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- ZeitschriftenartikelThe Role of Linked Data in Agriculture(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Dengel, Andreas
- ZeitschriftenartikelMother Tongue?(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Visser, Ubbo
- ZeitschriftenartikelDetection of Field Structures for Agricultural Vehicle Guidance(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Fleischmann, Patrick; Föhst, Tobias; Berns, KarstenThis paper introduces a model based detection approach for typical structures in the agricultural environment. In contrast to other existing approaches in this area it exclusively relies on distance data information generated by a laser scanner which makes the detection robust against varying illumination. Additionally, the method was designed to be easily adaptable to different agricultural structures as well as to minimize computation power. To test the performance and the capabilities of the presented approach a prototypical baling assistance system was implemented where a tractor had to follow a straw windrow while the implement created round bales.
- ZeitschriftenartikeliGreen—Intelligent Technologies for Public-Private Knowledge Management in Agriculture(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Bernardi, AnsgarModern agriculture will profit in many aspects from information sharing and knowledge exchange, in particular involving public-private collaboration. As an enabling prerequisite, participants in agricultural production shall become members of a comprehensive communication network which relies on message exchange using universal data formats and online ontologies representing shared data models and vocabularies. The research project iGreen unites 23 partners from agricultural technology, application, and academia, and applies semantic technologies at the heart of specifications, reference implementations, and numerous service prototypes.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013)
- ZeitschriftenartikelData Mining and Pattern Recognition in Agriculture(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Bauckhage, Christian; Kersting, KristianModern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture. Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Yet, precision farming is a fundamentally new domain for computational intelligence and constitutes a truly interdisciplinary venture. Accordingly, researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that allow for operation through users that are not necessarily trained computer scientists. We present approaches and applications that address these challenges and underline the potential of data mining and pattern recognition in agriculture.
- ZeitschriftenartikelBio-inspired Sensor Data Management for Modular Agricultural Machines(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Blank, SebastianIn agricultural applications modularity is common practice from the hardware perspective (e.g. combination of one tractor with different implements). This fact, however, is not adequately reflected in the way sensor data is processed for changing machine combinations so far. Thus, a new methodology for onboard processing of sensor data with a holistic view on the entire machine combination was developed. Due to the fact that the design principles and alignment mechanisms found in social insects and higher animals served as a source of inspiration it is both lean and sophisticated and thus well suited for the given application.
- ZeitschriftenartikelMechatronic System for Mechanical Weed Control of the Intra-row Area in Row Crops(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Gobor, ZoltanThe dissertation discusses the problem of mechanical weed control within the intra-row area in row crops. A novel concept featuring an electrically driven weeding tool similar to that applied in rotational crop thinning systems is proposed. The special characteristics of the system include the ability to tune rotational speed of the hoeing tool according to forward speed of the carrier, estimated in-row distance between adjacent plants and observed angular position of the arms. A laboratory prototype including an interim solution for detection of the single plants was developed and tested.
- ZeitschriftenartikelAI, Robotics and the Role of ECCAI(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Visser, Ubbo
- ZeitschriftenartikelSpecial Issue on Artificial Intelligence in Agriculture(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Dengel, Andreas