Detection of Leaf Diseases Using Color and Shape Models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, GwanIk | - |
dc.contributor.author | Sim, Kyu Dong | - |
dc.contributor.author | Kyeon, Min Su | - |
dc.contributor.author | Lee, Sang Hwa | - |
dc.contributor.author | Baek, Jeong Hyun | - |
dc.contributor.author | Park, Jong-Il | - |
dc.date.accessioned | 2023-08-07T07:57:51Z | - |
dc.date.available | 2023-08-07T07:57:51Z | - |
dc.date.created | 2023-05-30 | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188981 | - |
dc.description.abstract | This paper handles with detection of leaf diseases using deep learning networks which have learned the color and shape parameters of leaf diseases. This paper considers the color distribution and shape information of leaf diseases, and exploits two deep leaning networks in training the normal leaves and diseases. The input color image is partitioned into small segments using color clustering, and the color information of each segment is inspected by the Color Network. When a segment is determined as abnormal (that is, disease segment), the shape parameters of the segment are inspected by Shape Network to classify the disease types. This paper uses HSV color space for Color Network and proposes 24 parameters for Shape Network such as boundary length ratio, densities of subregions, correlation coefficients of x-y coordinates in the disease segments. According to the experiments with three types of diseases (type A, B, C) for images of iceberg, strawberry, coffee, sunflower, chinar, blackgram, citrus, and apple leaves images, leaf diseases are detected with 97.9% recall for a segment unit and 99.3% recall for an input image where there are more than two disease segments. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPIE-INT SOC OPTICAL ENGINEERING | - |
dc.title | Detection of Leaf Diseases Using Color and Shape Models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Jong-Il | - |
dc.identifier.doi | 10.1117/12.2666893 | - |
dc.identifier.scopusid | 2-s2.0-85159286286 | - |
dc.identifier.wosid | 001004075700047 | - |
dc.identifier.bibliographicCitation | Proceedings of SPIE - The International Society for Optical Engineering, v.12592, pp.1 - 6 | - |
dc.relation.isPartOf | Proceedings of SPIE - The International Society for Optical Engineering | - |
dc.citation.title | Proceedings of SPIE - The International Society for Optical Engineering | - |
dc.citation.volume | 12592 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Information use | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Sea ice | - |
dc.subject.keywordPlus | Color | - |
dc.subject.keywordPlus | Color clustering | - |
dc.subject.keywordPlus | Color models | - |
dc.subject.keywordPlus | Deep learning network | - |
dc.subject.keywordPlus | Leaf disease | - |
dc.subject.keywordPlus | Learning network | - |
dc.subject.keywordPlus | Mean shift | - |
dc.subject.keywordPlus | Meanshift color clustering | - |
dc.subject.keywordPlus | Phenomic system | - |
dc.subject.keywordPlus | Shape information | - |
dc.subject.keywordPlus | Shape parameters | - |
dc.subject.keywordAuthor | Leaf diseases | - |
dc.subject.keywordAuthor | Phenomics system | - |
dc.subject.keywordAuthor | Deep learning network | - |
dc.subject.keywordAuthor | Meanshift color clustering | - |
dc.subject.keywordAuthor | Shape information | - |
dc.identifier.url | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12592/2666893/Detection-of-leaf-diseases-using-color-and-shape-models/10.1117/12.2666893.full?SSO=1 | - |
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