2019/04/01 by Shadman Sakib, Sakib, Shadman, Zahidun Ashrafi +3
Agricultural and Biological Sciences · Chemistry · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Agriculture and AI #Spectroscopy and Chemometric Analyses #Vehicle License Plate Recognition
paper · pdf · doi:10.48550/arxiv.1904.00783
openalex publication_date 2019/04/01 · openalex created_date 2019/04/11 · openalex updated_date 2026/07/28
Fruit recognition using Deep Convolutional Neural Network (CNN) is one of the most promising applications in computer vision. In recent times, deep learning based classifications are making it possible to recognize fruits from images. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of the complexity and similarity. In this paper, a fruit recognition system using CNN is proposed. The proposed method uses deep learning techniques for the classification. We have used Fruits-360 dataset for the evaluation purpose. From the dataset, we have established a dataset which contains 17,823 images from 25 different categories. The images are divided into training and test dataset. Moreover, for the classification accuracies, we have used various combinations of hidden layer and epochs for different cases and made a comparison between them. The overall performance losses of the network for different cases also observed. Finally, we have achieved the best test accuracy of 100% and a training accuracy of 99.79%.