Review of Fruit Spoilage Using Convolutional Neural Networks and Machine Learning Techniques

Abstract

Fruit spoilage poses significant challenges in food preservation and supply chain management. This study explores the application of Convolutional Neural Networks (CNNs) and machine learning techniques to detect and classify fruit spoilage based on visual cues. The research leverages image datasets comprising various stages of fruit decay, captured under controlled conditions. The methodology involves preprocessing images to enhance feature extraction, followed by CNN models trained on labeled datasets to classify images into categories such as fresh, slightly spoiled, and heavily spoiled