Apple Picking Robot

To shape the sustainable food supply in Europe through the deployment of Apple Picking Robots, cooperation from multiple perspectives and disciplines is needed. For example, there is a need for domain knowledge of cultivation, experience knowledge with specific machine construction, control knowledge and technological knowledge. With these robots, large savings on herbicide emissions can be made indirectly (through chain optimization) and crops can be grown more sustainably. It also provides a solution to the labor shortage in the sector and allows healthy food to continue to be produced locally.

Cooperation with multiple organization is not easy, but crucial. Objectives of Munckhof and RIWO are respectively focused on developing and supplying picking robots and picking units with control sets. Wageningen Universitity & Research, Fontys and Saxion are developing new knowledge with which they can strengthen their research practice, support companies with technical new developments and strengthen related bachelor, minor and master programs at the institutes, thus better meeting the needs of the field.

The goal of this innovation project is to develop a robot suitable for automatic apple picking in European orchards (later worldwide) and also suitable for automatic pruning. Securing world food production over time can only be achieved through highly automated systems. These must save labor, but also support the automation of cultivation decisions. With the Apple Picking Robot, the monotonous, short-cycle repetitive actions of agrifood workers are prevented on the one hand, while technology is used to accelerate sustainability and greening. With knowledge from this challenging project, we are developing our technology platforms RIBOTIC Perception, RIBOTIC Manipulation. We are also using the RIMOTE Integration platform and RINEWABLE Battery.

Via Precision Farming and linked data analysis, the yield and quality of the harvested fruit can be measured directly in the orchard (before cooling) and used for measurement per (apple) tree with GPS location determination instead of per hectare. Subsequently, yield and quality can be controlled and improved in the future. This data is read and recorded in real time and used by the grower or sales organization to determine more insight and added value of the fruit harvest. Also, this data can be applied for static registration and knowledge development in the Agrifood domain to optimize fruit varieties, tree cultivation and 2D tree geometry. This information can also be used to deduce how healthy a tree is, thus ensuring sustainability. In short, this makes much more practical measurement data per tree or per meter of row length per tree species available and gives a much more accurate insight than information per hectare.

By specifically registering and treating per tree, this data will also lead to savings on crop protection products and fertilizers, and thus biodiversity and nature in and around orchards in fruit farming.