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AI-supported selection procedure for spectral sensors based on technical and economic characteristics

Zusammenfassung

This study presents an AI-supported spectral sensor selection process that combines technical and economic criteria to recommend the optimal sensor for specific applications, such as quality control of roasted coffee beans. Using a comprehensive database of spectral sensor characteristics, the SMART algorithm guides decisions that focus on both performance and cost-effectiveness. Our methodology involves simulating spectral responses and using an AI model to evaluate sensor effectiveness in classifying coffee bean types. Initial results highlight the method's ability to optimise sensor selection, effectively balancing performance with budget considerations, and underscore its potential to improve user decision making in technology applications and enhance their digital sovereignty.

Beschreibung

Menz, Patrick; Klein, Lauritz; Herzog, Andreas (2024): AI-supported selection procedure for spectral sensors based on technical and economic characteristics. INFORMATIK 2024. DOI: 10.18420/inf2024_111. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-746-3. pp. 1261-1267. KoLaZ-24-Kolloquium Landwirtschaft der Zukunft 2024: Digitale Souveränität in der Landwirtschaft, der Lebensmittelkette und dem ländlichen Raum: Trotz, mit oder durch KI?. Wiesbaden. 24.-26. September 2024

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