Transfer Learning-based Fruit Image Recognition

This research explores fruit image recognition using deep learning techniques, aiming to develop a robust and efficient model for accurately classifying diverse fruits. By leveraging transfer learning, the study applies pre-trained models with a particular focus on the EfficientNetV2-S architecture. The Fruits 360 dataset serves as the benchmark for evaluating the model's performance. The research follows a systematic evaluation protocol, with accuracy as the key metric. Techniques such as fine-tuning are employed to enhance performance, and the study also considers the impact of model efficiency. The proposed method, based on the EfficientNetV2-S model, strikes a balance between accuracy and computational efficiency, providing valuable insights into the practical applications of deep learning for fruit image recognition.