IMAGE ANALYSIS FRAMEWORK FOR CARBON FOOTPRINT ESTIMATION

This study has been undertaken to develop an image analysis framework for estimating the carbon footprint of consumer products using advanced computational techniques. The framework leverages image recognition models alongside a dataset of product-specific carbon emission metrics to provide accurate assessments. Streamlit serves as the primary interface for user interaction, while Python, HTML, and CSS facilitate backend computation and frontend design. The analytical framework integrates data preprocessing, feature extraction, and environmental impact modelling to ensure robust analysis. Publicly available datasets on carbon footprints have been utilized to train and validate the system. The proposed system aims to bridge the gap between academia and sustainable practices, offering an innovative tool for researchers, educators, and policymakers to assess environmental impacts effectively Key Words: Image Analysis, carbon footprint estimation, environmental impact, Streamlit framework, Python programming, sustainable development, machine learning.