Bird Species Identification Research Articles

Long-Range Bird Species Identification Using Directional Microphones and CNNs

This study explores the integration of directional microphones with convolutional neural networks (CNNs) for long-range bird species identification. By employing directional microphones, we aimed to capture high-resolution audio from specific directions, potentially improving the clarity of bird calls over extended distances. Our approach involved processing these recordings with CNNs trained on a diverse dataset of bird calls. The results demonstrated that the system is capable of systematically identifying bird species up to 150 m, reaching 280 m for species vocalizing at frequencies greater than 1000 Hz and clearly distinct from background noise. The furthest successful detection was obtained at 510 m. While the method showed promise in enhancing the identification process compared to traditional techniques, there were notable limitations in the clarity of the audio recordings. These findings suggest that while the integration of directional microphones and CNNs for long-range bird species identification is promising, further refinement is needed to fully realize the benefits of this approach. Future efforts should focus on improving the audio-capture technology to reduce ambient noise and enhance the system’s overall performance in long-range bird species identification.

Assessing determinants and trends in bird species identification skills among students: a comparative analysis with early 2000s data

ABSTRACT The decline of Earth’s biodiversity highlights the crucial role of knowledge about species in understanding ecological interdependencies and supporting conservation efforts. This study assessed bird species identification skills among 1752 students (46.4% boys, 51.9% girls, 1.1% diverse) in grades 4–12 from different school types, focusing on 30 species. Demographic and individual factors were examined for their impact on bird species identification. Comparative analyses with a 2005 dataset reveal a significant 37.75% decrease in students’ ability to correctly name birds at the species level. The average identification score was 6.84 (SD = 4.10) out of 30. Most students were only able to classify birds at the taxonomic level of family or order, but not at the species level. Factors such as age, school type, garden stay, distance to green space, interest in birds, perception of birds, and animal-related activities significantly influenced bird species identification. The study reveals a decline in bird species identification skills across all school types, particularly in schools of the highest stratification level, which nowadays have lower identification scores than medium stratification schools in 2005. The implications for educational institutions, particularly in the context of Education for Sustainable Development, emphasise the need for a reevaluatingcurriculum and teaching methods.

Deep transfer learning-based bird species classification using mel spectrogram images.

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet technique was utilized for the investigation. The study adapts the fine-tune model to learn the pertinent patterns from mel spectrogram images specific to this bird species classification task. The fine-tuned EfficientNet model combined with a type of Recurrent Neural Networks (RNNs) namely Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). RNNs are employed to capture the temporal dependencies in audio signals, thereby enhancing bird species classification accuracy. The dataset utilized in this work contains nearly 17,000 bird sound recordings across a diverse range of species. The experiment was conducted with several combinations of EfficientNet and RNNs, and EfficientNet-B7 with GRU surpasses other experimental models with an accuracy of 84.03% and a macro-average precision score of 0.8342.

Habitat niches of bird species along a recovery gradient in the Chocó tropical forest

Conservation programs need improved tools to measure the recovery of animal diversity across restoration gradients. We used soundscapes and expert identifications of bird species to calculate niche position (i.e., mean of environmental conditions across all areas a species occupies) and niche breadth (i.e., the standard deviation of the species distribution) along a recovery gradient; from agriculture to early (up to 20 yrs) and late (up to 38 yrs) recovery, to old-growth forests. Our survey included 323 bird species and was conducted in 66 plots in the lowland Chocoan tropical rainforest in Ecuador where less than 11% of the forest remains intact, and large areas are currently undergoing regeneration post-abandonment. First, we validated our niche metrics by contrasting them against independent global categories of forest density dependency. We then explained the niche metrics of the bird species with different ecological traits, gathered from the literature, reflecting species-specific primary diet, morphology and distribution, and accounting for the phylogenetic relatedness of species. Finally, we explored the phylogenetic signal present in bird species’ ecological traits and recovery niche metrics. Niche position and breadth across the recovery gradient closely followed global categories of forest density dependency. However, our approach provided a more fine-scaled sorting of bird species in forest plots categorized as late recovery and old-growth. Granivorous birds occupied niche positions in active cacao and pasture plots and were replaced by frugivorous birds in older regeneration plots. Along the recovery gradient, tail length and handwing index decreased with niche position, supporting previous observations that birds in old-growth forests are less mobile. Birds in old-growth forests had smaller global distribution ranges than birds in agricultural plots. Finally, the latitudinal distribution of birds in the study area averaged south of the equator, with birds in old-growth plots averaging latitudinal centroids in wet forests north of the equator, whereas birds in agriculture plots averaged latitudinal centroids further south of the country, towards the dry and open Tumbesian forest. Frugivores, invertivores and vertivores had broader niche breadths, but these decreased (marginally) with tail length. We suggest these recovery niche metrics as a potentially powerful tool for the rapid assessment of the recovery process, which might support conservation strategies such as biodiversity credits, compensation payments, and strategic land purchases. With the increasing availability of information-intensive models for bird species identification, such as deep learning artificial intelligence, our new niche metrics open the avenue for rapid assessment of tropical biodiversity and new conservation areas at larger scales.

