About this document
Deep Learning for Fruit Freshness Detection by percobaantumbal557 is a document available to read on EtoBox.
This research focuses on optimizing deep learning models for fruit freshness recognition to improve sorting efficiency during harvest seasons. By employing strategies such as fine-tuning, transfer learning, and data augmentation on models like MobileNetV2, ResNet50, and InceptionResNetV2, the study achieved a maximum classification accuracy of 100% with specific parameter settings. The dataset used comprises 13,737 images of fresh and rotten fruits, and the optimization strategies were tailored based on the
- Author
- percobaantumbal557
- Language
- EN