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All Journal Journal of ICT Research and Applications
Adeiza James Onumanyi
Federal University of Technology Minna
Computer Science & IT

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Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image Jibrin Bala; Habeeb Bello Salau; Ime Jarlath Umoh; Adeiza James Onumanyi; Salawudeen Ahmed Tijani; Basira Yahaya
Journal of ICT Research and Applications Vol. 14 No. 3 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.14.3.4

Abstract

The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.
Co-Authors Basira Yahaya Habeeb Bello Salau Ime Jarlath Umoh Jibrin Bala Salawudeen Ahmed Tijani