Ornithological Imaging: Transforming Bird Species Identification with Technology

Ornithological Imaging: Transforming Bird Species Identification with Technology

Bird Species Detection Using Deep Learning

Bird species classification plays a crucial role in various ecological and conservation endeavors. Leveraging deep learning techniques has shown promise in automating this task, offering the potential for efficient and accurate species identification. This paper presents a comprehensive study on the application of the YOLOv5 architecture, a state-of-the-art object detection framework, for bird species classification from images. We explore the effectiveness of YOLOv5 in comparison to traditional convolutional neural networks (CNNs) for bird species identification. Our study involves extensive experimentation with different configurations of YOLOv5, including variations in model size, training strategies, and dataset preprocessing techniques. We evaluate the performance of the YOLOv5-based classification framework on diverse datasets, ranging from publicly available bird image datasets to custom datasets collected through field observations. Additionally, we compare the classification accuracy of YOLOv5 with other popular CNN architectures, such as ResNet and EfficientNet, to assess its efficacy in bird species recognition tasks. Furthermore, we investigate the transferability of pre-trained YOLOv5 models to different bird species datasets and examine the robustness of the models to variations in image quality, background clutter, and occlusions.Our experimental results demonstrate that YOLOv5 offers competitive performance in bird species classification tasks, achieving high accuracy and efficiency. We discuss the strengths and limitations of using YOLOv5 for this application and provide insights into potential avenues for future research.

“Decoding Bird Species Diversity through Image Analysis

Bird species diversity plays an integral role in maintaining ecosystem equilibrium and serves as a significant indicator of environmental health. However, the meticulous monitoring and cataloging of bird species within specific regions pose considerable challenges, often requiring extensive time and expertise from ornithologists. In today's rapidly advancing technological landscape, the integration of image analysis and machine learning presents a promising avenue to streamline bird species identification and diversity assessment processes. Nonetheless, the increasing rarity of certain bird species presents a formidable obstacle, complicating their classification. Birds encountered across diverse environments present varying sizes, shapes, colors, and orientations, adding complexity to accurate identification via image analysis. Moreover, image- based classification exhibits more pronounced variations compared to audio classification, although human perception of birds through images remains intuitively comprehensible. Leveraging the deep convolutional neural network (DCNN) algorithm, images undergo conversion into grayscale format to generate autographs using TensorFlow, resulting in the creation of multiple comparison nodes. Subsequently, these nodes undergo comparison with the testing dataset, generating a score sheet for analysis. Interpretation of this score sheet facilitates the prediction of the target bird species based on the highest score achieved. Experimental analysis conducted on datasets such as Caltech-UCSD Birds demonstrates the algorithm's efficacy, with bird identification accuracy ranging between 80% and 90%.

EcoSonicML: Harnessing Machine Learning for Biodiversity Monitoring in South African Wetlands

Biodiversity monitoring, particularly in a country as diverse as South Africa with its extensive migratory bird population, presents significant challenges. This challenge becomes even more pronounced in environments with a multitude of coexisting bird species, notably wetlands, which serve as crucial breeding and feeding grounds for various avian species. This research will address this challenge by designing a cost-effective sound-based sensor system capable of deployment in diverse wetland ecosystems. The primary aim is to aid in the monitoring of bird species by detecting their presence and distribution and then transmitting this valuable data to a central base station. To assess the system’s feasibility and performance, a series of experiments were conducted at the Rondevlei Nature Reserve in Cape Town, South Africa. These experiments focused on the sensor’s capacity to accurately identify avian species while maintaining robustness in varying environmental conditions. The results yielded promising outcomes, demonstrating the successful identification of bird species. Furthermore, the system exhibited reliability across different weather conditions, positioning it as a viable choice for long-term deployment in wetland environments. Beyond species detection, this project also delved into practical aspects of data transfer and storage efficiency, ensuring the system’s suitability for real-world applications. Modularity was another crucial consideration, simplifying maintenance and upgrades. Moreover, a preliminary cost analysis indicated the cost-effectiveness of the system compared to commercial alternatives. The integration of climate sensors into the monitoring system was explored as a future direction. This addition holds the potential to provide a more comprehensive approach to environmental monitoring by incorporating climate data into the analysis. Such a holistic approach can further enrich our understanding of bird behaviour in relation to changing environmental conditions. The findings of this research have significant implications for avian conservation and ecological studies, particularly in the unique context of South Africa. This project introduces an affordable and practical tool for monitoring bird species in wetland habitats, offering valuable insights into the preservation and management of these critical ecosystems.

