife Pattern Tracking and Extinction Prediction for Vulnerable Species

Conservation of wildlife is becoming more reliant on artificial intelligence (AI) and deep learning for identifying, monitoring, and conserving species. This study discusses a deep learning-based method for classifying wildlife images through EfficientNetB7, a top-performing image classification model. The process consists of dataset preprocessing, model fine-tuning, and performance evaluation based on critical parameters like accuracy, precision, and recall. The model was trained on 210 animal species and obtained a 63% accuracy on the test dataset. The study shows the promising potential of deep learning in automating wildlife conservation measures, lessening human labor, and promoting species protection plans. Class imbalance and incorrect misclassification of visually comparable species are some challenges mentioned, along with recommendations for future development.