Fruits Classification and Detection Application Using Deep Learning

Abstract

Computer vision and image processing techniques are considered efficient tools for classifying various types of fruits and vegetables. In this paper, automated fruit classification and detection systems have been developed using deep learning algorithms. In this work, we used two datasets of colored fruit images. The first FIDS-30 dataset of 971 images with 30 distinct classes of fruits is publicly available. A major contribution of this work is to present a private dataset containing 761 images with eight categories of fruits, which have been collected and annotated by ourselves. In our work, the YOLOv3 deep learning object detection algorithm have been used for individual fruit detection across multiple classes, and ResNet50 and VGG16 techniques have been utilized for the final classification for the recognition of a single category of fruit in images. Next, we implemented the automatic fruit classification models with flask for the web framework. We got 86% and 85% accuracies from the public dataset with ResNet50 and VGG16, respectively. We achieved 99% accuracy with ResNet50 and 98% accuracy with the VGG16 model on the custom dataset. The domain adaptation approach is used in this work so that the proposed deep learning-based prediction model can cope with real-world problems of diverse domains. Finally, an Android smartphone application has been developed to classify and detect fruits with the camera in real-time. All the images uploaded from the Android device are automatically sent and consequently analyzed on the web, and finally, the processed data and results are returned to the smartphone. The custom dataset and implementation codes will be available after the manuscript has been accepted. The custom dataset can be found at: https://github.com/SumonAhmed334/dataset_fruit.