The Impact of Imperfect XAI on Human-AI Decision-Making

Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance with the aim of designing more human-centered computer-supported collaborative tools. Several human-centered explainable AI (XAI) techniques have been proposed in hopes of improving decision-makers' collaboration with AI; however, these techniques are grounded in findings from previous studies that primarily focus on the impact of incorrect AI advice. Few studies acknowledge the possibility of the explanations being incorrect even if the AI advice is correct. Thus, it is crucial to understand how imperfect XAI affects human-AI decision-making. In this work, we contribute a robust, mixed-methods user study with 136 participants to evaluate how incorrect explanations influence humans' decision-making behavior in a bird species identification task, taking into account their level of expertise and an explanation's level of assertiveness. Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance. We also discuss how explanations can deceive decision-makers during human-AI collaboration. Hence, we shed light on the impacts of imperfect XAI in the field of computer-supported cooperative work and provide guidelines for designers of human-AI collaboration systems.

Birds as potential bioindicators for terrestrial ecosystems

Considering the lack of ecosystems’ monitoring in Armenia and the urgent necessity of setting up such a system for decision making in nature conservation and natural resource management, the current paper aims at identification of indicator bird species for the key types of ecosystems in the country and crosscutting influence of climate change. In the forests, the study identified the bird species composition, total number of all birds, and 11 species as indicators of degradation, fragmentation, and aridization. In grasslands, the total number of all birds and seven species indicate degradation of the mountain steppes and meadows due to overgrazing and uncontrolled mowing. In arid lands, the change of bird species composition and total number of all birds indicate degradation of semi-deserts, scrublands, and juniper woodlands due to their transformation into orchards, while six species indicate degradation of these habitats due to overgrazing. In rivers and streams, four species indicate fragmentation of the rivers. In wetlands, at least seven species indicate drainage of the wetlands, and at least one indicates wetlands’ pollution. Also, the study identified seven species, which indicate the influence of climate change on birds’ distribution at the Eurasian scale. There is still a need for additional study of the indicators of wetlands and lakes, and influence of climate change. A need for improvement of the policy and institutional framework is important as well.

Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: A phylogenetic perspective

The study of bird populations is crucial for biodiversity research and conservation. Deep artificial neural networks have revolutionized bird acoustic recognition; however, most methods overlook inherent relationships among bird populations, resulting in the loss of biological information. To address this limitation, we propose the Phylogenetic Perspective Neural Network (PPNN), which incorporates hierarchical multilevel labels for each bird. PPNN uses a hierarchical semantic embedding framework to capture feature information at different levels. Attention mechanisms are employed to extract and select common and distinguishing features, thereby improving classification accuracy. We also propose a path correction strategy to rectify inconsistent predictions. Experimental results on bird acoustic datasets demonstrate that PPNN outperforms current methods, achieving classification accuracies of 90.450%, 91.883%, and 89.950% on the Lishui-Zhejiang Birdsdata (100 species), BirdCLEF2018-Small (150 species), and BirdCLEF2018-Large (500 species) datasets respectively, with the lowest hierarchical distance of a mistake across all datasets. Our proposed method is applicable to any bird acoustic dataset and presents significant advantages as the number of categories increases.

Socio-Cultural Impact of Avian Diversity in the Himalayan Region: A Comprehensive Study of Bird Species in Huddu Village, Chopta, and Deoriatal

The study employed a random visualization method to count and describe bird species in the Huddu Village, Chopta, and Deoriatal regions of the Himalayas. The data, collected through global positioning system (GPS) recordings between March and April, revealed 48 bird species across these areas, belonging to 20 different families and varying orders. The observations included 33 bird species in the Huddu region, 10 in Chopta, and 9 in Deoriatal, each displaying different elevations and geographical coordinates. Aside from birds, the fieldwork documented various flora and fauna species. The research detailed the relative abundance, distribution, and feeding habitats of bird species through recorded tables. Notable high-abundance species in Huddu Village included the streaked laughing thrush, common myna, various drongo species, grey treepie, and yellow or red-vented bulbul. Conversely, low-abundance species like alpine chough, Asian koel, scarlet minivet, and fire-breasted flowerpecker were rare, with their occurrences graphically depicted in the results. The identification of bird species utilized diverse methods, including internet resources and field guides. The study contributes valuable insights into the avian biodiversity of the Himalayan regions, emphasizing the importance of random visualization techniques and recording detailed ecological characteristics.

