Smart Marine Biodiversity Monitoring through Image Based Species Classification

Project Code :TCMAPY2183

Objective

The objective of this project, Smart Marine Biodiversity Monitoring through Image-Based Species Classification, is to develop an advanced system that utilizes deep learning techniques for efficient and accurate classification of marine species. By leveraging Convolutional Neural Networks (CNN), MobileNet, and DenseNet algorithms, the project aims to automate the process of species identification from image datasets, enabling accurate analysis and reducing the reliance on manual classification. The system seeks to provide a user-friendly interface developed with HTML, CSS, and JavaScript for easy image uploads and result visualization, while the backend, built with Python and Flask, processes and classifies the images. Through this automated solution, the project aims to enhance the scalability of marine biodiversity monitoring, offering a tool that can be employed for large-scale environmental conservation efforts, helping to protect marine ecosystems by ensuring accurate, timely, and accessible data on species diversity.

Abstract

The rapid degradation of marine biodiversity has become a critical issue in recent years. Monitoring and classifying marine species is essential for conservation efforts and sustainable management of marine ecosystems. This project, titled Smart Marine Biodiversity Monitoring through Image-Based Species Classification, leverages deep learning models to classify marine species from image datasets. The system incorporates advanced machine learning algorithms such as Convolutional Neural Networks (CNN), MobileNet, and DenseNet to accurately classify marine species such as Dolphins, Fish, Lobsters, Octopus, and Sea Horses.The front-end of the application is built using HTML, CSS, and JavaScript, providing an intuitive and interactive interface for users to upload images and view classification results. On the backend, Python and the Flask framework are used to process the images, run the classification models, and return predictions to the users. By utilizing image-based classification, this system aims to provide a scalable and efficient solution for marine species monitoring, promoting better understanding and protection of marine biodiversity.

Keywords: Marine biodiversity monitoring, species classification, deep learning, CNN, MobileNet, DenseNet, image classification, marine species, dolphins, fish, lobsters, octopus, sea horses, image processing, web application, marine ecosystem, conservation efforts, sustainable management, marine protection, machine learning, academic interface.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.