Seamless Machine Data Management for Agricultural Vehicles within the iGreen Infrastructure

Efficient data exchange among machines of different make beyond the scope of ISOBUS poses a significant challenge in modern agriculture. In order to overcome the shortcomings of vehicle centric and manufacturer specific solutions a new approach has to be found. The main objective of the iGreen project is to enable seamless data exchange among agricultural stakeholders and equipment combined with location-based services by applying semantic web technology. This paper is intended to provide an overview of the approaches taken to connect agricultural machines to this knowledge management infrastructure. Such a methodology can be regarded as a key enabler for more efficient and traceable production processes in the agricultural sector in the future. Introduction The global demand for agricultural products is growing rapidly. The two main drivers for this fact are the steadily growing demand for food due to the growing and developing world population on the one side and the increasing importance of biomass as an energy source on the other. In order to keep pace with this development in a sustainable and economic manner, improvements in agricultural productivity are required. Hence, the research project iGreen funded by the German Federal Ministry of Education and Research (BMBF) was started in 2009. Its purpose is to investigate how web-based technologies can be utilized to meet the described challenge. Therefore, an infrastructure is set up that enables efficient data exchange by providing innovative online services. Today, manufacturers of agricultural machines offer basic telematics solutions that allow to monitor machines remotely by providing information on its position, fuel consumption and workload in real-time. Even though such systems help to increase machine productivity, they mainly focus on optimizing single machine’s productivity only and are not compatible to each other. In order to overcome this productivity gap, an infrastructure enabling online data exchange between machines of iGreen-compatible manufacturers is one of the core objectives of the project. Besides basic telematics functionalities it enables advanced logistics and dispatching to meet the needs of mixed fleet operations. The iGreen Project As previously stated the intent behind this undertaking is to combine data from different public and private stakeholders to provide location-based services for the agricultural sector. For this purpose, 24 partners from research (universities, research institutes, public agencies) and industry (equipment manufacturers, IT companies) came together to leverage the application of state-of-the art semantic web technology combined with sophisticated and customer-oriented business solutions based on mobile network services. Due to this broad range of expertise among the project partners, a unique infrastructure will be provided as a powerful tool for the agricultural sector w.r.t. data exchange among cooperating parties and a unified platform for effective collaboration within heterogeneous (multicoloured) fleets. If deployed the right way, such a system will be the key component to speed up production processes, to increase product yield and quality, as well as to fulfill increasing transparency and food safety requirements in the future. Data Management within iGreen The vision of iGreen is to establish the methodologies and an according infrastructure to allow agricultural equipment to be included into a network in order to profit from services provided by various stakeholders participating in it as depicted in figure 1. Fig. 1: Data exchange concept via the iGreen network Here, data of different physical origins is aggregated and propagated using semantic web technology. This is achieved via a distributed structure for data storage (spanned by the so called “Online Boxes”) that contains mechanisms for mediating content between potentially collaborating entities. Within this setup, there exist two main groups that contribute information. The first group is formed by agricultural machines that capture data during operation with their sensors. The second one consists of stakeholders such as OEMs, suppliers, public sources, and companies offering proprietary data and services that share their knowledge via the structure. The technical challenges that need to be overcome in order to be successful span from proper ways for data aggregation on an intra-machine level (such as sensor fusion in modular vehicle & implement combinations) all the way to the realization of distributed data storage/sharing and authentication or authorization functionalities to ensure data privacy. Needless to say this task does not get easier if one considers the heterogeneity of the types of data that ranges from machine data to hardware information, task management, documentation/reporting data, prescriptions, or environmental information like the weather forecast or prognosis for pest infestation. Thus, mechanisms need to be established that allow the infrastructure to manage this vast load of data in an efficient way. Two key aspects that will be discussed in following sections in more detail are the Machine Connector and the Intelligent Vehicle Data Management system that both reside on the machines themselves. The former is responsible for managing data consistency and exchange on a technical level, while the latter ensures proper signal-level data processing and fusion within a single machine or machine combinations.