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Digitization of agricultural processes is advancing fast as telemetry data from the involved machines becomes more and more available. Current approaches commonly have a machine-centric view that does not account for machine-machine or machine-environment relations. In this paper we demonstrate how to model such relations in the generic semantic mapping framework SEMAP. We describe how SEMAP’s core ontology is extended to represent knowledge about the involved machines and facilities in a typical agricultural domain. In the framework we combine different information layers – semantically annotated spatial data, semantic background knowledge and incoming sensor data – to derive qualitative spatial facts and continuously track them to generate process states and events about the ongoing logistic process of a harvesting campaign, which adds to an increased process understanding.

In diesem Beitrag wird dargelegt, wie Digitalisierung von Geschaftsprozessen am Beispiel der Zusammenarbeit zwischen Landwirten, Lohnunternehmen, Betreibern von Biogasanlagen und Finanzdienstleistern in der Landwirtschaft erfolgen kann. Hierbei sind die sichere Vernetzung, die Datentransparenz und die nachvollziehbare Speicherung von Prozessanderungen von zentraler Bedeutung. Ein wichtiger Ansatz fur die Digitalisierung der unternehmensubergreifenden Zusammenarbeit ist die Weiterentwicklung und Anwendung der Blockchain-Technologie fur den betrachteten landwirtschaftlichen Anwendungsfall. Aufbauend auf der Blockchain-Technologie wird in diesem Beitrag ein Ansatz zur Automatisierung mithilfe von Smart Contracts und Smart Objects vorgestellt. Neben dem Tracking des Ernte- und Transportprozesses liegt der Fokus insbesondere auf der Anbindung einer Payment-Plattform an die Blockchain und dem entsprechenden Regelwerk, welches mittels Smart Contracts eine automatisierte und papierlose Transaktion ermoglicht. Zur Prozessbeschleunigung und zur automatischen Prozessuberwachung werden die Landmaschinen in der logistischen Kette mit dezentralen Steuereinheiten (Smart Objects) ausgerustet.

Modern telemetry-based applications enable farmers to work more efficiently and to improve the utilization of their farmland. However, connectivity between agricultural machines and central farm management information systems is almost exclusively realized via mobile networks and, thus, suffers from the challenging network conditions, which often characterize agricultural scenarios. Robust networking with reliable communication across multiple machines is one of the fundamental challenges in highly distributed agricultural processes, such as the harvesting of crops. In this paper, we present the design and evaluation of a new hybrid-opportunistic network architecture that combines multiple Internet and ad-hoc machine-to-machine (M2M) connections simultaneously to provide robust and reliable communication in a decentralized network.

Digitization of agricultural processes is advancing fast as telemetry data from the involved machines becomes more and more available. Current approaches commonly have a machine-centric view that does not account for machine-machine or machine-environment relations. In this paper we demonstrate how to model such relations in the generic semantic mapping framework SEMAP. We describe how SEMAP’s core ontology is extended to represent knowledge about the involved machines and facilities in a typical agricultural domain. In the framework we combine different information layers – semantically annotated spatial data, semantic background knowledge and incoming sensor data – to derive qualitative spatial facts about the involved actors and objects within a harvesting campaign, which add to an increased process understanding.

This paper presents the SEMAP framework designed to maintain and analyze the spatial data of a multi-modal environment model. SEMAP uses a spatial database at its core to store metric data and link it to semantic descriptions via semantic annotation. Through in-built and custom-made spatial operators of a PostGIS database, we enable the spatial analysis of quantitative metric data, which we then use in the context of semantic mapping. We use SEMAP to query for task-specific sets of spatial and semantic data, to create semantically augmented metric navigation maps, and to extract implicit topological information from geometric data.