Project Context and Objectives:
The main objective of the EU-FP7 project CROPS (GA 246252) is to develop a highly configurable, modular and clever carrier platform comprising a carrier plus modular parallel manipulators and “intelligent tools” (sensors, algorithms, sprayers, grippers) that can easily be installed onto the carrier and that are capable of adapting to new tasks and conditions. Both the scientific know-how and a number of technological demonstrators will be developed for the agro management of high value crops like greenhouse vegetables, orchard fruits, and grapes for premium wines. The CROPS robotic platform will be capable of site-specific spraying (targeted spraying only on foliage and selected targets) and selective harvesting of fruit (i.e., it will detect the fruit, determine its ripeness, move towards the fruit and grasp it and softly detach it). Another objective of CROPS is to develop techniques for reliable detection and classification of obstacles and other objects to enable successful autonomous navigation and operation of the platform in plantations and forests.
The CROPS project context and the CROPS objectives are described per work package.
The CROPS project consists of the following 13 work packages, with between brackets the lead beneficiary.
1. Systems engineering and architecture (TUM)
2. Sensing (CSIC)
3. Manipulators and end-effectors (TUM)
4. Intelligent sensor fusion and learning algorithms (BGU)
5. Sweep pepper – protected cultivation (WUR)
6. Harvesting systems in orchards: grapes and apples (KULeuven)
7. Precision spraying (UL)
8. Forestry (UMU)
9. Training (BGU)
10. Dissemination (UniMI)
11. Final demonstration (UniMI)
12. Economics, social aspects, sustainability and exploitation (WUR)
13. Coordination (WUR)
Project results:
Executive Summary:
The following results were obtained in the CROPS project.
For the robotic middleware ROS was chosen as software framework. The supervisory control system as well as the
high-level software architecture have been developed and tested.
The design and implementation of the sensory systems for CROPS have been completed and tested , including the
sensory system for detection and localisation of fruits in orchards and greenhouses, the sensory system for detection
and classification of objects, the sensory system for fruit ripeness evaluation, the sensory system for diseases
detection in crops and the sensory system for ground bearing in forestry.
A nine degree of freedom manipulator was manufactured. End-effectors were tested. A prototype of a canopy sprayer
was manufactured. The first manipulator prototype, the grippers, and the precision spray end-effector were tested in
laboratory and field experiments. Bases on these tests a final manipulator was designed and tested in a commercial
greenhouse for harvesting sweet pepper.
For sensor fusion the system architectures for sensing, grasping and fusion and algorithms for sensor fusion, learning
in sensing and grasping were developed. The adaptive sensor fusion algorithm was implemented and tested for apples
and sweet pepper and for disease and ripeness detection .A method for construction of a discrete fuzzy grasp
affordance manifold based on learning from human demonstration was developed and tested.
For sweet pepper harvesting the requirements for a harvesting robot were obtained. The manipulator, a platform to
transport the manipulator through the greenhouse, a gripper and a sensing system were integrated into a complete
system. This system was successfully tested and demonstrated to growers in both a laboratory setting as in a
commercial greenhouse.
For harvesting of grapes and apples requirements have been defined based on discussions with the growers. To
maximize the visibility and reachability of the fruits, the so-called ‘walls of fruit trees’ growing system has been chosen.
The CROPS manipulator, grippers, sensors and software architecture have been tested both in laboratory as well as in
apple orchards and as in vineyards. All the modules have been integrated into one system, which was successfully
tested in the laboratory and in an apple orchard.
For canopy optimised spraying and close range precision spraying requirements were selected in discussion with
spraying specialists and growers. A canopy optimised sprayer was designed as a trailed sprayer with centrifugal
blower. An eight DOF hydraulic driven manipulator with three arms was used. Orchard experiments were performed
with the canopy optimised sprayer during 2013. A good spraying quality was achieved with significant reduction of
pesticide use. The close range precision spraying was focused on testing disease detection with various sensing
principles. The manipulator with waterproof protecting case, sensors precision spraying end-effector were integrated in
a precision spraying robot for viniculture. The robot was successfully tested in a greenhouse environment and the
attained pesticide reduction was 84%.
For the forestry application the requirements for the detection of bushes, rocks, and trees and for the estimation of
ground bearing capacity (for propulsion of forest machines) have been specified. Two sensory system for detection and
classification of trees and humans respectively have been evaluated in a forestry environment.
As part of the training work package 44 graduate students were recruited in the project. 33 graduate courses were
proposed at the participating universities for the students. The students took 17 advanced courses, participated in
conferences and visited various universities and industries. Eight different workshops took place during the CROPS
meetings.
For dissemination the website www.crops-robots.eu has been made. 25 articles in technical journals, 10 articles in
farmer’s magazines, 69 papers in conference proceedings and 12 papers on scientific journals were published. 76
presentations on conferences were given. The final CROPS workshop was held during the AgEng2014 conference in
Zurich, July 2014.
Project partners:
1. Stichting Dienst Landbouwkundig Onderzoek (WUR), The Netherlands
2. Katholieke Universiteit Leuven (KU Leuven), Belgium
3. Ben-Gurion University of the Negev (BGU), Israel
4. Univerza V Ljubljani (UL), Slovenia
5. Umea Universitet (UMU), Sweden
6. Università degli Studi di Milano (UniMI), Italy
7. Agencia Estatal Consejo Superior De Investigaciones Cientificas (CSIC), Spain
8. Technische Universität München (TUM), Germany
9. CNH Industrial Belgium NV (CNHi), Belgium
10. Instituto De Investigaciones Agreopecuarias (INIA), Chile
11. FORCE-A SA (Force_A), France
12. Festo AG & Co (Festo), Germany
13. Sveriges Lantbruksunivesitet (SLU), Sweden.