The influence of bird pest attack on rice damage and production in Kuantan Singingi

Birds pest in rice field activities are one of the important pest that attack. It has even caused 161,4 ha of crop loss and 124.05 ha of heavy damage in Riau. This neet to be handled and managed properly. This study was initiated by identifying the types and effects of bird pest attacks on the damage and productionof rice crops in Kuantan Singingi. This study was used a survey method by making plots (10x10 m), bird pests species and populations by using a point transect. Observations were carried out in the morning, afternoon and evening. Bird species identification refers to the MacKinnon Bird Guide in Sumatera, Jawa, and Bali. Diversity of birds pest was calculated by the Shannon-Wienner index, species richness by margaleft index, and species dominance by the Simpson index. This study were observed 1.045 birds attack of paddy field. Koto Kari site was observed 479 individual, 332 individu in Kampung Baru, and 214 in Sangau Lonchura striata was observed as many as 635 individuals. Species diversity index was 0.667, species richness index 0.288, density 0.616 and dominance value 0.527. the highest frequency of bird pest visit was in Koto Kari sithe which was 42 times while based on the species was Lonchura striata71 times with an attack time in the morning of 83 times. The highest damage to panicles by bird pest was caused by Lonchura maja at 46.09% but the highest loss of rice wight was caused by Lonchura striata.

Automating bird species classification: A deep learning approach with CNNs

Bird species identification and classification are challenging yet crucial for research and conservation. Traditional methods are labor-intensive, require specialized expertise, and can be prone to error. Recent advances in deep learning offer an automated solution to this complex problem. This study evaluates a convolutional neural network (CNN) model for classifying images of 525 bird species. The model employs transfer learning using the EfficientNetB0 architecture and was trained on over 84,000 images. With data augmentation, the model achieved 87% validation accuracy and 86.7% test accuracy, demonstrating its ability to overcome limited data and generalize well. Difficulties in obtaining balanced, high-quality data for each species were addressed through transfer learning and augmentation.

Birdy: A Bird Detection System using CNN and Transfer Learning

Identification of bird species is an important part of efforts to monitor and protect biodiversity. The routine process of brand identification is often labor-intensive and time-consuming. In this project, we use the power of deep learning, specifically convolutional neural networks (CNN) and Transfer Learning, to recognize bird species from image data. Transformational learning allows us to build advanced learning models that achieve high accuracy with little data. Our method is based on collecting large numbers of bird images and can identify many bird species. The findings not only improve the effectiveness of bird species research, but also help better understand bird ecosystems and promote conservation. In-depth research on bird species CNN Change study Biodiversity monitoring Species Identification Sustainable conservation

Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5.

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model's accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species.

Automated Audio Signal Processing for Bird Species Identification

Automated Audio Signal Processing for Bird Species Identification

Effects of landscape and distance in automatic audio based bird species identification.

The present work focuses on how the landscape and distance between a bird and an audio recording unit affect automatic species identification. Moreover, it is shown that automatic species identification can be improved by taking into account the effects of landscape and distance. The proposed method uses measurements of impulse responses between the sound source and the recorder. These impulse responses, characterizing the effect of a landscape, can be measured in the real environment, after which they can be convolved with any number of recorded bird sounds to modify an existing set of bird sound recordings. The method is demonstrated using autonomous recording units on an open field and in two different types of forests, varying the distance between the sound source and the recorder. Species identification accuracy improves significantly when the landscape and distance effect is taken into account when building the classification model. The method is demonstrated using bird sounds, but the approach is applicable to other animal and non-animal vocalizations as well.

Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques

Wildlife conservationists have traditionally relied on manual identification and tracking of bird species to monitor populations and identify potential threats. However, many of these techniques may prove to be time-consuming. With the advancement of computer vision techniques, automated bird detection and recognition have become possible. In this manuscript, we present an application of an object-detection model for identifying and tracking wild bird species in natural environments. We used a dataset of bird images captured in the wild and trained the YOLOv4 model to detect bird species with high accuracy. We evaluated the model’s performance on a separate set of test images and achieved an average precision of 91.28%. Our method surpassed the time-consuming nature of manual identification and tracking, allowing for efficient and precise monitoring of bird populations. Through extensive evaluation on a separate set of test images, we demonstrated the performance of our model. Furthermore, our results demonstrated the potential of using YOLOv4 for automated bird detection and monitoring in the wild, which could help conservationists better understand bird populations and identify potential threats.

Evaluation of pre-trained convolutional neural networks for the development of web/mobile-based bird species identification applications under Indian Bioresource Information Network (IBIN) framework

Evaluation of pre-trained convolutional neural networks for the development of web/mobile-based bird species identification applications under Indian Bioresource Information Network (IBIN) framework