Abstract
There is an increasing tension in agriculture between the requirements to assure full safety on the one hand and keep costs under control on the other hand, both with respect to (inter)national strategies. Farmers need to measure and understand the impact of huge amount and variety of data which drive overall quality and yield in their fields. Among others, those are local weather data, Global Navigation System of Systems data, orthophotos and satellite imagery, data on soil specifics etc. A strong need to secure Big Data arises due to various repositories and heterogeneous sources. Data storage and visualization requirements are in some cases competing as they are a common interest as well as a threat that helps one part of a value chain to gain a higher profit. As demonstrated in this paper, handling (Big) data is therefore a sensitive topic, where trust of producers on data security is essential.
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Author Keywords
- precision agriculture,
- big data,
- yield productivity zones,
- visualization
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IEEE Keywords
- Agriculture,
- Data visualization,
- Data models,
- Productivity,
- Big Data,
- Three-dimensional displays,
- Unified modeling language
Introduction
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. (Precision) Agriculture requires the collection, storage, sharing, and analysis of large quantities of spatially and non-spatially referenced data. These data flows currently present a hurdle to the 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 are needed [1]. DataBio (Data-Driven Bioeconomy) project [2] aims at demonstrating the benefits of Big Data technologies in raw material production in agriculture, forestry, and fishery/aquaculture for the bioeconomy industry to produce food, energy and biomaterials responsibly and sustainably. DataBio project re-uses and further develops the FOODIE project results [3]. DataBio deploys a state of the art big data platform “on top of the existing partners” infrastructure and solutions – the Big DataBio Platform. The Big DataBio Platform also comprises experts from bioeconomy and technology research institutes, end-users, technology providers, and other partners. Furthermore, associated partners and other stakeholders are also actively involved in the pilots to verify the Big DataBio platform capabilities. Big data technology (BDT) is a new technological paradigm that is driving the entire economy, including low-tech industries such as agriculture where it is implemented under the banner of precision farming (PF). Following the BDT and PF schemes, a Big data analytics system then provides farm managers with highly localized descriptive, prescriptive, and predictive plans. Descriptive plans offer a better and more advanced way of looking at an operation, while prescriptive plans provide timely recommendations for operation improvement, i.e. seed, fertilizer, and other agricultural inputs application rates, soil analysis, and localized weather and disease/pest reports, based on realtime and historical data. Finally, predictive plans use current and historical data sets to forecast future localized events and returns. The following sections focus on three new domains of Big Data Visualisation and Analysis for Agriculture: • yield predictions (see section 2); • analysis and visualization of Agriculture Linked Open Data (RDF, see section 3); • 3D visualization and analysis of agricultural data (see section 4).
Conclusion
The presented data models, their semantic equivalents, the concept of yield productivity zones and visualization techniques represent the cornerstones for the Big data technology applications in the domain of Precision Farming as understood by the consortium of the DataBio project. The ongoing work focuses on several associated aspects, starting from validations of yield productivity zones prediction, through optimizing the performance of developed 3D visualization application and support of its further visualization capabilities to the development of semantic end-user applications.
Acknowledgment
This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 732064 entitled “Data-Driven Bioeconomy” (DataBio), the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement No. 621074 titled “Farm-Oriented Open Data in Europe” (FOODIE).
About KSRA
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FULL Paper PDF file:
Advanced Visualisation of Big Data for Agriculture as Part of Databio DevelopmentBibliography
author
Year
2018
Title
Advanced Visualisation of Big Data for Agriculture as Part of Databio Development,
Publish in
IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 415-418,
Doi
10.1109/IGARSS.2018.8517556.
PDF reference and original file: Click here
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
