Natural Inspired Intelligent Visual Computing and Its Application to Viticulture
1
School of Computing & Mathematics, Charles Sturt University, Wagga Wagga 2678, Australia
2
National Grape and Wine Industries Centre, Wagga Wagga 2678, Australia
3
CM3 Research Centre, Charles Sturt University, Bathurst 2795, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Stefan Bosse, Ansgar Trächtler, Klaus-Dieter Thoben, Berend Denkena and Dirk Lehmhus
Sensors 2017, 17(6), 1186; https://doi.org/10.3390/s17061186
Received: 16 February 2017
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Revised: 28 April 2017
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Accepted: 2 May 2017
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Published: 23 May 2017
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)
This paper presents an investigation of natural inspired intelligent computing and its corresponding application towards visual information processing systems for viticulture. The paper has three contributions: (1) a review of visual information processing applications for viticulture; (2) the development of natural inspired computing algorithms based on artificial immune system (AIS) techniques for grape berry detection; and (3) the application of the developed algorithms towards real-world grape berry images captured in natural conditions from vineyards in Australia. The AIS algorithms in (2) were developed based on a nature-inspired clonal selection algorithm (CSA) which is able to detect the arcs in the berry images with precision, based on a fitness model. The arcs detected are then extended to perform the multiple arcs and ring detectors information processing for the berry detection application. The performance of the developed algorithms were compared with traditional image processing algorithms like the circular Hough transform (CHT) and other well-known circle detection methods. The proposed AIS approach gave a Fscore of 0.71 compared with Fscores of 0.28 and 0.30 for the CHT and a parameter-free circle detection technique (RPCD) respectively.
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Keywords:
natural inspired computing; intelligent system; artificial immune system; visual information processing; viticulture applications
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MDPI and ACS Style
Ang, L.M.; Seng, K.P.; Ge, F.L. Natural Inspired Intelligent Visual Computing and Its Application to Viticulture. Sensors 2017, 17, 1186. https://doi.org/10.3390/s17061186
AMA Style
Ang LM, Seng KP, Ge FL. Natural Inspired Intelligent Visual Computing and Its Application to Viticulture. Sensors. 2017; 17(6):1186. https://doi.org/10.3390/s17061186
Chicago/Turabian StyleAng, Li M., Kah P. Seng, and Feng L. Ge. 2017. "Natural Inspired Intelligent Visual Computing and Its Application to Viticulture" Sensors 17, no. 6: 1186. https://doi.org/10.3390/s17061186
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