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Farm biosecurity hot spots prediction using big data analytics


Li, C and Dutta, R and Smith, D and Das, A and Aryal, J, Farm biosecurity hot spots prediction using big data analytics, Proceedings of the 2015 IEEE 31st International Conference on Data Engineering Workshops, 13-17 April 2015, Seoul, South Korea, pp. 101-104. ISBN 978-1-4799-8441-1 (2015) [Refereed Conference Paper]

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Copyright 2015 IEEE

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DOI: doi:10.1109/ICDEW.2015.7129555


In this paper a novel application of salad leaf disease detection has been developed using a combination of big data analytics and on field multi-dimensional sensing. Heterogeneous knowledge integration from publicly available various big data sources, calibrated with in-situ ground truth information, has the merit to be a very efficient way to tackle large area wise farm biosecurity related issues and early disease or pest infestation prevention. We propose a cloud computing based intelligent big data analysis platform to predict farm hot spots with high probability of potential biosecurity threats and early monitoring system aiming to save the farm from significant economic damage.

Item Details

Item Type:Refereed Conference Paper
Keywords:biosecurity, big data analytics, knowledge integration, hot spot prediction
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the agricultural, food and veterinary sciences
UTAS Author:Das, A (Dr Aruneema Das)
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:101657
Year Published:2015
Deposited By:Geography and Environmental Studies
Deposited On:2015-06-29
Last Modified:2017-10-30

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