Objective
Harvesting of white asparagus is still performed manually due to the absence of cost-effective harvesting technology. To date, there are no harvesting machines available, to realise economically viable harvesting cost reduction. Next to undesired working conditions, manual harvesting is characterised by high harvesting cost: 50% labour costs, 30% yield loss and 25% low quality asparagus.
Cerescon has developed an automated harvesting technology for white asparagus. This will result in a 50% harvesting cost reduction, 20% increase in yield and 20% increase in production of top class asparagus. Three patented technologies are key: subsurface asparagus detection, a precise cutting mechanism and high processing. This results in 50% harvesting cost reductions and significant reduced losses (from 30 to 5%). The Wageningen University and Research (WUR) conducted a successful technical evaluation and validation of our technology.
We now need to scale-up and improve our current prototype to the final setup with all functions while realizing 20% overall cost price reduction. Three important steps form this project: 1) scale up Sparter to have a 3-row configuration, 2) realize cost price reduction of core modules and, 3) demonstrate the reliability, robustness and verify yield and quality increase during two full asparagus harvesting seasons.
The user needs have been defined by the Cerescon User Group. The last two years, eight leading asparagus farmers from the Netherlands, Germany and Spain have provided us with their challenges, industry knowledge, application know how and their requirements. They have all signed a Letter of Intent and are the first customers.
Our commercialisation strategy focuses on European asparagus farmers (€900 million market) who suffer from a harvesting cost increase. Europe represents almost one third of the world's asparagus market, providing a large primary customer base.
Fields of science
- natural sciencescomputer and information sciencessoftwaresoftware applicationssystem softwareoperating systems
- social sciencesmedia and communicationsjournalism
- natural sciencescomputer and information sciencesdata sciencebig data
- agricultural sciencesagriculture, forestry, and fisheriesagriculture
- social sciencessociologydemographyhuman migrations
Programme(s)
Funding Scheme
SME-2 - SME instrument phase 2Coordinator
5591 RC HEEZE
Netherlands
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.