Work

BirdRecon: A free open source tool for image based bird species recognition

Year: 2025

Type: article

Abstract: Automated bird species recognition is a critical, yet challenging task, particularly for the systems aimed at supporting ornithologists, conservationists, and bird enthusiasts. This study introduces BirdRecon, an open-source bird species recognition system designed to enhance birdwatching, ornithological research, and biodiversity conservation. The system leverages a soft voting ensemble of four pretrained deep learning models—DenseNet201, EfficientNetB7, InceptionV3, and ResNet50V2—to improve classification accuracy and robustness. To address class imbalance and enhance generalization, data augmentation is applied and an early stopping optimization strategy is used to prevent overfitting during training. A benchmark dataset comprising 525 bird species with over 84,000 training images is used to evaluate the system. The experimental results demonstrate that the proposed ensemble model achieves a classification accuracy of 99.6%, precision of 99.7%, and recall of 99.6%, outperforming the existing state-of-the-art methods by a margin of 0.51%. BirdRecon is implemented as both a web and mobile application, offering real-time bird species identification with multilingual support (English, Hindi, and Telugu) and additional features, such as species descriptions through Google Gemini and visual references from Wikimedia Commons. The open-source nature of the system, available on GitHub, promotes collaboration and further advancements. With its user-friendly design and practical deployment capability on resource-constrained devices, BirdRecon serves as a valuable tool for researchers, conservationists, and birdwatchers, contributing to biodiversity monitoring and conservation efforts.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2025.103193