Applying the ABC Approach for Edge Detection in Unshelled Banana Prawn Images

This research explores the application of the artificial bee colony (ABC) algorithm in image edge detection to enhance the process of unshelled banana prawn weight prediction. Image edge detection plays a crucial role in extracting meaningful features from images, which are then utilized for predictive modeling. By integrating the ABC algorithm into the image edge detection stage, the efficiency and effectiveness of feature extraction are enhanced, leading to improved capability in identifying intricate edge patterns and efficiently generating continuous and extracted edges. The experimental findings underscore the effectiveness of our proposed ABC method, showcasing its superiority over existing techniques like Particle Swarm Optimization (PSO) and Bat Algorithm (BA). Therefore, our proposed method is considered suitable for application in the task of edge detection in unshelled banana prawn images. Moreover, the proposed approach also offers a methodology for addressing the challenges associated with weight prediction of unshelled banana prawns, contributing to advancements in aquaculture research and automation technologies.