Enhancing Segmentation Approach: FCM-CNN Based Robust Segmentation Method for Regular and Irregular Shape Fruit Image

Abstract

In today’s era of computer vision, the researcher solved the different challenges. Similar to these aspects, this article defined the FCM (Fuzzy C-Means) based enhanced method for segmentation of irregular shape fruit image captured in natural light is a computer vision technique used to identify artificially ripened fruits. The process involves segmenting the fruit image into its constituent parts using fuzzy logic to capture the ambiguity and variability of the fruit's features. The FCM algorithm utilizes a clustering approach to identify regions in the image that have similar characteristics or properties. This approach is particularly useful when the fruit's shape and color vary widely, making it challenging to segment using traditional image processing methods. The enhanced FCM method takes into account the fact that artificially ripened fruits exhibit distinct color variations compared to naturally ripened fruits. By considering the spectral properties of the fruit, the enhanced FCM method can accurately identify artificially ripened fruits. The segmentation process involves converting the fruit image into a set of pixel values, followed by clustering the pixels into different groups based on their similarity. The final step involves identifying the clusters that correspond to the fruit's color and shape and extracting these regions to obtain a segmented image. The state-of-art that the FCM based enhanced method for segmentation of irregular shape fruit image captured in natural light is a powerful tool for identifying artificially ripened fruits, which are a major issues in the food industry relate to health risks.