DEEP LEARNING BASED BIRD BREED CLASSIFICATION SYSTEM | IJCSE Volume 9 – Issue 6 | IJCSE-V9I6P9

Bird breed classification is very important for nature and study birds. Before people find birds by watch them and use own knowledge, but it takes long time and sometimes give wrong result. In this paper, we make system use deep learning to identify birds from picture. The system uses CNN model to see image and tell which bird breed. It can work with picture in many places, even light or background or bird position is change. First, we do image cleaning for better quality, and then CNN take features from picture. After that system classify bird breed from features. Last step is improving result by post-processing to make result better. CNN model is trained with many bird images with label, so it learns small difference between bird breeds. We test system with many pictures and show it work better than old methods. It gives more accurate and stable result. System helps in bird study, nature protection, and ecology. This method is faster and need less human work. With this system, we can identify many birds very easy.

The Deep Learning-Based Bird Breed Classification System represents a significant breakthrough in the field of automatic avian species identification. Leveraging state-of-the- art deep learning methods such as convolutional neural networks (CNNs) and transfer learning, the system attains high accuracy and robustness in accurately classifying a diverse range of bird species, outperforming traditional manual or feature-engineering approaches. Through extensive evaluation, the system demonstrated strong generalization capabilities across varied environmental conditions including different lighting, complex backgrounds, and diverse bird poses, validating its suitability for real-world ecological monitoring scenarios. Its automated nature streamlines bird population surveys, habitat assessment, and species distribution analysis, which are central to biodiversity conservation and ecological research. Additionally, the system promotes citizen science by enabling amateur bird enthusiasts and conservationists to actively participate in species identification, enhancing public engagement and data collection efforts. The integration of multimodal data such as image and audio fusion further enhances classification performance and system reliability. While showing promising performance, challenges remain in identifying rare or underrepresented species due to limited training data and handling low-quality or occluded images. Future research directions include expanding species datasets, developing advanced learning techniques like few-shot learning, real-time recognition on mobile/edge devices, and embedding explainable AI for better interpretability. Ethical considerations surrounding data privacy, responsible data use, and minimizing impact on natural habitats are essential for sustainable deployment. Ultimately, this deep learning- based approach not only advances bird species classification technology but also contributes meaningfully to biodiversity conservation, ecological understanding, and environmental stewardship, paving the way for smarter, scalable, and more inclusive ecological monitoring initiatives worldwide.

[1]”A novel approach to Indian bird species identification: employing visual-acoustic fusion techniques for improved classification accuracy” (Frontiers in Artificial Intelligence, 2025) — Discusses the integration of visual and acoustic modalities for bird classification using deep learning techniques and fusion strategies. [2]”Deep Convolutional Neural Network for Automated Bird Species Classification” (IIETA, 2023) — Describes a CNN-based system for identifying various bird species from images, achieving high accuracy and assisting bird enthusiasts. [3]”Deep transfer learning-based bird species classification using…” (PLOS ONE, 2024) — Focuses on transfer learning techniques and hybrid deep models for bird sound and image classification, with notable accuracy achieved on large datasets. [4]”A new efficient classifier for bird classification based on transfer learning” (Wiley, 2024) — Reports an accuracy of 98.86% using an EfficientNetB5-based architecture, highlighting the effectiveness of transfer learning models. [5] “BirdNET: A deep learning solution for avian diversity monitoring” (ScienceDirect, 2021) — Introduces BirdNET, a system capable of identifying over 984 bird species using audio data with high precision. [6]”Bird species recognition using transfer learning with a …” (ScienceDirect, 2024) — Details transfer learning applications to bird image recognition, emphasizing improved accuracy and efficiency. [7]”Enhanced Bird Species Image Recognition and Classification using MobileNet and InceptionV3 Transfer Learning Architectures” (2025) — Compares MobileNet and InceptionV3 architectures, demonstrating MobileNet’s efficiency and good accuracy in resource-constrained environments. [8]”Bird Species Identification from Audio Using Deep Learning” (International Journal of Creative Research Thoughts, 2025) — Highlights deep learning methodologies focusing on audio data classification, showing high accuracy despite challenging conditions. [9]”A Comprehensive Survey on Deep Learning Techniques for Bird Species Classification Using Image and Audio Modalities” (TechRxiv, 2025) — This survey provides an extensive overview of state- of-the-art deep learning architectures applied to bird species classification using both images and audio data, evaluating various approaches and datasets. [10]”A novel approach to Indian bird species identification” (Frontiers in AI, 2025) — Proposes an innovative fusion of visual and acoustic data for improved classification accuracy in Indian bird species with practical ecological applications. [11]”Bird Detection and Species Classification Using YOLOv5″ (The Science and Information Organization, 2023) — Details the use of YOLOv5 for efficient bird detection and classification in real- time, showing promise for deployment in field conditions. [12]”The evolution of machine learning techniques in bird species classification” (WJAETS, 2025) — Reviews machine learning approaches evolving towards deep learning, highlighting future directions and challenges.

Call for Paper (Hub) Fast Paper Publication IJCSE Indexing & Databases Submit Manuscript Call for Paper: Sep–Oct 2025 Call for Paper: Publish Research