Using digital image processing for counting whiteflies on soybean leaves
This paper presents a new system, based on digital image processing, to quantify whiteflies on soybean leaves. This approach allows counting to be fully automated, considerably speeding up the process in comparison with the manual approach. The proposed algorithm is capable of detecting and quantify...
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Published in | Journal of Asia-Pacific entomology Vol. 17; no. 4; pp. 685 - 694 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.12.2014
한국응용곤충학회 |
Subjects | |
Online Access | Get full text |
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Abstract | This paper presents a new system, based on digital image processing, to quantify whiteflies on soybean leaves. This approach allows counting to be fully automated, considerably speeding up the process in comparison with the manual approach. The proposed algorithm is capable of detecting and quantifying not only adult whiteflies, but also specimens in the nymph stage. A complete performance evaluation is presented, with emphasis on the conditions and situations for which the algorithm succeeds, and also on the circumstances that need further work. Although this proposal was entirely developed using soybean leaves, it can be easily extended to other kinds of crops with little or no changes in the algorithm. The system employs only widely used image processing operations, so it can be easily implemented in any image processing software package.
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•A new method for counting whiteflies in soybean leaves is presented.•The method is based on conventional digital images.•The method employs only well known morphological operations and heuristic rules.•The method is capable of identifying whiteflies in different stages of their life cycle.•The method provides good count estimates even under conditions moderately far from ideal. |
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AbstractList | This paper presents a new system, based on digital image processing, to quantify whiteflies on soybean leaves. This approach allows counting to be fully automated, considerably speeding up the process in comparison with the manual approach. The proposed algorithm is capable of detecting and quantifying not only adult whiteflies, but also specimens in the nymph stage. A complete performance evaluation is presented, with emphasis on the conditions and situations for which the algorithm succeeds, and also on the circumstances that need further work. Although this proposal was entirely developed using soybean leaves, it can be easily extended to other kinds of crops with little or no changes in the algorithm. The system employs only widely used image processing operations, so it can be easily implemented in any image processing software package. This paper presents a new system, based on digital image processing, to quantify whiteflies on soybean leaves. This approach allows counting to be fully automated, considerably speeding up the process in comparison with the manual approach. The proposed algorithm is capable of detecting and quantifying not only adult whiteflies, but also specimens in the nymph stage. A complete performance evaluation is presented, with emphasis on the conditions and situations for which the algorithm succeeds, and also on the circumstances that need further work. Although this proposal was entirely developed using soybean leaves, it can be easily extended to other kinds of crops with little or no changes in the algorithm. The system employs only widely used image processing operations, so it can be easily implemented in any image processing software package. [Display omitted] •A new method for counting whiteflies in soybean leaves is presented.•The method is based on conventional digital images.•The method employs only well known morphological operations and heuristic rules.•The method is capable of identifying whiteflies in different stages of their life cycle.•The method provides good count estimates even under conditions moderately far from ideal. This paper presents a new system, based on digital image processing, to quantify whiteflies on soybean leaves. This approach allows counting to be fully automated, considerably speeding up the process in comparisonwith the manual approach. The proposed algorithm is capable of detecting and quantifying not only adultwhiteflies,but also specimens in the nymph stage. A complete performance evaluation is presented, with emphasis onthe conditions and situations for which the algorithm succeeds, and also on the circumstances that need furtherwork. Although this proposal was entirely developed using soybean leaves, it can be easily extended to otherkinds of crops with little or no changes in the algorithm. The systememploys only widely used image processingoperations, so it can be easily implemented in any image processing software package. KCI Citation Count: 12 |
Author | Barbedo, Jayme Garcia Arnal |
Author_xml | – sequence: 1 givenname: Jayme Garcia Arnal surname: Barbedo fullname: Barbedo, Jayme Garcia Arnal email: jayme.barbedo@embrapa.br organization: Embrapa Agricultural Informatics, Av. André Tosello, 209-Barão Geraldo, C.P. 6041, 13083-886 Campinas, SP, Brazil |
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Cites_doi | 10.1145/965139.807361 10.1016/j.compag.2007.11.009 10.17660/ActaHortic.2005.691.95 10.9790/2834-565763 10.11646/zootaxa.1492.1.1 |
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References | Smith (bb0075) 1978; 12 Bechar, Moisan (bb0020) 2010 Pokharkar, Thool (bb0065) 2012; 1 Martin, Moisan, Paris, Nicolas (bb0055) 2011 Faithpraise, Birch, Young, Obu, Faithpraise, Chatwin (bb0035) 2013; 4 Barbedo (bb0005) 2012 Martin, Mound (bb0050) 2007; 1492 Huddar, Gowri, Keerthana, Vasanthi, Rupanagudi (bb0045) 2012 Barbedo (bb0010) 2013 Boissard, Martin, Moisan (bb0025) 2008; 62 Mundada, Gohokar (bb0060) 2013; 5 Powers (bb0070) 2011; 2 Souza (bb0080) 2004 Bauch, Rath (bb0015) 2005; 691 Cho, Choi, Qiao, Ji, Kim, Uhm, Chon (bb0030) 2007; 1 Flint (bb0040) 2002 Bauch (10.1016/j.aspen.2014.06.014_bb0015) 2005; 691 Martin (10.1016/j.aspen.2014.06.014_bb0050) 2007; 1492 Flint (10.1016/j.aspen.2014.06.014_bb0040) 2002 Powers (10.1016/j.aspen.2014.06.014_bb0070) 2011; 2 Cho (10.1016/j.aspen.2014.06.014_bb0030) 2007; 1 Pokharkar (10.1016/j.aspen.2014.06.014_bb0065) 2012; 1 Barbedo (10.1016/j.aspen.2014.06.014_bb0005) 2012 Boissard (10.1016/j.aspen.2014.06.014_bb0025) 2008; 62 Mundada (10.1016/j.aspen.2014.06.014_bb0060) 2013; 5 Faithpraise (10.1016/j.aspen.2014.06.014_bb0035) 2013; 4 Martin (10.1016/j.aspen.2014.06.014_bb0055) 2011 Smith (10.1016/j.aspen.2014.06.014_bb0075) 1978; 12 Souza (10.1016/j.aspen.2014.06.014_bb0080) 2004 Barbedo (10.1016/j.aspen.2014.06.014_bb0010) 2013 Huddar (10.1016/j.aspen.2014.06.014_bb0045) 2012 Bechar (10.1016/j.aspen.2014.06.014_bb0020) 2010 |
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SubjectTerms | adults Aleyrodidae algorithms computer software Counting crops Digital image processing digital images image analysis leaves Soybean leaves soybeans Whiteflies 농수해양학 |
Title | Using digital image processing for counting whiteflies on soybean leaves |
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