Boosting classifiers for weed seeds identification
The identification and classification of seeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in ocular inspection one should consider seed size, shape, color and texture, which can be obtained from seed images. In this work we compl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2003-04-01
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| Series: | Journal of Computer Science and Technology |
| Subjects: | |
| Online Access: | https://journal.info.unlp.edu.ar/JCST/article/view/950 |
| _version_ | 1818719807014436864 |
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| author | Pablo Miguel Granitto Pablo A. Garralda Pablo Fabián Verdes Hermenegildo Alejandro Ceccatto |
| author_facet | Pablo Miguel Granitto Pablo A. Garralda Pablo Fabián Verdes Hermenegildo Alejandro Ceccatto |
| author_sort | Pablo Miguel Granitto |
| collection | DOAJ |
| description | The identification and classification of seeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in ocular inspection one should consider seed size, shape, color and texture, which can be obtained from seed images. In this work we complement previous studies on the discriminating power of these characteristics for the unique identification of seeds of 57 weed species. In particular, we discuss the possibility of improving the naïve Bayes and artificial neural network classifiers previously developed in order to avoid the use of color features as classification parameters. Morphological and textural seed characteristics can be obtained from black and white images, which are easier to process and require a cheaper hardware than color ones. To this end, we boost the classification methods by means of the AdaBoost.M1 technique, and compare the results obtained with the performance achieved when using color images. We conclude that boosting the naïve Bayes and neural classifiers does not fully compensate the discriminating power of color features. However, the improvement in classification accuracy might be enough to make the classifier still acceptable in practical applications. |
| first_indexed | 2024-12-17T20:12:48Z |
| format | Article |
| id | doaj.art-fc06b107f4de4db3a25565f7e2aad8e3 |
| institution | Directory Open Access Journal |
| issn | 1666-6046 1666-6038 |
| language | English |
| last_indexed | 2024-12-17T20:12:48Z |
| publishDate | 2003-04-01 |
| publisher | Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata |
| record_format | Article |
| series | Journal of Computer Science and Technology |
| spelling | doaj.art-fc06b107f4de4db3a25565f7e2aad8e32022-12-21T21:34:12ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382003-04-013013439643Boosting classifiers for weed seeds identificationPablo Miguel Granitto0Pablo A. Garralda1Pablo Fabián Verdes2Hermenegildo Alejandro Ceccatto3Instituto de Física Rosario, CONICET and Universidad Nacional de Rosario, 2000 Rosario, ArgentinaInstituto de Física Rosario, CONICET and Universidad Nacional de Rosario, 2000 Rosario, ArgentinaInstituto de Física Rosario, CONICET and Universidad Nacional de Rosario, 2000 Rosario, ArgentinaInstituto de Física Rosario, CONICET and Universidad Nacional de Rosario, 2000 Rosario, ArgentinaThe identification and classification of seeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in ocular inspection one should consider seed size, shape, color and texture, which can be obtained from seed images. In this work we complement previous studies on the discriminating power of these characteristics for the unique identification of seeds of 57 weed species. In particular, we discuss the possibility of improving the naïve Bayes and artificial neural network classifiers previously developed in order to avoid the use of color features as classification parameters. Morphological and textural seed characteristics can be obtained from black and white images, which are easier to process and require a cheaper hardware than color ones. To this end, we boost the classification methods by means of the AdaBoost.M1 technique, and compare the results obtained with the performance achieved when using color images. We conclude that boosting the naïve Bayes and neural classifiers does not fully compensate the discriminating power of color features. However, the improvement in classification accuracy might be enough to make the classifier still acceptable in practical applications.https://journal.info.unlp.edu.ar/JCST/article/view/950machine visionclassificationboostingneural networks |
| spellingShingle | Pablo Miguel Granitto Pablo A. Garralda Pablo Fabián Verdes Hermenegildo Alejandro Ceccatto Boosting classifiers for weed seeds identification Journal of Computer Science and Technology machine vision classification boosting neural networks |
| title | Boosting classifiers for weed seeds identification |
| title_full | Boosting classifiers for weed seeds identification |
| title_fullStr | Boosting classifiers for weed seeds identification |
| title_full_unstemmed | Boosting classifiers for weed seeds identification |
| title_short | Boosting classifiers for weed seeds identification |
| title_sort | boosting classifiers for weed seeds identification |
| topic | machine vision classification boosting neural networks |
| url | https://journal.info.unlp.edu.ar/JCST/article/view/950 |
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