Fruit Freshness Evaluation using a Real-Time Industrial Framework for Deep Learning Ensemble Approaches

Abstract

Abstract: Consumers give a high value on fruits' freshness, and manual visual grading presents challenges due to labor effort and inconsistent results. This research suggests an effective machine vision system for automating a visual assessment of fruit freshness and attractiveness based on cutting-edge deep learning algorithms and ensemble methodologies. The suggested architecture enables the non-destructive and economical detection of fruit defects by utilizing convolutional neural networks (CNNs). To attain high classification accuracy, which acts as the performance metric, the system utilizes ensemble deep learning models. Fruit photographs are used to train the algorithm, enabling precise fruit quality assessment. This framework revolutionizes the inspection process by using computer vision in real-time for industrial applications in fruit freshness detection