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Published VersionCC BY V4.0, Open Access
Journal article
Natural inspired intelligent visual computing and its application to viticulture
Sensors, Vol.17(6), pp.1-16
2017
Abstract
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.
Details
- Title
- Natural inspired intelligent visual computing and its application to viticulture
- Authors
- Li-Minn Ang (Author) - Charles Sturt UniversityK P Seng (Author) - Charles Sturt UniversityF L Ge (Author) - Charles Sturt University
- Publication details
- Sensors, Vol.17(6), pp.1-16
- Publisher
- MDPI AG
- Date published
- 2017
- DOI
- 10.3390/s17061186
- ISSN
- 1424-8220; 1424-8220
- Copyright note
- © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
- Organisation Unit
- School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99513894702621
- Output Type
- Journal article
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- Web Of Science research areas
- Chemistry, Analytical
- Engineering, Electrical & Electronic
- Instruments & Instrumentation