Swarm Intelligence for Segmentation of Leaf Images
Leaf segmentation is important in assisting environmentalists to automatically segment the foreground leaf from the noisy background. The accuracy with which the image is segmented and the unwanted background areas are removed determines the result acquired from machine learning algorithms employed...
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| Veröffentlicht in: | National Academy science letters 2023-10, Vol.46 (5), p.413-421 |
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| container_title | National Academy science letters |
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| creator | Kumar, Anuj Sachar, Silky |
| description | Leaf segmentation is important in assisting environmentalists to automatically segment the foreground leaf from the noisy background. The accuracy with which the image is segmented and the unwanted background areas are removed determines the result acquired from machine learning algorithms employed in feature extraction or classification. In this paper, the most commonly used unsupervised clustering algorithm
K
-means has been discussed, which has further been optimized using particle swarm optimization and firefly algorithms, and the performance of the three techniques has been compared and results have been presented. The evaluation metrics used are sensitivity, specificity, segmentation accuracy, precision, dice, Jaccard distance and MCC. The experiments have been performed on 50 images from each class taken from the PlantVillage dataset. |
| doi_str_mv | 10.1007/s40009-023-01285-0 |
| format | Article |
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K
-means has been discussed, which has further been optimized using particle swarm optimization and firefly algorithms, and the performance of the three techniques has been compared and results have been presented. The evaluation metrics used are sensitivity, specificity, segmentation accuracy, precision, dice, Jaccard distance and MCC. The experiments have been performed on 50 images from each class taken from the PlantVillage dataset.</description><identifier>ISSN: 0250-541X</identifier><identifier>EISSN: 2250-1754</identifier><identifier>DOI: 10.1007/s40009-023-01285-0</identifier><language>eng</language><publisher>New Delhi: Springer India</publisher><subject>Algorithms ; Background noise ; Clustering ; Environmentalists ; Feature extraction ; Heuristic methods ; History of Science ; Humanities and Social Sciences ; Image processing ; Image segmentation ; Leaves ; Machine learning ; multidisciplinary ; Particle swarm optimization ; Science ; Science (multidisciplinary) ; Segmentation ; Short Communication ; Swarm intelligence</subject><ispartof>National Academy science letters, 2023-10, Vol.46 (5), p.413-421</ispartof><rights>The Author(s), under exclusive licence to The National Academy of Sciences, India 2023</rights><rights>The Author(s), under exclusive licence to The National Academy of Sciences, India 2023.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-c3e9554e84e1ac2a637121e21e0a7b7f1d34158f18da9a69addc438b7117f6863</cites><orcidid>0000-0002-0786-0527</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40009-023-01285-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40009-023-01285-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Kumar, Anuj</creatorcontrib><creatorcontrib>Sachar, Silky</creatorcontrib><title>Swarm Intelligence for Segmentation of Leaf Images</title><title>National Academy science letters</title><addtitle>Natl. Acad. Sci. Lett</addtitle><description>Leaf segmentation is important in assisting environmentalists to automatically segment the foreground leaf from the noisy background. The accuracy with which the image is segmented and the unwanted background areas are removed determines the result acquired from machine learning algorithms employed in feature extraction or classification. In this paper, the most commonly used unsupervised clustering algorithm
K
