Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing
Bees pollinate a wide variety of plant species, including agricultural crops. It is estimated that about 30% of the food consumed by the world population is derived from crops pollinated by bees.Nosemiasis infestation is one of the leading causes of bee hive loss worldwide. The laboratory methods fo...
| Autores Principales: | Prendas-Rojas, Juan Pablo, Figueroa-Mata, Geovanni, Ramírez-Montero, Marianyela, Calderón-Fallas, Rafael Ángel, Ramírez-Bogantes, Melvin, Travieso-González, Carlos Manuel |
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| Formato: | Artículo |
| Idioma: | Español |
| Publicado: |
Editorial Tecnológica de Costa Rica
2018
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| Acceso en línea: |
https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/3621 https://hdl.handle.net/2238/9903 |
| Sumario: |
Bees pollinate a wide variety of plant species, including agricultural crops. It is estimated that about 30% of the food consumed by the world population is derived from crops pollinated by bees.Nosemiasis infestation is one of the leading causes of bee hive loss worldwide. The laboratory methods for the diagnosis of the level of infestation by this microsporidium are slow, expensive and require the presence of an expert for spore count. It is proposed the creation of an automatic, reliable and economical system of quantification of Nosema infestation from digital image processing.Using the techniques of image segmentation, object characterization and shape counting, the Cantwell and Hemocytometer techniques have been automatically reproduced. For the counting of spores, three descriptors were implemented: size, eccentricity and circularity, in such a way that they are invariant to the scale and rotation of the images. We worked with a total of 375 photographs grouped in folders of 5, which were previously labeled according to the level of infestation (very mild, mild, moderate, semi-strong and strong). The correct diagnosis rate was 84%. |
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