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

IF 7.3 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2025-05-26 DOI:10.1016/j.ecoinf.2025.103193

Hari Kishan Kondaveeti , Nabin Kumar Upadhaya , Dheeraj Sai Tukkugudam , Rahul Panigrahi , Sirivella Madhan Chandra Mouli , Valli Kumari Vatsavayi , Nagendra Panini Challa

{"title":"BirdRecon: A free open source tool for image based bird species recognition","authors":"Hari Kishan Kondaveeti , Nabin Kumar Upadhaya , Dheeraj Sai Tukkugudam , Rahul Panigrahi , Sirivella Madhan Chandra Mouli , Valli Kumari Vatsavayi , Nagendra Panini Challa","doi":"10.1016/j.ecoinf.2025.103193","DOIUrl":null,"url":null,"abstract":"<div><div>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 developed 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 problem 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%.</div><div>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.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103193"},"PeriodicalIF":7.3000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S157495412500202X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}

引用次数: 0

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 developed 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 problem 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.

查看原文本刊更多论文

BirdRecon：一个基于图像的鸟类物种识别的免费开源工具

自动化鸟类物种识别是一项关键但具有挑战性的任务，特别是对于旨在支持鸟类学家，保护主义者和鸟类爱好者的系统。BirdRecon是一个开源的鸟类物种识别系统，旨在加强观鸟、鸟类学研究和生物多样性保护。该系统利用四个预训练深度学习模型（densenet201, EfficientNetB7， InceptionV3和resnet50v2）的软投票集合来提高分类准确性和鲁棒性。为了解决类不平衡问题和增强泛化能力，采用了数据增强和早期停止优化策略来防止训练过程中的过拟合。使用包含525种鸟类和超过84,000个训练图像的基准数据集来评估系统。实验结果表明，该集成模型的分类准确率为99.6%，精密度为99.7%，召回率为99.6%，比现有的先进方法高出0.51%。BirdRecon是一个网络和移动应用程序，提供实时鸟类物种识别，支持多语言（英语，印地语和泰卢古语）和其他功能，如通过谷歌Gemini和维基共享资源的视觉参考来描述物种。该系统的开源特性（可在GitHub上获得）促进了协作和进一步的进步。由于其用户友好的设计和在资源有限的设备上的实际部署能力，BirdRecon为研究人员，保护主义者和观鸟者提供了宝贵的工具，有助于监测生物多样性和保护工作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.