Enhancing biodiversity monitoring with CNN: Invasive plant species detection

The detection and classification of invasive plant species are crucial for biodiversity conservation and ecosystem management. This research work explores the use of Convolutional Neural Networks (CNNs) to identify invasive species from images. By leveraging the powerful feature extraction capabilities of CNNs, we developed a model that accurately classifies the non-invasive and invasive plant species. The model was trained on a diverse dataset, incorporating various environmental conditions and geographical locations to enhance its robustness. Evaluation metrics indicate high precision and recall, demonstrating the model's effectiveness in real-world applications. Our findings suggest that CNN-based approaches can significantly enhance monitoring and management efforts for invasive species, offering a scalable and automated solution for environmental scientists and policymakers. Our proposed model achieved 92.5% accuracy by utilizing Resnet50 architecture which is superior than existing state of the art models.