Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model  

作　　者：T.Satyanarayana Murthy Kollati Vijaya Kumar Fayadh Alenezi E.Laxmi Lydia Gi-Cheon Park Hyoung-Kyu Song Gyanendra Prasad Joshi Hyeonjoon Moon 

机构地区：[1]Department of Information Technology,Chaitanya Bharathi Institute of Technology,Hyderabad,Telangana,India [2]Department of Computer Science,College of Computer and Information Sciences,Majmaah University,Al-Majmaah,11952,Saudi Arabia [3]Department of Electrical Engineering,College of Engineering,Jouf University,Sakaka,Saudi Arabia [4]Department of Computer Science and Engineering,Vignan’s Institute of Information Technology,Visakhapatnam,530049,India [5]Department of International Affairs and Education,Gangseo University,Seoul,07661,Korea [6]Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone,Sejong University,Seoul,05006,Korea [7]Department of Computer Science and Engineering,Sejong University,Seoul,05006,Korea

出　　处：《Computer Systems Science & Engineering》2023年第11期1633-1650,共18页计算机系统科学与工程（英文）

基　　金：supported by Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (2020R1A6A1A03038540);by Korea Institute of Planning and Evaluation for Technology in Food,Agriculture,Forestry and Fisheries (IPET)through Digital Breeding Transformation Technology Development Program,funded by Ministry of Agriculture,Food and Rural Affairs (MAFRA) (322063-03-1-SB010);by the Technology development Program (RS-2022-00156456)funded by the Ministry of SMEs and Startups (MSS,Korea).

摘　　要：Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.

关 键 词：Fruit classification computer vision machine learning deep learning metaheuristics 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]