-means has been discussed, which has further been optimized using particle swarm optimization and firefly algorithms, and the performance of the three techniques has been compared and results have been presented. The evaluation metrics used are sensitivity, specificity, segmentation accuracy, precision, dice, Jaccard distance and MCC. The experiments have been performed on 50 images from each class taken from the PlantVillage dataset.</description><subject>Algorithms</subject><subject>Background noise</subject><subject>Clustering</subject><subject>Environmentalists</subject><subject>Feature extraction</subject><subject>Heuristic methods</subject><subject>History of Science</subject><subject>Humanities and Social Sciences</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Leaves</subject><subject>Machine learning</subject><subject>multidisciplinary</subject><subject>Particle swarm optimization</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Segmentation</subject><subject>Short Communication</subject><subject>Swarm intelligence</subject><issn>0250-541X</issn><issn>2250-1754</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kM1Lw0AQxRdRsNT-A54CnqMz-5HdHKX4USh4qIK3ZZvMhpQmqbsp4n_v1gjehIE3h997wzzGrhFuEUDfRQkAZQ5c5IDcqBzO2IxzBTlqJc_ZDE67kvh-yRYx7hINqlAK-YzxzacLXbbqR9rv24b6ijI_hGxDTUf96MZ26LPBZ2tyPlt1rqF4xS6820da_OqcvT0-vC6f8_XL02p5v84rrmHMK0GlUpKMJHQVd4XQyJHSgNNb7bEWEpXxaGpXuqJ0dV1JYbYaUfvCFGLObqbcQxg-jhRHuxuOoU8nLTc6_VAkSRSfqCoMMQby9hDazoUvi2BP9dipHpvqsT_1WEgmMZligvuGwl_0P65vIVtlnA</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Kumar, Anuj</creator><creator>Sachar, Silky</creator><general>Springer India</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-0786-0527</orcidid></search><sort><creationdate>20231001</creationdate><title>Swarm Intelligence for Segmentation of Leaf Images</title><author>Kumar, Anuj ; Sachar, Silky</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-c3e9554e84e1ac2a637121e21e0a7b7f1d34158f18da9a69addc438b7117f6863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Background noise</topic><topic>Clustering</topic><topic>Environmentalists</topic><topic>Feature extraction</topic><topic>Heuristic methods</topic><topic>History of Science</topic><topic>Humanities and Social Sciences</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Leaves</topic><topic>Machine learning</topic><topic>multidisciplinary</topic><topic>Particle swarm optimization</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Segmentation</topic><topic>Short Communication</topic><topic>Swarm intelligence</topic><toplevel>online_resources</toplevel><creatorcontrib>Kumar, Anuj</creatorcontrib><creatorcontrib>Sachar, Silky</creatorcontrib><collection>CrossRef</collection><jtitle>National Academy science letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kumar, Anuj</au><au>Sachar, Silky</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Swarm Intelligence for Segmentation of Leaf Images</atitle><jtitle>National Academy science letters</jtitle><stitle>Natl. Acad. Sci. Lett</stitle><date>2023-10-01</date><risdate>2023</risdate><volume>46</volume><issue>5</issue><spage>413</spage><epage>421</epage><pages>413-421</pages><issn>0250-541X</issn><eissn>2250-1754</eissn><abstract>Leaf segmentation is important in assisting environmentalists to automatically segment the foreground leaf from the noisy background. The accuracy with which the image is segmented and the unwanted background areas are removed determines the result acquired from machine learning algorithms employed in feature extraction or classification. In this paper, the most commonly used unsupervised clustering algorithm
K
-means has been discussed, which has further been optimized using particle swarm optimization and firefly algorithms, and the performance of the three techniques has been compared and results have been presented. The evaluation metrics used are sensitivity, specificity, segmentation accuracy, precision, dice, Jaccard distance and MCC. The experiments have been performed on 50 images from each class taken from the PlantVillage dataset.</abstract><cop>New Delhi</cop><pub>Springer India</pub><doi>10.1007/s40009-023-01285-0</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-0786-0527</orcidid></addata></record> |
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| ispartof | National Academy science letters, 2023-10, Vol.46 (5), p.413-421 |
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| language | eng |
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| source | SpringerNature Journals |
| subjects | Algorithms Background noise Clustering Environmentalists Feature extraction Heuristic methods History of Science Humanities and Social Sciences Image processing Image segmentation Leaves Machine learning multidisciplinary Particle swarm optimization Science Science (multidisciplinary) Segmentation Short Communication Swarm intelligence |
| title | Swarm Intelligence for Segmentation of Leaf Images |
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