ConvNeXt-Driven Multi-Class Animal Prediction Using Deep Feature Learning

Automated identification of animal species from visual data is essential for biodiversity conservation, ecological monitoring, and intelligent wildlife management. This paper presents a deep learning framework for large scale multi-class animal species classification using transfer learning with the ConvNeXt-Tiny architecture. A pretrained ConvNeXt model, initialized with ImageNet weights, is fine-tuned on a publicly available dataset comprising 5,400 images across 90 distinct animal classes. The proposed system integrates modern convolutional design principles inspired by vision transformers with the efficiency and inductive biases of conventional CNNs, enabling robust hierarchical feature learning. Data augmentation and dropout regularization are employed to improve generalization and mitigate overfitting. Experimental results demonstrate strong classification performance, achieving an overall accuracy of 93%, with balanced macro precision, recall, and F1-score of 93%. Both quantitative and qualitative evaluations confirm the model’s effectiveness in handling visual variability, background complexity, and inter-class similarity. The proposed framework offers a scalable and reliable solution for automated animal species recognition in real-world ecological and conservation applications.