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.
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007), 3-6 December 2007, Melbourne, Australia.