Utilizing Machine Learning for Automated Species Identification in Biodiversity Monitoring

Thesis Abstract

The rapid decline in global biodiversity and the increasing need for efficient monitoring tools have underscored the importance of innovative solutions in wildlife conservation. Traditional species identification techniques, reliant on manual visual surveys and expert taxonomists, are often time-consuming, labor-intensive, and limited in scalability, thereby constraining timely and comprehensive biodiversity assessments. This study aims to develop, evaluate, and optimize a machine learning-based framework for automated species identification from ecological images, with a specific focus on avian and mammalian fauna within tropical rainforest ecosystems. The research seeks to address the overarching problem of accurate, scalable, and resource-efficient species classification to support biodiversity monitoring efforts. The specific objectives include (1) to compile and preprocess a comprehensive dataset of labeled wildlife images captured via camera traps; (2) to train and validate multiple machine learning models, including convolutional neural networks (CNNs), support vector machines (SVMs), and random forest classifiers, for species identification; (3) to evaluate the models' accuracy, precision, recall, and F1 scores; (4) to compare the performance of deep learning versus traditional machine learning techniques; and (5) to assess the influence of image quality, environmental conditions, and species traits on model performance. The study draws on the theoretical framework of supervised learning within artificial intelligence and integrates the ecological niche theory to comprehend species-specific visual features. Methodologically, the research employs a quantitative, cross-sectional design. The population comprises wildlife images collected over 12 months from 50 camera trap stations distributed across a 100 km^2 tropical rainforest reserve. A stratified random sampling technique ensures representative data across different habitats and species groups, with an initial dataset of approximately 20,000 images. Data collection instruments include high-resolution camera traps equipped with infrared sensors, along with image annotation tools such as LabelImg and CVAT for creating labeled datasets. To ensure the validity and reliability of the models, data preprocessing involves image augmentation, normalization, and class balancing techniques, while cross-validation (k=10) evaluates model robustness. Data analysis encompasses training CNNs, SVMs, and random forest classifiers, followed by comparative performance evaluation using metrics such as accuracy, confusion matrices, and ROC curves. The research also incorporates feature importance analysis via SHAP (SHapley Additive exPlanations) values to interpret model decision processes. Ethical considerations include compliance with wildlife research regulations and minimization of disturbance during data collection. Anticipated findings suggest that deep learning models, specifically CNN architectures such as ResNet152, will outperform traditional machine learning classifiers in species identification accuracy, achieving F1 scores exceeding 0.90. Additionally, the study expects to reveal that image quality and environmental factors significantly affect model performance, emphasizing the need for standardized image capture protocols. The comparative analysis aims to establish a scalable, accurate, and resource-efficient system for biodiversity monitoring that can be integrated into conservation policies and strategies. This research advances knowledge by providing a rigorous evaluation of machine learning applications in ecological contexts, bridging AI technology with practical conservation needs, and identifying critical factors that influence model effectiveness in real-world biodiversity monitoring. The study's outcomes will contribute to the development of automated, real-time species identification systems capable of supporting large-scale ecological surveillance, thereby enhancing conservation decision-making processes. Concluding, the study recommends the adoption of optimized CNN models for species identification in biodiversity projects, emphasizes improvements in camera trap deployment, and advocates for further research on multispecies detection frameworks across diverse ecological zones to refine and expand automated monitoring capabilities.