Optimized Coverage Map Representation for Bandwidth-Limited Data Exchange of Agricultural Vehicles

Abstract

Sharing location-based data among multiple agricultural machines or with the cloud is a key enabler for cooperative farming in precision agriculture. Wireless communication is essential for real-time synchronization, but bandwidth remains limited, particularly when links are shared across multiple machines and applications. Reducing the amount of transmitted data is therefore a critical requirement. This article proposes an efficient message structure for coverage-map sharing, where polygons serve as the spatial representation of worked area. A globally applicable scaling method is developed to quantize geodetic coordinates at a user-defined resolution, independent of geographic location. In addition, polygon vertices are minimized without loss of relevant information and transmitted as relative coordinates using Google Protocol Buffers. Additional message-size reductions are achieved through optimized selection of a reference point for relative encoding, based on heuristics and a mixed-integer linear programming benchmark. Applied to a real-world dataset, the proposed approach achieves a compression ratio of $4.66$ compared to previous implementations at a resolution of $2 \,\text{c}\text{m}$. These results demonstrate the potential of the method to reduce bandwidth demands and enable robust multimachine and cloud-based cooperation in agriculture.