Fruit Image Classification using the Inception-V3 Deep Learning Model

Abstract

Fruit image recognition plays a vital role in the realm of deep learning, with applications extending to smart agriculture and harvesting robots. However, traditional image classification algorithms often suffer from limited generalization ability and low accuracy. In this research, we address these challenges by proposing a novel fruit image classification algorithm that leverages deep learning and transfer learning techniques. Specifically, we employ a modified version of the Inception-V3 model for feature extraction from fruit images and subsequently utilize a deep learning classifier to classify these extracted features. Additionally, we incorporate transfer learning to enhance the training process. Through comprehensive testing, our proposed algorithm demonstrates significantly higher recognition accuracy compared to conventional fruit classification approaches, marking a significant advancement in the field of fruit image classification using deep learning techniques.