The future for food production, handling and processing is hands-free farming, hands-free harvesting & hands-free assessment and monitoring. The skilled workforce that accepts repetitive tasks under harsh climatic conditions is decreasing rapidly. There’s a growing need to automate labour intensive tasks. Next to that robots also provide new assets such as: acquiring spatial specific data, able to work in harsh conditions, continuous improvement and even learning capabilities and the ability to work 24/7.
One example of the innovation strength of Wageningen University & Research is Sweeper, the Sweet Pepper Harvesting Robot. Sweeper’s objective is to put the first generation sweet pepper harvesting robots onto the market. The robot will help growers to automate labour in their greenhouses. Will you join us? Let us boost your innovation with Agro Food Robotics!
The availability of a skilled workforce for quality inspection in agri- and food industry is decreasing rapidly. Because of that there’s a growing need to automate tasks such as colour assessment of fruit, vegetables and processed food for sorting purposes, to classify batches or to monitor animal health and welfare or products like milk, meat and eggs. Extra benefits that come within reach by automation are also that the work can be done faster, more objectively, more precise and 24/7 if necessary. The Colour Cabinet is a clear example of the innovation strength of Wageningen University & Research. A precision instrument to objectively measure and analyse the colour of agri- and food products in all parts of the production chain from harvest to retail. Will you join us?
Mobile robots help growers to perform operations in their fields without spending tedious hours in their tractor cabins. Scaling down tractors to mobile robots has therefore a promising future. This will enable nature inclusive agriculture practice such as pixel farming with precise operations for seeding, crop care and harvesting. Furthermore, farmers are not exposed to crop protection chemicals anymore.
Robots could harvest apples, weed in spring and sort stored fruit or vegetables in winter time. This requires robots to learn their tasks in an easy way. Robots should not be task specific, they should be able to learn new operations easily from demonstration or from humans directly. This requires new, innovative and flexible robot systems. Deep learning and artificial intelligence are required to teach robot systems their goals of harvesting apples and weeding fields. Wageningen research includes learning behaviour of robot systems and how to implement this in our future farming and food production systems.
Robots for milking, pushing feed and removing manure have become common practice. However, to let them cooperate an become smater in their tasks new robot developments in livestock farming systems are needed. Special care is needed for the robot-human-animal interactions. We can help you.
Mobile self learning robotics
The autonomous navigation software implemented on the Husky robot is a clear example of the strength of Wageningen University & Research. On the video you see another example of the innovative software implemented on an Autonomous Orchard Robot. This joint research project was carried out by Wageningen University & Research and the Republic of Korea.