Research Article Deep Learning Based on Residual Networks for Automatic Sorting of Bananas Abdulkader Helwan , 1 Mohammad Khaleel Sallam Ma’aitah , 2 RahibH.Abiyev , 3 Selin Uzelaltinbulat , 4 andBengiSonyel 5 1 School of Engineering, Lebanese American University, Byblos, Lebanon 2 Department of Management Information Systems, Near East University, Nicosia, TRNC, Mersin 10, Turkey 3 Department of Computer Engineering, Near East University, Nicosia, TRNC, Mersin 10, Turkey 4 Department of Computer Information Systems, Near East University, Nicosia, TRNC, Mersin 10, Turkey 5 Department of Educational Sciences, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey CorrespondenceshouldbeaddressedtoRahibH.Abiyev;rahib.abiyev@neu.edu.tr Received 13 February 2021; Accepted 25 March 2021; Published 8 April 2021 AcademicEditor:RijwanKhan Copyright©2021AbdulkaderHelwanetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residuallearning-basednetwork“ResNet-50”isdesignedtosortoutfruits,particularlybananasintohealthyordefectiveclasses. edesignofthesystemisimplementedbyusingtransferlearningthatusesthestoredknowledgeofthedeepstructure.Datasets ofbananashavebeencollectedfortheimplementationofthedeepstructure.esimulationresultsofthedesignedsystemhave shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. e simulationresultsofthedesignedresiduallearning-basedsystemarecomparedwiththeresultsofothersystemsusedforgrading the bananas. Comparative results indicate the efficiency of the designed system. e 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 productionrateandallowmeetingthedemandofconsumers.esystemcanreplaceorassisthumanoperatorswhocanexert their energy on the selection of fruits. 1.Introduction In the food industry, the quality of processed fruits is ex- tremelyimportant.Meetingthedemandsoftheconsumers andproducinghigh-qualityfruitsattheproductionlineata very fast rate requires the implementation of high-perfor- mancetechnologies[1].Moreover,thefoodindustryisone of the few fields which have restricting conditions and constraintsduetoitsdependencyonweatherconditionsand the labor market [2]. For example, if the fruits were not harvested at the most suitable time due to weather condi- tions, the quality and quantity of the harvest may decrease duetobadweatherconditionsandexcessiveripeningofthe fruits. Over the years, the most technological processes in this industry were mainly controlled by human operators. Some delicate tasks such as postharvest and grading of healthyanddefectiveproductswerebasedonhuman-made decisions. Human operators are sometimes exposed to the tirednessoftheeyesduetolackofsleepandfatiguecaused by overworking that can affect their performances. Fruit sorting is a decision-making task which is based on some visual features of the fruit and decides whether a fruit is healthyordefectivewhenitpassesthroughaconveyorbelt. erefore, it is a computer vision problem which can be perfectlysolvedusingmachinelearningthatcanpreventthe errors caused by human operators [3]. Recently,differentresearchworkshavebeenperformed forcontrollingandgradingoffruitsusingcomputervision andmachinelearningtechniques.ecommonapplications areclassificationandsortingoffruits[4,5],identificationof Hindawi Journal of Food Quality Volume 2021, Article ID 5516368, 11 pages https://doi.org/10.1155/2021/5516368