2017 Technology Exchange

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Global Service Platforms for Precision Agriculture

Time 10/17/17 11:20AM-12:10PM

Room Seacliff B

Session Abstract

Precision agriculture for crop production is creating large quantities of digital data. Crop farmers use computers with GPS input to record both crop input information and harvest data. In addition to in-field data, crop producers are using various types of remote sensing to monitor crop growth and identify crop growth stages, nutrient deficiencies, pest infestations and damage, and various crop anomalies. Presenters in this session will describe how they are managing precision agricultural data associated with research projects in North Dakota and in Europe.
Collaborators include the NDSU Agricultural and Biosystems Engineering Department, the NDSU Information Technology Division, and the Farm-oriented Open Data in Europe (FOODIE)
Presenters share experiences of collaborative projects among private sector partners to collect, manage, and analyze producers’ crop digital data over large areas, and deliver actionable information to producers to use for timely in-season and future years crop production decisions. Collaborators in North Dakota use large and small unmanned aircraft systems (UAS) to collect high-resolution imagery for use with producers’ crop production data in precision field crop management decisions. NDSU precision agricultural projects are collaborating with Elbit Systems America to collect 8 cm ground sample distance remote sensing imagery of 40,000 acres per hour using the Hermes 450. The Hermes 450 flying at 8,000’ altitude generates approximately 1 TB of imagery per hour. Project personnel collaborate with private sector partners to collect, manage, analyze and transfer agricultural digital crop data. This UAS precision agriculture pilot project is dependent on access to local and regional research and education networks to successfully implement project activities. R&E networks supporting this project include North Dakota's state government and education network and the Northern Tier regional network which provides support for data transfer and analysis needs critical to collaboration among project partners.
Collaborators in Europe include the Farm-oriented Open Data in Europe. The agriculture sector is of strategic importance for European society and economy. Due to its complexity, agri-food operators have to manage many different and heterogeneous sources of information. Agriculture requires collection, storage, sharing and analysis of large quantities of spatially and non-spatially referenced data. These data flows currently present a hurdle to uptake of precision agriculture as the multitude of data models, formats, interfaces and reference systems in use result in incompatibilities. In order to plan and make economically and environmentally sound decisions a combination and management of information is needed. The key point of FOODIE project was to create a platform hub on the cloud where spatial and non-spatial data related to agricultural sector are available for agri-food stakeholders groups and interoperable. It aimed at offering an infrastructure for building an interacting and collaborative network; the integration of existing open datasets related to agriculture; data publication and data linking of external agriculture data sources, providing specific and high-value applications and services for the support of planning and decision-making processes. We will present FOODIE data model, providing a standardized vocabulary for the representation of economically and ecologically related agricultural information. It comprises unified definitions for collecting data about yield, reference materials for subsidies, evidence of environmental burden (e.g. phosphates, nitrates, pesticides, etc.). In order to ensure the maximum degree of data interoperability, FOODIE data model follows INSPIRE generic data models, in particular the INSPIRE data model for Agricultural and Aquaculture Facilities, by extending and specializing them. FOODIE model is associated with ontology, enabling the representation of data compliant with FOODIE data model in semantic format and their interlinking with established vocabularies and ontologies. It can be used for data semantization tasks, in order to enable access to the (semi-)structured data (e.g., tabular, relational) collected and produced in FOODIE through semantic tools and applications, as well as the publication of such data following linked data principles.
The session will open new opportunities for collaboration in terms of building and sharing globally innovative technologies, solutions and data for agriculture established around research networks and data infrastructures providers worldwide. We would like to interest collaborators from less developed regions to advance their economic and social development but most important to transfer knowledge and technology in the context of global significance of food and agriculture (FAO, 2012: Towards the Future We Want, http://www.fao.org/docrep/015/an894e/an894e00.pdf)

Speakers

Speaker Cezary Mazurek PIONIER (Poznan Supercomputing and Networking Center)

Speaker John Nowatzki North Dakota State University - Main Campus

Speaker Kim Owen North Dakota State University - Main Campus

Presentation Media

media item thumbnail UAV Imagery and Data Management for Precision Agriculture

Speaker John Nowatzki North Dakota State University - Main Campus

Speaker Kim Owen North Dakota State University - Main Campus

media item thumbnail Towards Smart Agriculture, Food and Life

Speaker Cezary Mazurek PIONIER (Poznan Supercomputing and Networking Center)

Primary track Applications for Research

Secondary tracks Web-Scale Computing

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