DeepWings©: automatic wing geometric morphometrics classification of honey bee (Apis mellifera) subspecies using deep learning for detecting landmarks

Honey bee classification by wing geometric morphometrics entails the first step of manual annotation of 19 landmarks in the forewing vein junctions. This is a time-consuming and error- prone endeavor, with implications for classification accuracy. Herein, we developed a software called DeepWings © that overcomes this constraint in wing geometric morphometrics classification by automatically detecting the 19 landmarks on digital images of the right forewing. We used a database containing 7634 forewing images, including 1864 analyzed by F. Ruttner in the original delineation of 26 honey bee subspecies, to tune a convolutional neural network as a wing detector, a deep learning U-Net as a landmarks segmenter, and a support vector machine as a subspecies classifier. The implemented MobileNet wing detector was able to achieve a mAP of 0.975 and the landmarks segmenter was able to detect the 19 landmarks with 91.8% accuracy, with an average positional precision of 0.943 resemblance to manually annotated landmarks. The subspecies classifier, in turn, presented an average accuracy of 86.6% for 26 subspecies and 95.8% for a subset of five important subspecies. The final implementation of the system showed good speed performance, requiring only 14 s to process 10 images. DeepWings © is very user-friendly and is the first fully automated software, offered as a free Web service, for honey bee classification from wing geometric morphometrics. DeepWings© can be used for honey bee breeding, conservation, and even scientific purposes as it provides the coordinates of the landmarks in excel format, facilitating the work of research teams using classical identification approaches and alternative analytical tools.Financial support was provided through the program COMPETE 2020—POCI (Programa Operacional para a Competividade e Internacionalização) and by Portuguese funds through FCT (Fundação para a Ciência e a Tecnologia) in the framework of the project BeeHappy (POCI-01- 0145-FEDER-029871). FCT provided financial support by national funds (FCT/MCTES) to CIMO (UIDB/00690/2020).info:eu-repo/semantics/publishedVersio

Africanization of Melliferous Bees (Apis mellifera.L.) Bibliographic Review

Context: The hybridization process (Africanization) of European bees with African bees is a problem for apiculture farmers in the Americas, due to high swarming levels and defensiveness. The latter hinders colony handling, and has caused accidents to people and animals, increasing the risks of beekeeping. In this sense, there is a need for genetic breeding of melliferous bees, and previous identification of subspecies. Aim: To evaluate the origin of melliferous bees (Apis mellifera), and the process of Africanization and dispersion of Africanized bees throughout the Americas, as well as methods of identification. Methods: The databases of Sciencedirect, Google-Scholar, Scopus, and NCBI were reviewed under the following key words, Apis mellifera, Apis, Africanized bees, geometric morphometrics, mitochondrial DNA. Special emphasis was paid to papers published within the last five years. Results: The origin and distribution of melliferous bees, and the Africanization and dispersion processes of Africanized bees were described. Additionally, the evolution of methods for the characterization of Apis mellifera species were updated. Conclusions: Africanization can be considered the most important process in the transformation of conduct and morphological features of melliferous bees, which allowed for their rapid dispersion in the Americas. The identification methods based on parents are essential to know possible process of genetic erosion, and to present strategies for bee conservation and breeding in every region

Africanized honeybee population (Apis mellifera L.) in Nicaragua: Forewing length and mitotype lineages

Various subspecies of Apis mellifera L. were introduced to Central America since colonization 500 years ago. Hybridization increased with the entrance of the Africanized bee in Nicaragua in 1984. Rustic beekeeping activities and numerous feral swarms define the genetic pattern, reflected in phenotypic heterogeneity and remarkable differences in the behaviour of the bee colonies, especially the nest defence. Due to these facts, the question emerge about the degree of Africanization of honeybee colonies in Nicaragua. In this study, we identified Africanized honeybee colonies based on the single character "mean forewing length"and we corroborated our results by determining mitotypes using mtDNA analysis. Morphometric and genetic approaches were realized in three different geographical zones of Nicaragua and related to beehive characteristics and management. Worker bee samples were taken from the inside of 146 hives from 26 apiaries. Abdominal colour as phenotypic character was the first examination, followed by measurement of 1460 right forewings to determine corresponding probability of Africanization. More than 60% of the beehives showed phenotypic heterogeneity and mean forewing length of 8.74 mm (SD 0.16 mm) indicated a high degree of Africanization. Those results provided a selection of 96 worker bees to perform PCR of two worker bees per hive. For mitochondrial DNA analysis 14 samples from sentinel apiaries were added. Three from 61 beehives presented bees with different mtDNA. Throughout, three mitotypes of the African (A) lineage were detected; one mitotype is still unidentified. Mitotype A1 A. mellifera iberiensis was represented by 88 bees and mitotype A4 A. mellifera scutellata by 21 bees. Phylogenetic analysis confirmed the PCR findings. No associations were found between mitotypes, forewing length, beehive characteristics and management. A high degree of Africanization in A. mellifera colonies represented by two predominating mitotypes from the A lineage, prevail in Neotropical Nicaragua, with mitotype A4 predominating at higher altitudes.Fil: Düttmann, Christiane. Universidad Politécnica de Nicaragua; NicaraguaFil: Flores, Byron. Universidad Politécnica de Nicaragua; NicaraguaFil: Sheleby Elías, Jessica. Universidad Politécnica de Nicaragua; NicaraguaFil: Castillo, Gladys. Universidad Politécnica de Nicaragua; NicaraguaFil: Rodriguez, Daymara. Universidad de La Habana; CubaFil: Maggi, Matías Daniel. Universidad Nacional de Mar del Plata. Instituto de Investigaciones En Produccion, Sanidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Mar del Plata. Instituto de Investigaciones En Produccion, Sanidad y Ambiente.; ArgentinaFil: Demedio, Jorge. Universidad de La Habana; Cub

