Animal Image Classification Using Deep Learning Algorithms

Abstract

The process of animal image classification plays a vital role in establishing essential requirements that enable effective monitoring of ecosystems and the preservation of biodiversity and agricultural practices and ecological research work. The task of animal face recognition faces difficulties because animals from the same species present different facial features which results in difficulties to identify their species through the use of facial recognition technology. The research presents a deep learning-based system which uses convolutional neural networks and transfer learning techniques together with document enhancement methods to create an animal concept classification system. The main objective of this work search out create a precise and robust classification system which will evaluate its performance against existing deep learning-based systems. The research provides three main contributions which include the creation of a systematic exercise that tests various deep learning architectures in a standardized research environment and the application of transfer knowledge to enhance feature representation and the establishment of a comprehensive achievement assessment system that uses standard versification. The proposed model reaches a 95.4% accuracy rate which surpasses the performance of conventional CNN models that were developed from scratch (88.2%) and standard transfer learning models (91.6%) and inconsequential CNN architectures (89.4%) and ensemble-based methods (93.1%). The proposed method reaches high accuracy (95.1%) together with recall (94.6%) and F1-score (94.8%) which demonstrates its capability to properly categorize data while generalizing across various situations. The research demonstrates that the deep learning foundation functions as a dependable and scalable solution for tasks involving animal representation categorization