Automatic Grading of Green Intensity in Soybean Seeds

In this work we introduce a low cost machine vision system for grading problems in agriculture. Instead of a careful evaluation of a given quantity over a reduced number of samples with a high cost dedicated equipment, we propose to measure the quantity with less precision but over a much bigger num...

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Detalles Bibliográficos
Autores principales: Namías, Rafael, Gallo, Carina, Craviotto, Roque M., Arango, Miriam R., Granitto, Pablo Miguel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2012
Materias:
Ciencias Informáticas
Soybean
Machine Vision
Green seeds
Grading
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/123731
https://41jaiio.sadio.org.ar/sites/default/files/9_ASAI_2012.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-123731
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Soybean
Machine Vision
Green seeds
Grading
spellingShingle Ciencias Informáticas
Soybean
Machine Vision
Green seeds
Grading
Namías, Rafael
Gallo, Carina
Craviotto, Roque M.
Arango, Miriam R.
Granitto, Pablo Miguel
Automatic Grading of Green Intensity in Soybean Seeds
topic_facet Ciencias Informáticas
Soybean
Machine Vision
Green seeds
Grading
description In this work we introduce a low cost machine vision system for grading problems in agriculture. Instead of a careful evaluation of a given quantity over a reduced number of samples with a high cost dedicated equipment, we propose to measure the quantity with less precision but over a much bigger number of samples. The advantage of our procedure is that very low cost vision equipment can be used in this case. For example, we used a standard flatbed scanner as an integrated illumination plus acquisition hardware. Our system is aimed at the quantification of the amount of chlorophyll present in a production batch of soybean seeds. To this end we arbitrarily divided green seeds in four classes, with a decreasing amount of green pigment in each class. In particular, in this work we evaluate the possibility of an accurate discrimination among the four classes of green seeds using machine vision methods. We show that morphological features have low discrimination capabilities, and that a set of simple features measured over color distributions provides good separation among grades. Also, most errors are assignations to neighbor grades, which have a lower cost in grading. The good results are almost independent from the classifier being use, Random forest or Support Vector Machines with a Gaussian kernel in our case.
format Objeto de conferencia
Objeto de conferencia
author Namías, Rafael
Gallo, Carina
Craviotto, Roque M.
Arango, Miriam R.
Granitto, Pablo Miguel
author_facet Namías, Rafael
Gallo, Carina
Craviotto, Roque M.
Arango, Miriam R.
Granitto, Pablo Miguel
author_sort Namías, Rafael
title Automatic Grading of Green Intensity in Soybean Seeds
title_short Automatic Grading of Green Intensity in Soybean Seeds
title_full Automatic Grading of Green Intensity in Soybean Seeds
title_fullStr Automatic Grading of Green Intensity in Soybean Seeds
title_full_unstemmed Automatic Grading of Green Intensity in Soybean Seeds
title_sort automatic grading of green intensity in soybean seeds
publishDate 2012
url http://sedici.unlp.edu.ar/handle/10915/123731
https://41jaiio.sadio.org.ar/sites/default/files/9_ASAI_2012.pdf
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AT gallocarina automaticgradingofgreenintensityinsoybeanseeds
AT craviottoroquem automaticgradingofgreenintensityinsoybeanseeds
AT arangomiriamr automaticgradingofgreenintensityinsoybeanseeds
AT granittopablomiguel automaticgradingofgreenintensityinsoybeanseeds
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