Computational intelligent color normalization for wheat plant images to support precision farming

Susanto B. Sulistyo; W. L. Woo; S. S. Dlay

doi:10.1109/ICACI.2016.7449816

Computational intelligent color normalization for wheat plant images to support precision farming

Susanto B. Sulistyo, W. L. Woo, S. S. Dlay

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016
Publisher	IEEE
Pages	130-135
Number of pages	6
ISBN (Electronic)	9781467377829
ISBN (Print)	9781467377805
DOIs	https://doi.org/10.1109/ICACI.2016.7449816
Publication status	Published - 11 Apr 2016
Event	8th International Conference on Advanced Computational Intelligence, ICACI 2016 - Chiang Mai, Thailand Duration: 14 Feb 2016 → 16 Feb 2016

Conference

Conference	8th International Conference on Advanced Computational Intelligence, ICACI 2016
Country/Territory	Thailand
City	Chiang Mai
Period	14/02/16 → 16/02/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

color constancy
genetic algorithm
gray world
neural networks
scale-by-max

Access to Document

10.1109/ICACI.2016.7449816

Cite this

@inproceedings{a7769b53074a4246aa34eb49b5801ac7,

title = "Computational intelligent color normalization for wheat plant images to support precision farming",

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.",

keywords = "color constancy, genetic algorithm, gray world, neural networks, scale-by-max",

author = "Sulistyo, \{Susanto B.\} and Woo, \{W. L.\} and Dlay, \{S. S.\}",

year = "2016",

month = apr,

day = "11",

doi = "10.1109/ICACI.2016.7449816",

language = "English",

isbn = "9781467377805",

pages = "130--135",

booktitle = "Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016",

publisher = "IEEE",

address = "United States",

note = "8th International Conference on Advanced Computational Intelligence, ICACI 2016 ; Conference date: 14-02-2016 Through 16-02-2016",

}

Sulistyo, SB, Woo, WL & Dlay, SS 2016, Computational intelligent color normalization for wheat plant images to support precision farming. in Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016., 7449816, IEEE, pp. 130-135, 8th International Conference on Advanced Computational Intelligence, ICACI 2016, Chiang Mai, Thailand, 14/02/16. https://doi.org/10.1109/ICACI.2016.7449816

Computational intelligent color normalization for wheat plant images to support precision farming. / Sulistyo, Susanto B.; Woo, W. L.; Dlay, S. S.
Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016. IEEE, 2016. p. 130-135 7449816.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computational intelligent color normalization for wheat plant images to support precision farming

AU - Sulistyo, Susanto B.

AU - Woo, W. L.

AU - Dlay, S. S.

PY - 2016/4/11

Y1 - 2016/4/11

N2 - 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.

AB - 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.

KW - color constancy

KW - genetic algorithm

KW - gray world

KW - neural networks

KW - scale-by-max

UR - https://www.scopus.com/pages/publications/84966620085

U2 - 10.1109/ICACI.2016.7449816

DO - 10.1109/ICACI.2016.7449816

M3 - Conference contribution

AN - SCOPUS:84966620085

SN - 9781467377805

SP - 130

EP - 135

BT - Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016

PB - IEEE

T2 - 8th International Conference on Advanced Computational Intelligence, ICACI 2016

Y2 - 14 February 2016 through 16 February 2016

ER -

Computational intelligent color normalization for wheat plant images to support precision farming

Abstract

Conference

UN SDGs

Keywords

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