Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases
Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of...
Uloženo v:
| Vydáno v: | 2018 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) s. 158 - 162 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.11.2018
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of leaves or fruits are used as input data. From the data, we design discriminative features that are good for diseases classification. However, finding suitable features from the images are often challenging due to high intra-variability and inter-variability of the data. In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages. First, the use of handcrafted features is not necessary as the network itself may learn to produce discriminative features. Secondly, the procedure is conducted in an unsupervised manner and hence, no labeling of the data are required. Here, we use the output of the autoencoder as inputs to SVM-based classifiers for automatic detection of plant diseases. The method indicates to be better than conventional autoencoder with more hidden layers. |
|---|---|
| AbstractList | Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of leaves or fruits are used as input data. From the data, we design discriminative features that are good for diseases classification. However, finding suitable features from the images are often challenging due to high intra-variability and inter-variability of the data. In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages. First, the use of handcrafted features is not necessary as the network itself may learn to produce discriminative features. Secondly, the procedure is conducted in an unsupervised manner and hence, no labeling of the data are required. Here, we use the output of the autoencoder as inputs to SVM-based classifiers for automatic detection of plant diseases. The method indicates to be better than conventional autoencoder with more hidden layers. |
| Author | Zilvan, Vicky Suryawati, Endang Pardede, Hilman F. Sustika, Rika |
| Author_xml | – sequence: 1 givenname: Hilman F. surname: Pardede fullname: Pardede, Hilman F. organization: Research Center for Informatics, Indonesian Institute of Sciences, Bandung, Indonesia – sequence: 2 givenname: Endang surname: Suryawati fullname: Suryawati, Endang organization: Research Center for Informatics, Indonesian Institute of Sciences, Bandung, Indonesia – sequence: 3 givenname: Rika surname: Sustika fullname: Sustika, Rika organization: Research Center for Informatics, Indonesian Institute of Sciences, Bandung, Indonesia – sequence: 4 givenname: Vicky surname: Zilvan fullname: Zilvan, Vicky organization: Research Center for Informatics, Indonesian Institute of Sciences, Bandung, Indonesia |
| BookMark | eNotkMtOwzAURI0EErT0C7rxD6T4xo84y5JSiFQBC7quHOemMkrtynEq8fe00NUsZs5ZzITc-uCRkDmwBQArn-qK1-_LRc5AL7TKSwn6hkxAcq1kIUDck9kwfDPGcqW5kPqB7Ld-GI8YT27AllbBn0I_Jhe86elyTAG9DS3G7Nlc-jWaNEakGzTRO7-nXYh_s4NJztIVJrQXmIaOfvbGJ7o6e8_o8EjuOtMPOLvmlGzXL1_VW7b5eK2r5SZzUMiUgWK20KoF3ilhOFMNamnLAptSKC7OwVtQwFstWWu60pRWCytz0AiqMQ2fkvm_1yHi7hjdwcSf3fUL_gv0eFil |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/IC3INA.2018.8629518 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 1538657414 9781538657416 |
| EndPage | 162 |
| ExternalDocumentID | 8629518 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IL 6IN AAJGR AAWTH ABLEC ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK IEGSK OCL RIE RIL |
| ID | FETCH-LOGICAL-i175t-160c786d13f64a306be85c97eb94634eb93d1613d850daf9a9c84c5218e16bab3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 36 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000459872600029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 03:05:49 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i175t-160c786d13f64a306be85c97eb94634eb93d1613d850daf9a9c84c5218e16bab3 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_8629518 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-Nov. |
| PublicationDateYYYYMMDD | 2018-11-01 |
| PublicationDate_xml | – month: 11 year: 2018 text: 2018-Nov. |
| PublicationDecade | 2010 |
| PublicationTitle | 2018 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) |
| PublicationTitleAbbrev | IC3INA |
| PublicationYear | 2018 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0002683458 |
| Score | 1.9401649 |
| Snippet | Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 158 |
| SubjectTerms | convolutional autoen-coder Deep learning Diseases Feature extraction feature learning Kernel Plant diseases detection Support vector machines SVM Training |
| Title | Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases |
| URI | https://ieeexplore.ieee.org/document/8629518 |
| WOSCitedRecordID | wos000459872600029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV27bsIwFLUAdehEW6j6loeODSTEsZ2RQlFZEEOR2JAf1wipShBJ-P7aTkpVqUsnW37Ikh-6x_a95yD0HMvUgFYksKZABiTyTgAmCUaShUwKKmWqvNgEWyz4ep0uW-jlFAsDAN75DAYu6__yda4q91Q2tOjbAgLeRm3GaB2rdXpPGVEek4Q3xEJRmA7nk3i-GDvvLT5oev6SUPEWZNb939gXqP8TioeXJyNziVqQXaHutxYDbo5mD21XWVHt3cEvQGPb-djsKfGJx1WZO75KDYfgVbh6B_yqA-CGXXWLLXT1zTyBK55C6T20Mpwb7GSNSjytP3KKPlrN3j4m70EjohDsLDIog4iGinGqo9hQIuwFQQJPVMpApoTGxCaxtqgv1jwJtTCpSBUnyhp1DhGVQsbXqJPlGdwgzGkkmXHMoaEhxnBu0QsPISGCADAub1HPzdtmX_NkbJopu_u7-B6du6Wp4_oeUKc8VPCIztSx3BWHJ7-4X6cPp5E |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bT8IwFG4QTfQJFYx3--Cjg411XfuIIIGICw-Q8EbW7ZSQmI3swu-37SbGxBef2vSSJr3kfG3P-T6Enl3BJcQRsZQpEBZxjBOA9Ky-8G1fhFQIHhmxCT8I2GrF5w30coiFAQDjfAZdnTV_-XEalfqprKfQtwIE7Agde4T07Spa6_Ci0qfMJR6rqYUcm_emQ3caDLT_FuvWfX-JqBgbMm79b_Rz1PkJxsPzg5m5QA1ILlHrW40B14ezjTbLJC93-ujnEGPVeV_vqvATD8oi1YyVMWTWa6jrNfQrM8A1v-oGK_BqmhkKVzyCwvhoJTiVWAsbFXhUfeXkHbQcvy2GE6uWUbC2ChsUlkPtyGc0dlxJSaiuCAKYF3EfBCfUJSpxY4X73Jh5dhxKHvKIkUiZdQYOFaFwr1AzSRO4RphRR_hSc4fakkjJmMIvzAaPhATAZ-IGtfW8rXcVU8a6nrLbv4uf0Olk8TFbz6bB-x0608tURfndo2aRlfCATqJ9sc2zR7PQX4pFqtg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2018+International+Conference+on+Computer%2C+Control%2C+Informatics+and+Its+Applications+%28IC3INA%29&rft.atitle=Unsupervised+Convolutional+Autoencoder-Based+Feature+Learning+for+Automatic+Detection+of+Plant+Diseases&rft.au=Pardede%2C+Hilman+F.&rft.au=Suryawati%2C+Endang&rft.au=Sustika%2C+Rika&rft.au=Zilvan%2C+Vicky&rft.date=2018-11-01&rft.pub=IEEE&rft.spage=158&rft.epage=162&rft_id=info:doi/10.1109%2FIC3INA.2018.8629518&rft.externalDocID=8629518 |