TreeIDNet: Species Identification through Video Analysis using Detectron2-Based Computer Vision

Tree species identification plays a crucial role in various environmental monitoring and conservation efforts. In this paper, we present TreeIDNet, a system for species identification through video analysis using Detectron2-based computer vision techniques. The proposed system leverages the capabilities of the Detectron2 framework, for instance segmentation and object detection tasks. We begin by collecting a dataset of video footage containing diverse tree species, annotated with bounding boxes for training purposes. The training pipeline involves custom data augmentation techniques, model training, and evaluation to ensure accurate species identification. Once trained, TreeIDNet can detect and identify tree species in video frames, enabling real-time or batch processing for environmental surveillance applications. We evaluate the system's performance on a separate validation dataset and demonstrate its effectiveness in accurately identifying tree species. Our model can play a pivotal role in environmental monitoring and conservation by swiftly and accurately identifying tree species, crucial for biodiversity conservation, forest management, environmental monitoring, research, and education. Its efficiency and scalability make it a valuable tool for addressing pressing environmental challenges. The deployment of TreeIDNet has the potential to streamline species identification processes, leading to more efficient and informed decision-making in conservation efforts. Additionally, its versatility allows for seamless integration into existing environmental monitoring systems, enhancing their capabilities for long-term sustainability and ecosystem preservation.