TY - JOUR A2 - Khan, Rijwan AU - Helwan, Abdulkader AU - Sallam Ma’aitah, Mohammad Khaleel AU - Abiyev, Rahib H. AU - Uzelaltinbulat, Selin AU - Sonyel, Bengi PY - 2021 DA - 2021/04/08 TI - Deep Learning Based on Residual Networks for Automatic Sorting of Bananas SP - 5516368 VL - 2021 AB - This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented by using transfer learning that uses the stored knowledge of the deep structure. Datasets of bananas have been collected for the implementation of the deep structure. The simulation results of the designed system have shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. The simulation results of the designed residual learning-based system are compared with the results of other systems used for grading the bananas. Comparative results indicate the efficiency of the designed system. The developed system can be used in food processing industry, in real-life applications where the accuracy, cost, and speed of the intelligent system will enhance the production rate and allow meeting the demand of consumers. The system can replace or assist human operators who can exert their energy on the selection of fruits. SN - 0146-9428 UR - https://doi.org/10.1155/2021/5516368 DO - 10.1155/2021/5516368 JF - Journal of Food Quality PB - Hindawi KW - ER -