This study presents an effective vision -based method to accurately identify predator species from camera trap images in protected Uganda areas. To address the challenges of object detection in natural environments, we propose a new multiphase deep learning architecture that combines extraction of various features with concentrated edge detection. Compared to previous approaches, our method offers 90.9% classification accuracy, significantly requiring fewer manual advertising training samples. Background pixels were systematically filtered to improve model performance under various environmental conditions. This work advances in both biology and computational vision, demonstrating an effective and data-oriented approach to automated wildlife monitoring that supports science -based conservation measures.

Keywords: deep learning, camera trap, convolutional neural network, dataset, predator, kidepo national park, wildlife

This study is a testament to the potential of convolutional neural networks in softmax activation to classify mantis, honey badger, and weasel samples. The model was able to predict highly with low misclassification and had the potential to reduce environmental variance by minimizing it using data augmentation. The research shows how deep learning networks would be used in the automation of taxonomic classification, which in turn would help species identification through images and large-scale conservation monitoring.

Keywords: deep learning, machine learning, convolutional neural networks, dataset, softmax function, image classification, wildlife, data augmentations