Occurrence and distribution of the microsporidium Vairimorpha (Nosema) spp. in apiaries in Brazil - Systematic review DOI Open Access
Vivian Marina Gomes Barbosa Lage, Camila Dias Santana, Rejane Peixoto Noronha

et al.

Research Society and Development, Journal Year: 2024, Volume and Issue: 13(11), P. e123131147482 - e123131147482

Published: Nov. 19, 2024

Nosemosis is one of the main diseases bees, caused by microsporidia genus Vairimorpha (Nosema). The etiologic agents are apis and ceranae. These pathogens widely distributed around world, in Brazil, they have already been reported some states, but these data never gathered a review. objective this study was to perform systematic review occurrence distribution microsporidium spp. apiaries Brazil. search done SciELO, PubMed, DOAJ, Capes Journals (Scopus) databases. 14 articles were selected published English Portuguese. In included publications, only eight Brazilian states referred (Bahia, Espírito Santo, Goiás, Minas Gerais, Piauí, Rio Grande do Sul, Santa Catarina, São Paulo). No work conducted northern country, most publications concentrated southern southeastern. Regarding species, V. ceranae investigated, predominantly detected. Most studies with Africanized bee Apis mellifera. This showed which regions explored terms incidence pathogens, revealing gap be filled country's beekeeping surveillance health systems. addition, because great biodiversity, it evident that also need investigated other species.

Language: Английский

Emerging technologies for pollinator monitoring DOI
Toke T. Høye, Matteo Montagna, Bas Oteman

et al.

Current Opinion in Insect Science, Journal Year: 2025, Volume and Issue: unknown, P. 101367 - 101367

Published: March 1, 2025

Language: Английский

Citations

1
AInsectID Version 1.1: An Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks DOI Creative Commons
Haleema Sadia, Parvez Alam

Published: March 25, 2025

AInsectID Version 1.1 is a Graphical User Interface (GUI)‐operable open‐source insect species identification, color processing, and image analysis software. The software has current database of 150 insects integrates artificial intelligence approaches to streamline the process with focus on addressing prediction challenges posed by mimics. This paper presents methods algorithmic development, coupled rigorous machine training used enable high levels validation accuracy. Our work transfer learning prominent convolutional neural network (CNN) architectures, including VGG16, GoogLeNet, InceptionV3, MobileNetV2, ResNet50, ResNet101. Here, we employ both fine tuning hyperparameter optimization improve performance. After extensive computational experimentation, ResNet101 evidenced as being most effective CNN model, achieving accuracy 99.65%. dataset utilized for sourced from National Museum Scotland, Natural History London, open source datasets Zenodo (CERN's Data Center), ensuring diverse comprehensive collection species.

Language: Английский

Citations

0
AInsectID Version 1.1: an Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks DOI Creative Commons
Haleema Sadia, Parvez Alam

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 3, 2024

ABSTRACT AInsectID Version 1.1 1 , is a GUI operable open-source insect species identification, color processing 2 and image analysis software. The software has current database of 150 insects integrates Artificial Intelligence (AI) approaches to streamline the process with focus on addressing prediction challenges posed by mimics. This paper presents methods algorithmic development, coupled rigorous machine training used enable high levels validation accuracy. Our work transfer learning prominent convolutional neural network (CNN) architectures, including VGG16, GoogLeNet, InceptionV3, MobileNetV2, ResNet50, ResNet101. Here, we employ both fine tuning hyperparameter optimization improve performance. After extensive computational experimentation, ResNet101 evidenced as being most effective CNN model, achieving accuracy 99.65%. dataset utilized for sourced from National Museum Scotland (NMS), Natural History (NHM) London open source datasets Zenodo (CERN’s Data Center), ensuring diverse comprehensive collection species.

Language: Английский

Citations

0
Occurrence and distribution of the microsporidium Vairimorpha (Nosema) spp. in apiaries in Brazil - Systematic review DOI Open Access
Vivian Marina Gomes Barbosa Lage, Camila Dias Santana, Rejane Peixoto Noronha

et al.

Research Society and Development, Journal Year: 2024, Volume and Issue: 13(11), P. e123131147482 - e123131147482

Published: Nov. 19, 2024

Nosemosis is one of the main diseases bees, caused by microsporidia genus Vairimorpha (Nosema). The etiologic agents are apis and ceranae. These pathogens widely distributed around world, in Brazil, they have already been reported some states, but these data never gathered a review. objective this study was to perform systematic review occurrence distribution microsporidium spp. apiaries Brazil. search done SciELO, PubMed, DOAJ, Capes Journals (Scopus) databases. 14 articles were selected published English Portuguese. In included publications, only eight Brazilian states referred (Bahia, Espírito Santo, Goiás, Minas Gerais, Piauí, Rio Grande do Sul, Santa Catarina, São Paulo). No work conducted northern country, most publications concentrated southern southeastern. Regarding species, V. ceranae investigated, predominantly detected. Most studies with Africanized bee Apis mellifera. This showed which regions explored terms incidence pathogens, revealing gap be filled country's beekeeping surveillance health systems. addition, because great biodiversity, it evident that also need investigated other species.

Language: Английский

Citations

0