Aplicaciones de Machine Learning para el análisis de comida : Modelos de aprendizaje automático para identificación de comida.

Abstract

This thesis presents a comprehensive study on the application of machine learning (ML) techniques, with a focus on Convolutional Neural Networks (CNNs), for the analysis of food data. In an era where dietary health and food quality are of paramount concern, the need for advanced, accurate, and efficient methods to analyze and classify food items has never been more critical. This work leverages the capabilities of ML to bridge this gap, offering innovative solutions for food classification, nutritional analysis, and quality assessment. Utilizing a rich dataset from Food 101 and employing state-of-the-art ML algorithms, this research navigates through the challenges of food image recognition, providing insights into the textural, nutritional, and qualitative aspects of various food items. The thesis not only delves into the technicalities of algorithm development and model optimization but also contextualizes the findings within the broader implications for the food industry, health monitoring, and consumer awareness.