This project aims to reduce food waste by developing fundamental technologies for autonomous quality assessment. We will develop self-learning systems that can simply be retraining based on examples provided by food experts. Not having to rely on computer experts, will greatly increase the use of such objective quality measuring systems in the industry.
Quality assessment of produce is important in many parts of fresh-food chains. Preferably, every individual food item is checked, as with accurate quality information of every single product, logistics can be optimized and food losses can be prevented. However, in current practice, quality assessment often relies on manual labour. The costs of manual labour often results in checking only a few samples from a batch, hoping that the samples represent the batch well. Manual labour, furthermore, is unreliable, as human judgement of quality is very subjective. This poor judgement of quality results in unnecessary loss of produce. This links to efficiency in resource conversion, point 2 in High Efficiency Use of Resources.