Machine Learning Based Product Quantity and Quality Prediction in Food Production

IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1–10

Abstract

In food production, numerous factors significantly influence both the growth and quality of products. Predicting product quality and quantity in an early stage and precise way is particularly difficult. For accurate order planning, reducing waste, and ensuring customer satisfaction, production planners need to know the provided product quality and quantity from the suppliers in a precise way. To address this need and support planners in their daily decision-making processes, an approach has been created that involves the development and application of machine learning models aimed at initially predicting product quantity and subsequently product quality in two distinct phases. The developed machine learning pipeline focuses specifically on the case study of raspberry production. By utilizing production and field data, alongside publicly available weather data and synthetic datasets, various machine learning models were tested and validated.

Authors 2

1. Jan Hendrik Hellmich Aachen Fraunhofer Institute for Production Technology

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology (IPT),Production Quality,Aachen,Germany

2. Maximilian Brochhaus
   Affiliation as printed

   Cire di Pergine Valsugana (Trento),Production Research and Development Sant'Orsola s.c.a.,Italy