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Remote sensing in decision support systems : using fuzzy post adjustment in localisation of weed prediction

conference contribution
posted on 06.12.2017, 00:00 authored by Andrew ChiouAndrew Chiou, Xinghuo YuXinghuo Yu
This paper explores the post adjustment of input data from a remote source to fit localised weed prediction for the control and management of weed infestation. The deployment of decision support systems in agricultural sectors often require refinement of its results to adapt to data that has been acquired externally via remote sensing. This paper will detail the fuzzy meta-consequent functions to facilitate the post adjustment. A case study is presented to demonstrate the workability of such fuzzy post-adjustment in the prediction of weed infestation.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Parent Title

Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007), 3-6 December 2007, Melbourne, Australia.

Start Page

533

End Page

538

Number of Pages

6

Start Date

01/01/2007

ISBN-10

1424415020

Location

Melbourne, Australia

Publisher

University of Melbourne, Australia

Place of Publication

Melbourne

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics; RMIT University;

Era Eligible

Yes

Name of Conference

Intelligent Sensors, Sensor Networks & Information Processing Conference