Identifying Central Features of Cotton Leaves in Digital Images with Difficult Backgrounds
Many digital image processing techniques applied to agricultural problems have as main target the leaves of certain species of plants. The most basic task in such a context is to segment the leaf of interest from the rest of the scene, which is relatively straightforward when the leaf is isolated an...
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Veröffentlicht in: | Revista IEEE América Latina 2015-09, Vol.13 (9), p.3072-3079 |
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description | Many digital image processing techniques applied to agricultural problems have as main target the leaves of certain species of plants. The most basic task in such a context is to segment the leaf of interest from the rest of the scene, which is relatively straightforward when the leaf is isolated and the image is captured under controlled conditions. However, real field conditions will often imply in little control over lighting and, more importantly, the background may include several elements that make the task considerably more challenging. This is especially true if there are other leaves with similar shape, texture and color in the scene, which is often the case. This paper presents a method to identify the main node of cotton leaves (where the petiole meets the veins) and the main primary vein, giving valuable information about the position and orientation of those leaves. The only constraint to which the method is subject is that the leaf of interest be located in a central position in the image. |
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The most basic task in such a context is to segment the leaf of interest from the rest of the scene, which is relatively straightforward when the leaf is isolated and the image is captured under controlled conditions. However, real field conditions will often imply in little control over lighting and, more importantly, the background may include several elements that make the task considerably more challenging. This is especially true if there are other leaves with similar shape, texture and color in the scene, which is often the case. This paper presents a method to identify the main node of cotton leaves (where the petiole meets the veins) and the main primary vein, giving valuable information about the position and orientation of those leaves. The only constraint to which the method is subject is that the leaf of interest be located in a central position in the image.</description><identifier>ISSN: 1548-0992</identifier><identifier>EISSN: 1548-0992</identifier><identifier>DOI: 10.1109/TLA.2015.7350061</identifier><language>eng</language><publisher>Los Alamitos: IEEE</publisher><subject>Color ; Cotton ; cotton leaves ; Deformable models ; Digital images ; Digital imaging ; Image resolution ; Image segmentation ; Irrigation ; Leaves ; segmentation ; Surface layer ; Tasks ; Texture ; Veins</subject><ispartof>Revista IEEE América Latina, 2015-09, Vol.13 (9), p.3072-3079</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The only constraint to which the method is subject is that the leaf of interest be located in a central position in the image.</description><subject>Color</subject><subject>Cotton</subject><subject>cotton leaves</subject><subject>Deformable models</subject><subject>Digital images</subject><subject>Digital imaging</subject><subject>Image resolution</subject><subject>Image segmentation</subject><subject>Irrigation</subject><subject>Leaves</subject><subject>segmentation</subject><subject>Surface layer</subject><subject>Tasks</subject><subject>Texture</subject><subject>Veins</subject><issn>1548-0992</issn><issn>1548-0992</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkDFPwzAQRiMEEqWwI7FEYmFJOcdObI9QKFSKxFIWFss4dnBJk2I7oP57XLUgxHSn796dTi9JzhFMEAJ-vahuJjmgYkJxAVCig2SECsIy4Dw__NMfJyfeLwEwKxkeJS_zWnfBmo3tmnQaWyfbdKZlGJz2aW_SaR9C36WVlp8xsF16ZxsbIjRfySYmXza8xcwYq4Y2pLdSvTeuH7ranyZHRrZen-3rOHme3S-mj1n19DCf3lSZygsSsoIqJhklBnKqDDaveS2BAS8V1gQbDtgwziVjWJGcAslLIAXWiKiaUEINHidXu7tr138M2gexsl7ptpWd7gcvEKUMECecR_TyH7rsB9fF7yJFOJAoDkUKdpRyvfdOG7F2diXdRiAQW9kiyhZb2WIvO65c7Fas1voX_5l-AzMFeRc</recordid><startdate>201509</startdate><enddate>201509</enddate><creator>Garcia Arnal Barbedo, Jayme</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>201509</creationdate><title>Identifying Central Features of Cotton Leaves in Digital Images with Difficult Backgrounds</title><author>Garcia Arnal Barbedo, Jayme</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c254t-57c8a874f027cf3fb2da08096c3e43f903f899a883c42704260453e14cd4747f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Color</topic><topic>Cotton</topic><topic>cotton leaves</topic><topic>Deformable models</topic><topic>Digital images</topic><topic>Digital imaging</topic><topic>Image resolution</topic><topic>Image segmentation</topic><topic>Irrigation</topic><topic>Leaves</topic><topic>segmentation</topic><topic>Surface layer</topic><topic>Tasks</topic><topic>Texture</topic><topic>Veins</topic><toplevel>online_resources</toplevel><creatorcontrib>Garcia Arnal Barbedo, Jayme</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>Revista IEEE América Latina</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Garcia Arnal Barbedo, Jayme</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying Central Features of Cotton Leaves in Digital Images with Difficult Backgrounds</atitle><jtitle>Revista IEEE América Latina</jtitle><stitle>T-LA</stitle><date>2015-09</date><risdate>2015</risdate><volume>13</volume><issue>9</issue><spage>3072</spage><epage>3079</epage><pages>3072-3079</pages><issn>1548-0992</issn><eissn>1548-0992</eissn><abstract>Many digital image processing techniques applied to agricultural problems have as main target the leaves of certain species of plants. The most basic task in such a context is to segment the leaf of interest from the rest of the scene, which is relatively straightforward when the leaf is isolated and the image is captured under controlled conditions. However, real field conditions will often imply in little control over lighting and, more importantly, the background may include several elements that make the task considerably more challenging. This is especially true if there are other leaves with similar shape, texture and color in the scene, which is often the case. This paper presents a method to identify the main node of cotton leaves (where the petiole meets the veins) and the main primary vein, giving valuable information about the position and orientation of those leaves. 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subjects | Color Cotton cotton leaves Deformable models Digital images Digital imaging Image resolution Image segmentation Irrigation Leaves segmentation Surface layer Tasks Texture Veins |
title | Identifying Central Features of Cotton Leaves in Digital Images with Difficult Backgrounds |
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