Automatic identification of weed seeds by color image processing

Autores
Granitto, Pablo Miguel; Navone, Hugo Daniel; Verdes, Pablo Fabián; Ceccatto, Hermenegildo Alejandro
Año de publicación
2000
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The analysis and classification of seeds are essential activities contributing to the final added value in the crop production. Besides varietal identification and cereal grain grading, it is also of interest in the agricultural industry the early identification of weeds from the analysis of strange seeds, with the purpose of chemically controlling their growth. The implementation of new methods for reliable and fast identification and classification of seeds is thus of major technical and economical importance. Like the manual identification work, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work we present a study of the discriminating power of morphological, color and textural characteristics of weed seeds, which can be measured from video images. This study was conducted on a large basis, considering images of weed seeds found in Argentina’s commercial seed production industry and listed by the Secretary of Agriculture as prohibited and primary- and secondary-tolerated weeds. We first describe the experimental setting and hardware used to capture the seed images. Then, we define the morphological, color and textural parameters measured from these images, and discuss the selection of the most relevant ones for identification purposes. Finally, we present results for the identification of test images obtained using a Naive Bayes classifier and a committee of Artificial Neural Networks.
Área: Procesamiento de Imágenes - Tratamiento de Señales - Computación Gráfica - Visualización
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Patterns
Neural nets
Image processing software
Digitization and Image Capture
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23599

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network_name_str SEDICI (UNLP)
spelling Automatic identification of weed seeds by color image processingGranitto, Pablo MiguelNavone, Hugo DanielVerdes, Pablo FabiánCeccatto, Hermenegildo AlejandroCiencias InformáticasPatternsNeural netsImage processing softwareDigitization and Image CaptureThe analysis and classification of seeds are essential activities contributing to the final added value in the crop production. Besides varietal identification and cereal grain grading, it is also of interest in the agricultural industry the early identification of weeds from the analysis of strange seeds, with the purpose of chemically controlling their growth. The implementation of new methods for reliable and fast identification and classification of seeds is thus of major technical and economical importance. Like the manual identification work, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work we present a study of the discriminating power of morphological, color and textural characteristics of weed seeds, which can be measured from video images. This study was conducted on a large basis, considering images of weed seeds found in Argentina’s commercial seed production industry and listed by the Secretary of Agriculture as prohibited and primary- and secondary-tolerated weeds. We first describe the experimental setting and hardware used to capture the seed images. Then, we define the morphological, color and textural parameters measured from these images, and discuss the selection of the most relevant ones for identification purposes. Finally, we present results for the identification of test images obtained using a Naive Bayes classifier and a committee of Artificial Neural Networks.Área: Procesamiento de Imágenes - Tratamiento de Señales - Computación Gráfica - VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI)2000-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23599enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:28:18Zoai:sedici.unlp.edu.ar:10915/23599Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:19.632SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Automatic identification of weed seeds by color image processing
title Automatic identification of weed seeds by color image processing
spellingShingle Automatic identification of weed seeds by color image processing
Granitto, Pablo Miguel
Ciencias Informáticas
Patterns
Neural nets
Image processing software
Digitization and Image Capture
title_short Automatic identification of weed seeds by color image processing
title_full Automatic identification of weed seeds by color image processing
title_fullStr Automatic identification of weed seeds by color image processing
title_full_unstemmed Automatic identification of weed seeds by color image processing
title_sort Automatic identification of weed seeds by color image processing
dc.creator.none.fl_str_mv Granitto, Pablo Miguel
Navone, Hugo Daniel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author Granitto, Pablo Miguel
author_facet Granitto, Pablo Miguel
Navone, Hugo Daniel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author_role author
author2 Navone, Hugo Daniel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Patterns
Neural nets
Image processing software
Digitization and Image Capture
topic Ciencias Informáticas
Patterns
Neural nets
Image processing software
Digitization and Image Capture
dc.description.none.fl_txt_mv The analysis and classification of seeds are essential activities contributing to the final added value in the crop production. Besides varietal identification and cereal grain grading, it is also of interest in the agricultural industry the early identification of weeds from the analysis of strange seeds, with the purpose of chemically controlling their growth. The implementation of new methods for reliable and fast identification and classification of seeds is thus of major technical and economical importance. Like the manual identification work, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work we present a study of the discriminating power of morphological, color and textural characteristics of weed seeds, which can be measured from video images. This study was conducted on a large basis, considering images of weed seeds found in Argentina’s commercial seed production industry and listed by the Secretary of Agriculture as prohibited and primary- and secondary-tolerated weeds. We first describe the experimental setting and hardware used to capture the seed images. Then, we define the morphological, color and textural parameters measured from these images, and discuss the selection of the most relevant ones for identification purposes. Finally, we present results for the identification of test images obtained using a Naive Bayes classifier and a committee of Artificial Neural Networks.
Área: Procesamiento de Imágenes - Tratamiento de Señales - Computación Gráfica - Visualización
Red de Universidades con Carreras en Informática (RedUNCI)
description The analysis and classification of seeds are essential activities contributing to the final added value in the crop production. Besides varietal identification and cereal grain grading, it is also of interest in the agricultural industry the early identification of weeds from the analysis of strange seeds, with the purpose of chemically controlling their growth. The implementation of new methods for reliable and fast identification and classification of seeds is thus of major technical and economical importance. Like the manual identification work, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work we present a study of the discriminating power of morphological, color and textural characteristics of weed seeds, which can be measured from video images. This study was conducted on a large basis, considering images of weed seeds found in Argentina’s commercial seed production industry and listed by the Secretary of Agriculture as prohibited and primary- and secondary-tolerated weeds. We first describe the experimental setting and hardware used to capture the seed images. Then, we define the morphological, color and textural parameters measured from these images, and discuss the selection of the most relevant ones for identification purposes. Finally, we present results for the identification of test images obtained using a Naive Bayes classifier and a committee of Artificial Neural Networks.
publishDate 2000
dc.date.none.fl_str_mv 2000-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23599
url http://sedici.unlp.edu.ar/handle/10915/23599
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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instname:Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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