Leaf Disease Segmentation From Agricultural Images via Hybridization of Active Contour Model and OFA

Abstract

Abstract In this paper, an alternative active contour model (ACM) driven by an oppositional fruit fly algorithm (OFA) is presented. Unlike the traditional ACM variant, which is frequently caught in a local minimum, this methodology helps the focalizing of control points toward the global least of the energy function. In the proposed system, energy minimization is performed through a fruit fly algorithm, and every control point is compelled in a local search window. As for the local search window, the rectangular-shaped approach has been viewed. The results demonstrated that the fruit fly strategy utilizing polar coordinates is, for the most part, desirable over the fruit fly performed in rectangular shapes. Three performance metrics, such as the Jaccard index, the Dice index, and the Hausdorff distance, were utilized to validate the proposed strategy in real agricultural and synthetic images. From the results, it is clear that the proposed OFA technique shows a great option for the agricultural plant image segmentation process, considering any kind of disease that occurred in plant leaves.