The ability to optimize inputs at spatial scale has been enabled with the use of Variable Rate Applications (VRA). Despite the fact that there are numerous VRA systems aimed at arable crops, specific systems for orchard management utilizing precision monitoring and application of the orchards are still lacking. These cropping systems face increasing market pressure to produce quality products, and provide a detailed traceable system for the origin of the product including, the treatments and the conditions that have occurred during the production.

A number of technologies originating from ICT, has been successfully applied in agriculture. Nevertheless, sensor solutions have not been adopted into common agricultural practice. Two main gaps were identified, that this project is approaching to abridge: 1. Reliability - systems with insufficient reliability for everyday use in harsh conditions, and limited robustness of calibrations. 2. Usability - techniques were applied as isolated approaches without synergy of sensor data.

The target of 3D-Mosaic is to promote precision management of orchards by means of a decision support system (DSS) aiming to optimize efficiency of inputs including water and to diminish the environmental footprint of fruit production. The DSS will apply information and communication technologies (ICT) for precision management of the most economically relevant tree crops, apple and citrus. For this purpose, sensors, monitoring strategies, information processing and decision support systems will be developed. Together, these will produce maps for orchard management including irrigation.

This project will apply precision agriculture techniques to improve the supply of high quality cherries from our orchards.

The main objectives of this project are (1) to develop a working prototype of an artificial fruit sensor system that mimics the size, shape and composition of fresh fruit, and thereby also its thermal behaviour, and that logs core and surface temperatures, (2) develop a manufacturing method for this artificial fruit, (3) prove the feasibility of the prototypes for monitoring fruit temperature history in cold chain operations by lab and field experiments.

Abstract AUTOPIC is a multi disciplinary project aimed at mechanising the harvesting of soft fruit through the use of autonomous vehicles and robotics. Partners include Harper Adams University, the Shadow Robot Company, Interface Devices Limited and the National Physical Laboratory. The project is timely since the source of migrant seasonal fruit pickers is no longer supported by the Seasonal Agricultural Workers Scheme and in general migration is being discouraged by government policy.

This project is funded by the innovation program of the Federal Ministry of Food and Agriculture (BMEL), funding agency is the Federal Office for Agriculture and Food (BLE). Control and optimization of harvest and storage for apple with data-based prognosis models for improvement of fruit-quality and reduction of storage-losses In Germany, fruit quality losses and rots occurring from harvest to consumption are estimated to be up to 18%. In addition to the direct loss of food for human consumption there are also considerable losses of natural resources and human labour.