{"title":"Evaluation of The Deep Learning Techniques to Identify Plant Diseases Using Leaf Images","authors":"Harry Yuliansyah, Rudy Hartanto, I. Soesanti","doi":"10.15676/ijeei.2021.13.4.5","DOIUrl":null,"url":null,"abstract":": Successful farming is influenced by various techniques conducted by farmers in identifying the types of diseases that affect plant yields to avoid greater losses. Therefore, this study aims to evaluate the deep learning techniques in identifying plant diseases using leaf images. Furthermore, an artificial intelligence approach was used to identify types of plant diseases. During the deep learning training, about 11 deep learning architectural models and consisting of 38 classes in the dataset were used. The results showed that the highest minimum accuracy value obtained was 87.10%, with only one class having an accuracy value below 90%.","PeriodicalId":38705,"journal":{"name":"International Journal on Electrical Engineering and Informatics","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Electrical Engineering and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15676/ijeei.2021.13.4.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 1
Abstract
: Successful farming is influenced by various techniques conducted by farmers in identifying the types of diseases that affect plant yields to avoid greater losses. Therefore, this study aims to evaluate the deep learning techniques in identifying plant diseases using leaf images. Furthermore, an artificial intelligence approach was used to identify types of plant diseases. During the deep learning training, about 11 deep learning architectural models and consisting of 38 classes in the dataset were used. The results showed that the highest minimum accuracy value obtained was 87.10%, with only one class having an accuracy value below 90%.
期刊介绍:
International Journal on Electrical Engineering and Informatics is a peer reviewed journal in the field of electrical engineering and informatics. The journal is published quarterly by The School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia. All papers will be blind reviewed. Accepted papers will be available on line (free access) and printed version. No publication fee. The journal publishes original papers in the field of electrical engineering and informatics which covers, but not limited to, the following scope : Power Engineering Electric Power Generation, Transmission and Distribution, Power Electronics, Power Quality, Power Economic, FACTS, Renewable Energy, Electric Traction, Electromagnetic Compatibility, Electrical Engineering Materials, High Voltage Insulation Technologies, High Voltage Apparatuses, Lightning Detection and Protection, Power System Analysis, SCADA, Electrical Measurements Telecommunication Engineering Antenna and Wave Propagation, Modulation and Signal Processing for Telecommunication, Wireless and Mobile Communications, Information Theory and Coding, Communication Electronics and Microwave, Radar Imaging, Distributed Platform, Communication Network and Systems, Telematics Services, Security Network, and Radio Communication. Computer Engineering Computer Architecture, Parallel and Distributed Computer, Pervasive Computing, Computer Network, Embedded System, Human—Computer Interaction, Virtual/Augmented Reality, Computer Security, VLSI Design-Network Traffic Modeling, Performance Modeling, Dependable Computing, High Performance Computing, Computer Security.