Carbon Footprint Prediction from Food Image

Food and agricultural consumption are responsible for nearly a quarter of the world’s greenhouse gas (GHG) emissions, so eating is a critical element in slowing climate change. Accurate estimation of meals carbon price is necessary to promote sustainable food intakes, but current methods rely heavily on self-administered dietary questionnaires, nutrition databases, or manual input, which are time-consuming, subject to errors, and difficult to scale up. In response to these challenges, we provide a novel machine learning model that predicts the carbon footprint of meals from images of food. Our approach marries deep-learning-based food identification and carbon intensity data from established life cycle assessment (LCA) studies. With ubiquitous food image datasets such as Food-101 and UECFood256, we train convolutional neural networks (CNNs) and newer models such as Efficient Net to classify meal ingredients. Each of the recognised food items is then cross-mapped to a carbon footprint database, where emission factors (in g CO₂-eq/100 g) are summed up to create a composite meal-level estimate. In addition to prediction, our system suggests alternative, lower-emission foods, providing actionable evidence for environmentally conscious dietary changes. Experimental results demonstrate high accuracy of food classification and footprint estimation, with prediction errors in an acceptable range compared to ground-truth values for emissions. Survey bias is eliminated by the suggested system, real-time estimation is achieved, and the system can be incorporated as part of mobile or web-based diet-tracking tools without any difficulty. This research is one of the first to combine computer vision and sustainability strategies, and it offers a scalable and automated platform to guide people and organizations toward sustainable food consumption patterns.