Parrot Species Recognition by using CNN & SVM

Parrot Species Recognition by using CNN & SVM

Mithilesh Vishwakarma1, Amit Kumar Pandey2, Bipin Yadav3, Gopal Rajbhar4

1 Assistant Professor (IT), 2, 3,4PG Students

1,2,3,4Department of Information Technology, Thakur College of Science and Commerce, Thakur Village, Kandivali (East), Mumbai-401107, Maharashtra, India

vmithsoft@gmail.com,amitpandey8089@gmail.com.,bipinyadav1030@gmail.com., rajbhargopal203@gmail.com

Abstract:

This research project addresses the imperative task of parrot species recognition through the integration of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) methodologies. Focused on three distinct parrot species—'Blue and Gold Macaw,' 'Ringneck Parakeet,' and 'Sun Conure'—the study explores the efficacy of advanced machine learning techniques in ornithological research. The CNN serves as a robust feature extractor, autonomously learning intricate visual patterns crucial for species identification. Meanwhile, the SVM acts as a discriminative classifier, optimizing decision boundaries in the feature space. Comparative analysis reveals the CNN's superior accuracy of 95%, surpassing the SVM's 83.33%. The visual representation, a bar chart, provides a clear and accessible depiction of the research findings. Titled "Comparative Analysis of CNN and SVM for Accuracy Assessment of Parrot Species," the chart ensures relevance and context for the audience. Overall, this research contributes valuable insights into the potential of deep learning in parrot species recognition, with implications for automated wildlife monitoring and biodiversity conservation in the realm of ornithology.