Vista Equipo: Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing

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...

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Autores Principales:	Prendas-Rojas, Juan Pablo, Figueroa-Mata, Geovanni, Ramírez-Montero, Marianyela, Calderón-Fallas, Rafael Ángel, Ramírez-Bogantes, Melvin, Travieso-González, Carlos Manuel
Formato:	Artículo
Idioma:	Español
Publicado:	Editorial Tecnológica de Costa Rica 2018
Materias:	Nosema; image segmentation; object count; image processing. Nosema; segmentación de imágenes; conteo de objetos; procesamiento de imágenes.
Acceso en línea:	https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/3621 https://hdl.handle.net/2238/9903

id	RepoTEC9903
recordtype	dspace
spelling	RepoTEC99032018-08-16T19:45:54Z Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing Diagnóstico automático de infestación por Nosemiasis en abejas melíferas mediante procesado de imágenes Prendas-Rojas, Juan Pablo Figueroa-Mata, Geovanni Ramírez-Montero, Marianyela Calderón-Fallas, Rafael Ángel Ramírez-Bogantes, Melvin Travieso-González, Carlos Manuel Nosema; image segmentation; object count; image processing. Nosema; segmentación de imágenes; conteo de objetos; procesamiento de imágenes. 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%. Las abejas polinizan una gran variedad de especies de plantas, incluyendo los cultivos agrícolas. Se estima que cerca del 30% del alimento consumido por la población mundial es derivado de cultivos polinizados por abejas. La infestación por Nosemiasis es una de las principales causas de la pérdida de colmenas a nivel mundial. Los métodos de laboratorio para el diagnóstico del nivel de infestación por este microsporidio son lentos, caros y demandan la presencia de un experto. Se propone un sistema automático, confiable y económico de cuantificación de infestación por Nosema, a partir del procesamiento digital de imágenes.Con el uso de técnicas de segmentación de imágenes, caracterización de objetos y conteo de formas se han reproducido la técnican de Cantwell y Hemocitómetro de manera automática. Para el conteo de esporas se implementaron tres descriptores el tamaño, la excentricidad y la circularidad, de manera tal que son invariantes a la escala y rotación de las imágenes. Se trabajó con un total de 375 fotografías agrupadas en carpetas de 5, las cuales fueron previamente etiquetadas por un experto según el nivel de infestación (muy leve, leve, moderado, semifuerte y fuerte). Con ello se alcanzó un porcentaje de diagnóstico correcto de infestación del 84%. 2018-06-29 2018-08-16T19:45:54Z 2018-08-16T19:45:54Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artículo revisado por pares https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/3621 10.18845/tm.v31i2.3621 https://hdl.handle.net/2238/9903 spa https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/3621/pdf Copyright (c) 2018 Revista Tecnología en Marcha application/pdf Editorial Tecnológica de Costa Rica Revista Tecnología en Marcha; Vol. 31, Núm. 2: Abril-Junio 2018; 14-25 2215-3241 0379-3982
institution	Tecnológico de Costa Rica
collection	Repositorio TEC
language	Español
topic	Nosema; image segmentation; object count; image processing. Nosema; segmentación de imágenes; conteo de objetos; procesamiento de imágenes.
spellingShingle	Nosema; image segmentation; object count; image processing. Nosema; segmentación de imágenes; conteo de objetos; procesamiento de imágenes. Prendas-Rojas, Juan Pablo Figueroa-Mata, Geovanni Ramírez-Montero, Marianyela Calderón-Fallas, Rafael Ángel Ramírez-Bogantes, Melvin Travieso-González, Carlos Manuel Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing
description	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%.
format	Artículo
author	Prendas-Rojas, Juan Pablo Figueroa-Mata, Geovanni Ramírez-Montero, Marianyela Calderón-Fallas, Rafael Ángel Ramírez-Bogantes, Melvin Travieso-González, Carlos Manuel
author_sort	Prendas-Rojas, Juan Pablo
title	Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing
title_short	Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing
title_full	Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing
title_fullStr	Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing
title_full_unstemmed	Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing
title_sort	automatic diagnostic of nosemiasis infestation on honey bee using image processing
publisher	Editorial Tecnológica de Costa Rica
publishDate	2018
url	https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/3621 https://hdl.handle.net/2238/9903
_version_	1796138867196166144
score	12.2319145

Automatic diagnostic of Nosemiasis Infestation on honey bee using image processing

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