Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image

Jibrin Bala (Ahmadu Bello University)
Habeeb Bello Salau (Ahmadu Bello University)
Ime Jarlath Umoh (Ahmadu Bello University)
Adeiza James Onumanyi (Federal University of Technology Minna)
Salawudeen Ahmed Tijani (University of Jos)
Basira Yahaya (Ahmadu Bello University)

Journal of ICT Research and Applications
Vol. 14 No. 3 (2021)

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.