by Mirco Boschetti (IREA-CNR) and Erwin Schoitsch (AIT)

“Smart Anything, Everywhere” is the new hype around IoT, internet of things, combined with intelligence, autonomy and connectivity. Smart systems are today’s drivers of innovation; in all areas of industry and society, highly automated, intelligent systems are taking over tasks, services – and maybe one day, control of our lives.

by Simon van Mourik, Peter Groot Koerkamp and Eldert van Henten (Wageningen Universiteit)

A key challenge in precision farming is complex decision making under variable and uncertain circumstances. A possible solution is offered by mathematical models and algorithms.

by Manousos Bouloukakis, Christos Stratakis and Constantine Stephanidis (ICS-FORTH)

A unique implementation of precision agriculture in an experimental greenhouse in Crete, Greece, utilises software driven automations to boost greenhouse vegetable cultivation. The system is based on agricultural scenarios for each individual species that grows inside the greenhouse, consisting of the environmental parameters that each vegetable needs for an optimum life cycle, as well as machine learning techniques that allow the system to predict and provide information about possible hazards.

by Andrea Pezzuolo, Luigi Sartori and Francesco Marinello (University of Padua, Italy)

Animal dimensions play a key role in providing data in support of management decisions regarding livestock production. In the last fifty years, manual measurements have been the most common way to get an indication of animal growth. An alternative approach, which overcomes the limitations of manual direct measurements, is to use techniques based on optical detection instruments.

by Christoph Schmittner (AIT), Christian Hirsch (TU Wien) and Ma Zhendong (AVL List GmbH)

Smart Farming – the application of IoT and Industry4.0 technologies within the agricultural domain – has the potential to increase yield and efficiency while decreasing environmental impact. A European project will address the challenges necessary to reach these goals. The results will be demonstrated in multiple real world agricultural demonstrations.

by Christian Hirsch (TU Wien)

In crop production there are still many diseases where the time of infection and outbreak in connection with the weather is still not known. Some farmers have weather stations in their fields to monitor weather conditions in order to prevent a possible spread of a disease. However, the measurements of the weather stations represent the climate within an area of several 100 m2. To overcome this problem, TU Wien and BOKU Wien are preparing an IoT infrastructure to measure micro-climate in vineyards. Many sensors are placed all over the field and the collected data will be used to create disease prediction models using machine learning algorithms.

by Róbert Lovas (MTA SZTAKI), Krisztián Koplányi (eNET) and Gábor Élő (Széchenyi István University)

Since 2014 the Agrodat project and its collaborating partners have been working on introducing new, cost-effective sensor technologies and advanced ICT solutions for the Hungarian agriculture sector. Currently, 50 farmers and other targeted user communities have access to data analytics services with automatic warnings in case of hazardous conditions.

by Dario Albani and Vito Trianni (ISTC-CNR)

Drawing inspiration from the behaviour of honeybees, a buzzing swarm of small unmanned aerial vehicles (UAVs) will fly over hectares of cultivated fields seeking weeds.

by Andrea Manno-Kovács, András L. Majdik and Tamás Szirányi (MTA SZTAKI)

Over the last three years, the Institute for Computer Science and Control (SZTAKI) of the Hungarian Academy of Sciences (MTA) has been conducting research in the area of smart farming. The Machine Perception Research Laboratory [L1] has been integrating drone-captured aerial images  with freely available European Space Agency (ESA) Sentinel satellite imagery to develop an operational system for smart farming applications.

by Marco Napoli, Anna Dalla Marta, Marco Mancini and Simone Orlandini (University of Florence)

Technology is increasingly being used to make farming processes more efficient. We are integrating agronomic knowledge and precision farming techniques in order to achieve an adequate agronomic planning at the farm or consortium level and then to transfer this knowledge to farmers, facilitators and farm advisors.

by Florence Le Ber (Université de Strasbourg, ENGEES), Jean Lieber  (Université de Lorraine, Inria) and Marc Benoît (INRA)

A farmer’s decision to plant new perennial biomass crops is a complex process that involves numerous parameters and can change the food / non-food balance. The paradigm of case-based reasoning is able to deal with the kind of complex and sparse information that is available in this situation, to model and forecast the allocation of these crops.

Christophe Pradal (CIRAD), Sarah Cohen-Boulakia (Univ. Paris-Saclay), Gaetan Heidsieck (Inria), Esther Pacitti (Univ. Montpellier), François Tardieu (INRA) and Patrick Valduriez (Inria)
 
High-throughput phenotyping platforms allow acquisition of quantitative data on thousands of plants required for genetic analyses in well-controlled environmental conditions. However, analysing these massive datasets and reproducing computational experiments require the use of new computational infrastructure and algorithms to scale.

by Panagiotis Zervas, Leonardo Candela and Pythagoras Karampiperis (Agroknow)

Virtual research environments are proposed as a prominent cloud-based solution for agricultural and food scientists willing to collaborate and seamlessly access, use and reuse research resources such as datasets, mathematical models, software components results and publications.

by Bella Tsachidou, Philippe Delfosse and Christophe Hissler (LIST)

At the Luxembourg Institute of Science and Technology (LIST), a multidisciplinary scientific group is looking to prove how recycling of biogas residues back to agricultural soils has the potential to mitigate nitrate leaching, enhance long term storage of nutrients in the agro-ecosystems and improve agronomic performance.

by Joost Batenburg and Robert van Liere (CWI)

The Computational Imaging group at CWI develops mathematical techniques and algorithms for 3D image reconstruction. One of the main application areas is industrial quality control and rapid inspection, which is becoming increasingly important in the food processing industry. Characteristics that need to be tested and possibly also quantified include appearance of the product (size, shape, colour), texture, flavour, as well as internal factors (chemical, physical, microbial).

by Massimo Borrelli, Vanes Coric, Clemens Gnauer, Jennifer Wolfgeher and Markus Tauber (FH Burgenland)

The grape-growing industry is changing as growers increasingly combine technology with traditional growing methods in smart vineyards. If wine makers want to maximise the potential of their plants, it is no longer enough to rely on gut-feeling, but rather on locally gathered environmental data. These data help to accurately plan individual tasks, such as fertilisation, plant protection, and harvesting. This is where automated and IT-supported farming, or smart farming, comes into play.

Next issue: July 2018
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Human-Robot Interaction
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