Remote sensing in decision support systems : using fuzzy post adjustment in localisation of weed prediction
conference contribution
posted on 2017-12-06, 00:00 authored by Andrew ChiouAndrew Chiou, Xinghuo YuXinghuo YuThis 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
533End Page
538Number of Pages
6Start Date
2007-01-01ISBN-10
1424415020Location
Melbourne, AustraliaPublisher
University of Melbourne, AustraliaPlace of Publication
MelbourneFull Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Faculty of Business and Informatics; RMIT University;Era Eligible
- Yes