Democratizing spectral imaging in agrifood
One of the most important challenges the modern world is facing is food insecurity, while little progress has been achieved at introducing non-destructive and reliable food quality assesment methods at both pre- and post-harvest stages. SPECTROFOOD aspires to develop digital technology solutions for use in the agrifood value chain, combining innovative Hyperspectral Imaging Systems, Artificial Intelligence techniques, analytic tools, and data platform – related solutions. At its core, SPECTROFOOD is formed by four use cases, demonstrated with end-users from the primary production, processing, packaging, and distribution across different high-value and perishable crops. SPECTROFOOD recognises that solutions must be practicable and commercially viable in order to ensure citizen ‘buy in’ and industry implementation. Therefore, will create easily adaptable digital technology solutions able to be harmonised with existing quality evaluation systems, as well as widely reusable mechanisms. The SPECTROFOOD digital technology solution aims to reduce food waste and enable optimal use of production inputs, taking steps for a transition towards resilient and sustainable agri-food value chains.
Goal and context
- Democratize the use of spectral imaging
- Optimize resource use efficiency both at pre- and post harvest level
- Reduce food waste
- Reduce packaging
- Increase production
Obj 1: Advances in the exploitation of emerging sensing technologies across the supply chain
- Develop expertise in the use of HIS as a robust quality evaluation tool at both field and post-harvest level.
- Reveal HIS sensor-specific limitations and define “global” imaging principles.
Obj 2: Study the effects of the in-field treatment on the post-harvest product quality characteristics
- A spatiotemporal analysis of the critical characteristics.
- More transparent and reliable product quality evaluation.
Obj 3: Analysis and availability of data to all the stakeholders involved
- Correlation of product parameters and significance analysis will boost data utilisation across all supply chain stages.
- Big volumes of spectral data will be translated into product quality indices.
- Better understanding and forecast of the product life cycle.
- Quality monitoring & production increase.
- Waste minimization.
- Appropriate product labelling (quality, safety, authenticity, and standards compliance).
- Inputs decrease.
- 10% food waste reduction.
- Make agriculture more attractive to young people.
- Give access and transparency to value-chain actors.
- Raise awareness on agrifood products’ lifecycle.
- Food loss and Waste prevention.
- Sustainable Food Production.
- Sustainable Food consumption.
- Sustainable Food processing and Distribution.