Assembly of European Horticultural Regions

EU projects

Lowinfood

The LowinFood project and UNITUS presented an AI technology that provides retailers with accurate forecasts of sales.

LOWINFOOD supports the reduction of the loss and waste in the European Union by demonstrating innovative solutions following a multi-actor approach that involves actors along the entire value chain.  

 

During the SIDEA Annual Conference in Italy (sept 2023) , Roberta Pietrangelli from UNITUS presented the results of a research on the efficacy of a newly developed machine learning technology that provides retailers with accurate forecasts of sales.

 

Fresh fruits and vegetables account for 54% of the total food loss and waste in Europe, making them the most wasted food products, and also represent the main fraction, in mass of the food waste generated at the retail level. The primary drivers behind this high wastage can be attributed to the perishable nature of fresh fruits and vegetable products and the inadequate technological equipment at retail stores to support their preservation.

 

Artificial intelligence (AI) has the potential to push changes in food systems. Specially, machine learning is widely studied in the field of food waste prevention to prevent overproduction, detect non-compliance causes and target products in the appropriate market through forecasting, monitoring and grouping.

 

The study contributed to determining whether the stores can improve the efficiency of their orders, thus avoiding surplus ordering, by using the forecasts as input data for decision making. In turn, this helps understand and assess the extent to which these innovations can reduce fresh fruits and vegetables waste at the retail level. The results showed that considerable improvements are possible when considering the information that is available to food managers to place orders.

 

For more information about the project, visit the following media :

  

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No.101000439

 

 

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