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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23599
Ver los metadatos del registro completo
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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) |
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application/pdf |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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