SAGA (Swarm Robotics for Agricultural Applications) is funded by ECHORD++, a European project that wants to bring the excellence of robotics research “from lab to market”, through focused experiments in specific application domains, among which is precision agriculture. SAGA is a collaborative research project that involves: the Institute of Cognitive Sciences and Technologies (ISTC-CNR) of the Italian National Research Council (CNR), which provides expertise in swarm robotics applications and acts as the coordinator for SAGA’s activities; Wageningen University & Research (WUR), which provides expertise in the agricultural robotics and precision farming domains; and Avular, which provides the AUV quadcopter platform that is capable of running the algorithms provided by the other partners.
The goal of the experiment was to prove the applicability of swarm robotics to precision farming. The application of swarm intelligence principles to agricultural robotics could lead to disruptive innovation, thanks to the parallel operation of multiple robots and their cooperation. The experiment aimed at demonstrating the advantages of swarm robotics compared with the current state of the art within the context of a monitoring/mapping scenario.
By exploiting swarm robotics principles, a group of small UAVs collectively monitor a sugar beet field and cooperatively map the presence of volunteer potatoes. Volunteer potatoes are a major threat as they spread diseases (e.g., late blight) and facilitate harmful soil nematodes. Hence, it is very important to precisely map the presence of volunteer potatoes to facilitate weed control procedures.
An existing multi-rotor UAV is enhanced with on-board camera and vision processing, radio communication systems and suitable protocols to support safe swarm operations. On-line, on-board processing is key for weed detection, as the UAV needs to fly close to the plants to obtain images of sufficient quality, and this should be done only when there is some evidence of weed to reduce flight time and energy expenditures. Hence, a trade-off between accuracy and flying costs must be considered. The proposed solution exploits multiple UAVs that can focus on areas of interest while abandoning those areas of the field that do not require closer inspection. The usage of multiple UAVs is particularly important in case weed control actions are required (e.g., micro-spraying), which would be unpractical with a single platform.
The experiment has developed robust on-board vision routines capable of supporting local navigation and discriminating volunteer potatoes from sugar beets. Collectively, the robots build a map of the field with tags that convey precise information about the presence and amount of weed in the different parts of the field.
Partners: Wageningen University & Research, CNR
Supported by: ECHORD++