Deep Learning for Accurate Fruit Freshness Detection

This presentation explores the detection of freshness through an automated approach, contrasting manual assessments and leveraging custom CNN frameworks. It includes a comprehensive review of related works, details on dataset preparation, preprocessing, and augmentation. The experimental setup highlights training configurations, hyperparameters, and evaluation metrics. Results showcase performance analysis, comparative studies, and identified strengths and limitations for further improvement.