Toggle Main Menu Toggle Search

Open Access padlockePrints

Computational Intelligent Color Normalization for Wheat Plant Images to Support Precision Farming

Lookup NU author(s): Susanto Sulistyo, Dr Wai Lok Woo, Professor Satnam Dlay

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

Image colors are considerably affected by the intensity of the light source. In this paper, we propose a color constancy method using neural networks fusion to normalize images captured under sunlight with a variation of light intensities. A genetic algorithm is also applied to optimize the color normalization. A 24-patch Macbeth color checker is utilized as the reference to normalize the images. The results of our proposed method is superior to other methods, i.e. the conventional gray world and scale-by-max methods, as well as linear model and single neural network method. Furthermore, the proposed method can be used to normalize wheat plant images captured under various light intensities.


Publication metadata

Author(s): Sulistyo SB, Woo WL, Dlay SS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)

Year of Conference: 2016

Pages: 130-135

Online publication date: 11/04/2016

Acceptance date: 01/01/1900

Publisher: IEEE

URL: http://dx.doi.org/10.1109/ICACI.2016.7449816

DOI: 10.1109/ICACI.2016.7449816


Share