An image segmentation technique with statistical strategies for pesticide efficacy assessment
Image analysis is a useful technique to evaluate the efficacy of a treatment for weed control. In this study, we address two practical challenges in the image analysis. First, it is challenging to accurately quantify the efficacy of a treatment when an entire experimental unit is not affected by the...
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| Veröffentlicht in: | PloS one 2021-03, Vol.16 (3), p.e0248592-e0248592 |
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| description | Image analysis is a useful technique to evaluate the efficacy of a treatment for weed control. In this study, we address two practical challenges in the image analysis. First, it is challenging to accurately quantify the efficacy of a treatment when an entire experimental unit is not affected by the treatment. Second, RGB codes, which can be used to identify weed growth in the image analysis, may not be stable due to various surrounding factors, human errors, and unknown reasons. To address the former challenge, the technique of image segmentation is considered. To address the latter challenge, the proportion of weed area is adjusted under a beta regression model. The beta regression is a useful statistical method when the outcome variable (proportion) ranges between zero and one. In this study, we attempt to accurately evaluate the efficacy of a 35% hydrogen peroxide (HP). The image segmentation was applied to separate two zones, where the HP was directly applied (gray zone) and its surroundings (nongray zone). The weed growth was monitored for five days after the treatment, and the beta regression was implemented to compare the weed growth between the gray zone and the control group and between the nongray zone and the control group. The estimated treatment effect was substantially different after the implementation of image segmentation and the adjustment of green area. |
| doi_str_mv | 10.1371/journal.pone.0248592 |
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In this study, we address two practical challenges in the image analysis. First, it is challenging to accurately quantify the efficacy of a treatment when an entire experimental unit is not affected by the treatment. Second, RGB codes, which can be used to identify weed growth in the image analysis, may not be stable due to various surrounding factors, human errors, and unknown reasons. To address the former challenge, the technique of image segmentation is considered. To address the latter challenge, the proportion of weed area is adjusted under a beta regression model. The beta regression is a useful statistical method when the outcome variable (proportion) ranges between zero and one. In this study, we attempt to accurately evaluate the efficacy of a 35% hydrogen peroxide (HP). The image segmentation was applied to separate two zones, where the HP was directly applied (gray zone) and its surroundings (nongray zone). The weed growth was monitored for five days after the treatment, and the beta regression was implemented to compare the weed growth between the gray zone and the control group and between the nongray zone and the control group. The estimated treatment effect was substantially different after the implementation of image segmentation and the adjustment of green area.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0248592</identifier><identifier>PMID: 33720980</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Agricultural land ; Biology and Life Sciences ; Cereal crops ; Codes ; Control ; Correlation analysis ; Crops ; Data analysis ; Data collection ; Deep learning ; Drafting software ; Editing ; Human error ; Image analysis ; Image processing ; Image segmentation ; Learning algorithms ; Longitudinal studies ; Machine learning ; Mathematical analysis ; Methods ; Pesticides ; Physical Sciences ; Research and Analysis Methods ; Statistical analysis ; Technology ; Technology utilization ; Testing ; Visualization ; Weeds</subject><ispartof>PloS one, 2021-03, Vol.16 (3), p.e0248592-e0248592</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). 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In this study, we address two practical challenges in the image analysis. First, it is challenging to accurately quantify the efficacy of a treatment when an entire experimental unit is not affected by the treatment. Second, RGB codes, which can be used to identify weed growth in the image analysis, may not be stable due to various surrounding factors, human errors, and unknown reasons. To address the former challenge, the technique of image segmentation is considered. To address the latter challenge, the proportion of weed area is adjusted under a beta regression model. The beta regression is a useful statistical method when the outcome variable (proportion) ranges between zero and one. In this study, we attempt to accurately evaluate the efficacy of a 35% hydrogen peroxide (HP). The image segmentation was applied to separate two zones, where the HP was directly applied (gray zone) and its surroundings (nongray zone). The weed growth was monitored for five days after the treatment, and the beta regression was implemented to compare the weed growth between the gray zone and the control group and between the nongray zone and the control group. The estimated treatment effect was substantially different after the implementation of image segmentation and the adjustment of green area.</description><subject>Agricultural land</subject><subject>Biology and Life Sciences</subject><subject>Cereal crops</subject><subject>Codes</subject><subject>Control</subject><subject>Correlation analysis</subject><subject>Crops</subject><subject>Data analysis</subject><subject>Data collection</subject><subject>Deep learning</subject><subject>Drafting software</subject><subject>Editing</subject><subject>Human error</subject><subject>Image analysis</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Learning algorithms</subject><subject>Longitudinal studies</subject><subject>Machine learning</subject><subject>Mathematical analysis</subject><subject>Methods</subject><subject>Pesticides</subject><subject>Physical Sciences</subject><subject>Research and Analysis Methods</subject><subject>Statistical 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image segmentation technique with statistical strategies for pesticide efficacy assessment</title><author>Kim, Steven B ; Kim, Dong Sub ; Mo, Xiaoming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-61e61398c1300568957d7720a2bb080714a1133ca1c9438416aa153a9c8bad9c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Agricultural land</topic><topic>Biology and Life Sciences</topic><topic>Cereal crops</topic><topic>Codes</topic><topic>Control</topic><topic>Correlation analysis</topic><topic>Crops</topic><topic>Data analysis</topic><topic>Data collection</topic><topic>Deep learning</topic><topic>Drafting software</topic><topic>Editing</topic><topic>Human error</topic><topic>Image analysis</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Learning algorithms</topic><topic>Longitudinal studies</topic><topic>Machine 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one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Steven B</au><au>Kim, Dong Sub</au><au>Mo, Xiaoming</au><au>Niedz, Randall P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An image segmentation technique with statistical strategies for pesticide efficacy assessment</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-03-15</date><risdate>2021</risdate><volume>16</volume><issue>3</issue><spage>e0248592</spage><epage>e0248592</epage><pages>e0248592-e0248592</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Image analysis is a useful technique to evaluate the efficacy of a treatment for weed control. In this study, we address two practical challenges in the image analysis. First, it is challenging to accurately quantify the efficacy of a treatment when an entire experimental unit is not affected by the treatment. Second, RGB codes, which can be used to identify weed growth in the image analysis, may not be stable due to various surrounding factors, human errors, and unknown reasons. To address the former challenge, the technique of image segmentation is considered. To address the latter challenge, the proportion of weed area is adjusted under a beta regression model. The beta regression is a useful statistical method when the outcome variable (proportion) ranges between zero and one. In this study, we attempt to accurately evaluate the efficacy of a 35% hydrogen peroxide (HP). The image segmentation was applied to separate two zones, where the HP was directly applied (gray zone) and its surroundings (nongray zone). The weed growth was monitored for five days after the treatment, and the beta regression was implemented to compare the weed growth between the gray zone and the control group and between the nongray zone and the control group. The estimated treatment effect was substantially different after the implementation of image segmentation and the adjustment of green area.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33720980</pmid><doi>10.1371/journal.pone.0248592</doi><tpages>e0248592</tpages><orcidid>https://orcid.org/0000-0001-9997-382X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agricultural land Biology and Life Sciences Cereal crops Codes Control Correlation analysis Crops Data analysis Data collection Deep learning Drafting software Editing Human error Image analysis Image processing Image segmentation Learning algorithms Longitudinal studies Machine learning Mathematical analysis Methods Pesticides Physical Sciences Research and Analysis Methods Statistical analysis Technology Technology utilization Testing Visualization Weeds |
| title | An image segmentation technique with statistical strategies for pesticide efficacy assessment |
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