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Precision Agriculture is becoming ever more relevant as the agricultural industry struggles to come to terms with the environment, economics, traceability, vehicle guidance and crop management. Whilst some benefits have proved elusive, others contribute positively to today's agriculture. Research continues to be necessary and needs to be reported and disseminated to a wide audience.These proceedings contain the reviewed papers from the 7th European Conference on Precision Agriculture. The papers reflect the wide range of disciplines that impinge upon precision agriculture including remote sensing, plant disease and weed detection, yield monitoring, soil sensing, geo statistics and path planning, regional and crop modelling, cooperation and guidance of robots, precision application, ICT in precision agriculture, future farming and European relevance for precision agriculture.The broad range of research topics reported is a valuable resource for researchers, advisors, teachers and professionals in agriculture.Also note that the reviewed papers from the 4th European Conference on Precision Livestock Farming are presented in a companion publication

Autonomous robots for agricultural practices will become reality soon. These mobile robots could take over regular tasks such as scouting for weeds and diseases, plant specific applications, yield and field mapping and for instance the release of info-chemicals for attracting predators of pests. This paper presents WURking, a small sized sub-canopy autonomous robot that can be used for a wide range of tasks on the farm of the future. WURking was designed for navigating within row crops like corn. It consists of a mobile platform with three independently driven steerable wheels. The robot carries ultrasound sensors and a gyroscope used for navigation between the crop rows, end of row detection and headland turning. A camera is mounted to detect objects like weeds. High level control of this robot was implemented using the visual programming language LabView. A data fusion technique is used to extract the position and orientation of the robot relative to the crop rows, from the redundant set of sensor data. Feedback linearization of the non-linear system dynamics yielded a simple linear controller structure which was fed by state estimates generated by a Kalman filter using the raw ultrasound sensor data. The headland turning was based on a proportional controller using data from the gyroscope. This paper contains a description of the robot as well as results of a performance test performed in 2008. This test revealed that the robot was able to navigate through the corn field with a maximum offset of ±10 cm from the centre of the rows and an maximum orientation error of ±0.15 rad.