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Using Deep Learning to Combat Food Waste: A Data Science/Big Data Practical Project

A special achievement for our students in the Master’s program in Business Informatics with a focus on Data Science, AI & Consulting: They presented their project results as a poster presentation at the TDWI 2026 trade show in Munich at the end of June 2026 to an audience of industry and research professionals. The project was supervised by Prof. Dr. Maximilian Coblenz (Department III) as part of the “Data Science/Big Data Practical Project” course and was conducted in cooperation with Wageningen University & Research in the Netherlands, where the data used was collected. TDWI is one of the most prestigious conferences for data science, analytics, and AI in the German-speaking world.

About the poster presentation

Specifically, the project focused on classifying the quality of apples. Manual sorting of apples is costly, subjective, and hardly scalable, while misclassifications lead to economic losses and quality risks. The project explored an automated approach to three-level quality classification based on modern deep learning architectures and transfer learning under real-world conditions—an approach that is economically viable even with small datasets and does not require large amounts of proprietary data. The automated and scalable classification method developed in the project reduced classification errors and has the potential to reduce food waste and support data-driven value creation in agriculture.

Subject Matter Expert:
Prof. Dr. Maximilian Coblenz
Services and Consulting Department
maximilian.coblenz@hwg-lu.de

Three apples, each telling its own unique story through its signs of wear and tear, are presented in a playful arrangement that highlights their imperfections.

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