Towards transparency in AI: Explainable bird species image classification for ecological research

Abstract

Birds are indicators of biodiversity and ecosystem health and play an essential role in maintaining the balance of natural ecosystems. However, urbanization, deforestation, and technological advancements have severely affected bird habitats, leading to a significant decline in species diversity. Manual detection and recognition of bird species based on morphological and behavioral characteristics during birdwatching and surveys are challenging and require expertise, making deep learning a promising alternative. Deep learning techniques offer the advantage of automatic identification, which can significantly enhance the efficiency and accuracy of species recognition tasks. However, the black-box nature of these models presents a significant issue, as it is difficult to understand their internal decision-making processes, leading to concerns about their reliability and trustworthiness. This study addresses these issues by employing Explainable Artificial Intelligence (XAI) to enhance the transparency of deep learning models for bird species image classification. In this paper, a three-stage XAI-based approach is proposed, involving transfer learning, Local Interpretable Model-Agnostic Explanations (LIME), and Intersection over Union (IoU) scores to assess model performance. Six pretrained models are evaluated on the CUB 200-2011 dataset, with EfficientNetB0 achieving the highest accuracy (99.51%) and IoU score (0.43). Despite high accuracy, models such as InceptionResNetV2 and DenseNet201 showed lower IoU scores, raising trustworthiness concerns. This study underscores the importance of XAI in ensuring the transparency and reliability of Artificial Intelligence (AI) models in ecological applications.

Keywords

• Bird species recognition
• Deep learning
• Explainable Artificial Intelligence (XAI)
• Local Interpretable Model-Agnostic Explanations (LIME)
• Transfer learning
• Bird image classification