Home Publications Projects

My Projects

Selected work in computer vision, data acquisition, data engineering and applied machine learning for food & health.

PhD Project: 4D data frameworks and multi-modal imaging fusion

PhD Research: 4D Data Frameworks & Multi-Modal Imaging Fusion in Agri-Food

Description: As a PhD Researcher I focus on the intersection of artificial intelligence and the agri-food sector. The project develops 4D data frameworks utilizing multi-modal imaging fusion, specifically combining 3D X-ray CT scan simulations and optical data. By applying deep learning, 3D shape modeling, and 3D Gaussian Splatting to non-destructive testing, the goal is to accurately predict the physiological parameters of fruit for automated quality control.

Date: May 2026 – Present

Deep Learning Computer Vision 3D X-ray CT Multi-Modal Fusion Non-Destructive Testing
AI-based crop modelling project image

AI-based Crop Modelling and Data Engineering (FieldBigData Project)

Description: As part of the FieldBigData project at Wageningen University & Research (WUR), this work focuses on developing data engineering pipelines for AI-driven crop modelling. Using the APSIM (Agricultural Production Systems sIMulator) tool, large-scale agricultural simulations are generated and processed to extract only relevant variables for model integration. The goal is to support predictive models of crop growth and yield estimation using heterogeneous environmental and management data. This project bridges simulation modelling, data preprocessing, and machine learning, contributing to smarter agricultural decision support systems.

Date: August 2025 – May 2026 (10 months)

Python APSIM Data Engineering AI Modelling Crop Science
MSc Thesis Project image

MSc Thesis Project: AI and Citizen Science to combat food fraud

Description: This project focused on leveraging computer vision techniques to detect food fraud related to the geographical origin of bananas. RGB image data were sourced from the iNaturalist platform and processed using two state-of-the-art models, Grounding-DINO and Grounded-SAM, to streamline the detection and segmentation of banana bunches from their backgrounds. Subsequently, a Convolutional Neural Network (CNN) was developed, utilizing the pretrained MobileNet model, to classify and analyze the isolated banana images with precision. This approach demonstrated the potential for automated and scalable solutions to address food fraud issues.

Date: September 2024 - March 2025 (6 months)

Python Computer Vision CNN Image Segmentation Food Fraud
Dietary supplements database project image

Creation of Database for Dietary Supplements

Description: Dietary supplements are a key part of the Dutch population's nutrient intake, but accurate and comprehensive data is crucial for monitoring. This project, conducted in collaboration with the National Institute for Public Health and the Environment of Netherlands (RIVM), developed a web scraping pipeline to automate real-time extraction of dietary supplement data from three major Dutch retail websites: Etos, Kruidvat, and Vitamin Store. Using tools like Beautiful Soup and Scrapy, we extracted, cleaned, and standardized data on 1753 unique supplements with over 500 ingredients. The pipeline reduced manual effort, ensured high accuracy, and significantly enhanced the coverage of supplement data compared to existing databases.

Date: May - July 2024 (2 months)

Python Scrapy BeautifulSoup Data Engineering Nutrition
Food consumption prediction project image

Prediction of Food Consumption to Reduce Food Waste

Description: This project, conducted in collaboration with Wastewatchers, focused on minimizing food waste by leveraging consumer behavior data provided by Wastewatchers. For a specific location, models were developed using PyCaret and advanced time series analysis to forecast daily consumption trends and the consumption of the most wasted products. By predicting consumption, the project aimed to indirectly predict waste and identify opportunities to reduce it. The workflow included data cleaning, feature engineering, and stacked modeling, with accuracy as the primary evaluation criterion. The models outperformed alternative approaches, offering actionable insights to optimize production processes and reduce food waste effectively.

Date: March - May 2024 (2 months)

Python Time Series PyCaret Food Waste Reduction
Extended reality and data science in education project image

Extended Reality for Data Science Education (Data Factory Hackathon)

Description: During the Data Factory Hackathon at Wageningen University & Research in December 2023, our team developed a virtual reality (XR) concept for teaching data science. The idea was to create an interactive 3D environment where students and lecturers can literally "grab" and explore data. For example, instead of looking at a static PCA plot on a slide, users can walk around a 3D scatter plot, select individual points, and inspect how they move when switching between principal components. This immersive interaction aims to make abstract concepts like PCA more intuitive, while also allowing students to test and deepen their understanding through direct manipulation of the data. Our prototype concept was awarded 3rd place.

Date: December 2023

Unity Extended Reality Data Visualisation Data Science Education
COVID-19 mortality prediction project image

Prediction of COVID-19 Mortality Using Food Intake Data

Description: This study investigated the relationship between food intake and COVID-19 mortality rates across 170 countries, using data from Kaggle and the Food and Agriculture Organization (FAO). The project applied Machine Learning (ML) techniques to predict COVID-19 death rates based on dietary patterns and identify the most effective predictive model for decision-making. Through data preprocessing, feature engineering, and model evaluation, the XGB Regressor emerged as the best-performing model, demonstrating strong predictive accuracy. The findings provide actionable insights into the potential impact of dietary habits on health outcomes during the pandemic.

Date: November 2023 - January 2024 (2 months)

Python Predictive Modelling Data Engineering Nutrition
BSc Thesis project image

BSc Thesis: Sentiment Analysis in Online Food Delivery

Description: The rapid growth of user-generated data, such as reviews and preferences, offers businesses new opportunities to refine strategies through sentiment analysis. This process identifies opinions and their polarity (positive, negative, neutral) using machine learning and lexical resources, helping businesses enhance customer satisfaction, marketing, product quality, and profitability. This study examines the potential of sentiment analysis in assessing consumer behavior in online food delivery. It includes a review of applications in the food and beverage industry (2011–2021) and an overview of top sentiment analysis tools. The analysis focuses on 300 Greek-language reviews from a leading online food delivery platform in Greece, offering insights for improving product quality, customer relationships, and strategies. The precision of two sentiment analysis tools is compared to human evaluation, highlighting areas of online orders that impact consumer satisfaction and need improvement.

Date: January - July 2022 (6 months)

Python NLP Sentiment Analysis Online Food Delivery