An algorithm for determination of the morphological characteristics of honey bees

At the current level of science and technology is used semi-automatic measurement of body parts of the bees, yielding images taken with a reference object via a camera or a scanner and then perform measurement by software using a pointing device. There are attempts to fully automated process of measuring the morphological characteristics of bees, at this stage there are conversions for Measuring wings, but this process for other parts are still made by manual way. The informative colour features for the separation of tergite and probotics from background in the image are selected by distance functions and correspondence analysis. Distances are determined between the values of the colour components of the object and background. From statistical analysis is found that appropriate for the separation of an object from background are S and V colour components of the HSV colour model. Algorithms and program in Matlab environment for separating tergite and proboscis from the background of the image and definition of their main sizes are developed. From the analysis of the results is found that the major influence on the accuracy of the measurement is the angle at the disposal of the bee body part in the image

Africanización de la abeja melífera (Apis mellifera L.). Revisión de Literatura

Contexto: El proceso de hibridación (africanización) de la abeja europea con abejas de origen africanos es un problema para los apicultores del continente americano, por su alta enjambrazón y defensividad, esta última dificulta en buena medida el manejo de las colonias y ha provocado accidentes en el caso de personas y anímales, lo que hace de la apicultura una actividad riesgosa. En este sentido, se ve la necesidad de mejoramiento genético de la abeja melífera para lo cual es esencial la identificación de subespecies. Objetivo: Evaluar el origen de la abeja mellifera (Apis mellifera), así como el proceso de africanización y dispersión de las abejas africanizadas a través del continente americano y métodos de identificación. Métodos: Se revisaron las bases de datos de Sciencedirect, Google-Scholar, Scopus y NCBI con el empleo de las palabras claves: Apis mellifera, Apis, abejas africanizadas, morfometría geométrica, ADN mitocondrial. Se enfatizó en los artículos de los últimos cinco años. Resultados: Se describen el origen y distribución de la abeja melífera, así como el proceso de africanización y dispersión de la abeja africanizada. Además, se actualiza sobre la evolución de los métodos de caracterización de subespecies de Apis mellifera. Conclusiones: La africanización puede considerarse el proceso más importante en la transformación de las características conductuales y morfológicas de la abeja melífera, las que permitieron su rápida dispersión a través del continente americano. Los métodos de identificación tanto vía materna como paterna son esenciales para conocer posibles procesos de erosión genética y para plantear estrategias de conservación y mejoramiento de las abejas a nivel de cada región

Can you make morphometrics work when you know the right answer? Pick and mix approaches for apple identification

Morphological classification of living things has challenged science for several centuries and has led to a wide range of objective morphometric approaches in data gathering and analysis. In this paper we explore those methods using apple cultivars, a model biological system in which discrete groups are pre-defined but in which there is a high level of overall morphological similarity. The effectiveness of morphometric techniques in discovering the groups is evaluated using statistical learning tools. No one technique proved optimal in classification on every occasion, linear morphometric techniques slightly out-performing geometric (72.6% accuracy on test set versus 66.7%). The combined use of these techniques with post-hoc knowledge of their individual successes with particular cultivars achieves a notably higher classification accuracy (77.8%). From this we conclude that even with pre-determined discrete categories, a range of approaches is needed where those categories are intrinsically similar to each other, and we raise the question of whether in studies where potentially continuous natural variation is being categorised the level of match between categories is routinely set too high

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU

The Cinderella discipline: morphometrics and their use in botanical classification

Between the 1960s and the present day, the use of morphology in plant taxonomy suffered a major decline, in part driven by the apparent superiority of DNA-based approaches to data generation. However, in recent years computer image recognition has re-kindled the interest in morphological techniques. Linear or geometric morphometric approaches have been employed to distinguish and classify a wide variety of organisms; each has strengths and weaknesses. Here we review these approaches with a focus on plant classification and present a case for the combination of morphometrics with statistical/machine learning. There is a large collection of classification techniques available for biological analysis and selecting the most appropriate one is not trivial. Performance should be evaluated using standardised metrics such as accuracy, sensitivity, and specificity. The gathering and storage of high-resolution images, combined with the processing power of desktop computers, makes morphometric approaches practical as a time- and cost-efficient way of non-destructive identification of plant samples

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU.info:eu-repo/semantics/publishedVersio