Classification Of Bees Research Articles

BeeNet: An End-To-End Deep Network For Bee Surveillance

Computer vision-based image classification plays a vital role in developing surveillance tools for measuring the biological behavior of bees and their disease detection. Native bees often face numerous environmental threats, ranging from invasive bees to numerous parasitic diseases, which affect not only the existing ecosystem but also the booming honey and wax industries. Numerous ML-based, pre-trained models showed potential in bee classification and monitoring tasks, but heavily curated data-set and closed-set models hinder their applicability in-field monitoring tasks. In this paper, we proposed a deep learning model to obtain improved levels of feature representations of eleven economically important bee species, fine-grained object (e.g. parasite, pollen) detection for bee-health monitoring and gradually progress to an end-to-end model to provide a solution for bee surveillance. Our model can extract learned feature representations from publicly available complex back-grounded images and propose similar usage on other domains through a qualitative analysis to learn appropriate defining features, Specifically for morphological classification. In particular, we utilize a variant of the transformer encoder-decoder architecture with the incorporation of extracted image features from a ResNet50 network. Our model obtained 92.45% classification accuracy on the bee species classification task and up to 99.18% on fine-grain object detection sub-tasks. Besides the classification task, our end-to-end model can detect varroa pests and pollen on bee images with 94.50% and 99.18% accuracies. Our model outperformed other existing models for bee surveillance or health monitoring tools. We also discussed the applicability of our BeenNet model in real-time settings. Overall, our end-to-end model has implications in both computer vision and biological computing tasks, such as visual feature extraction, in-domain classification, and sub-task identification. It will also serve as a baseline for future bee monitoring tools and a multi-modal model for disease detection.

Classification of Animals as Pet, Pest, or Profit: Consistency and Associated Variables Among Spanish University Students

ABSTRACT The present study analyzes the effectiveness (exhaustivity and mutual exclusivity) of a taxonomy for classifying animals into the categories of pet, pest, and profit (PPP), as well as variables that affect the classification assigned to the different species. A total of 463 university students (Mean age = 21.07 years; SD = 4.15), mainly female (87%), classified 14 animal species into the PPP categories and answered questionnaire assessments of personality, anthropomorphism, and attitudes toward the wellbeing of animals. On average, the different species were classified into a single PPP category in 71% of the cases (maximum 95% for dogs, and minimum 27% for chimpanzees), with 8% having multiple classifications (15% for horses and pigs, 21% for rabbits), and 21% being unclassified. Women used the profit category less than men (p < 0.05; g = 0.33). Attitudes toward animals, anthropomorphism, and conscientiousness presented significant correlations (p < 0.05) with the use of PPP categories. Finally, logistic regression analyses showed that these variables had a limited yet significative effect in predicting the classification of horses as either pet or profit (explained variance = 8%) and the classification of snakes as pet or pest (explained variance = 9%), but no effect on the classification of bees as pest or profit. The fact that 29% of the cases received multiple or null classifications indicates the need for caution when considering conclusions drawn from studies based on the PPP taxonomy. Personality, anthropomorphism, sex, and attitudes toward animals’ wellbeing had only a small effect on the classification of animals, but they revealed possible mechanisms for developing interventions to raise awareness about the treatment of animals.

Bees Assorter

Many attempts were made to classify the bees that is bumble bee or honey bee , there have been such a large amount of researches which were made to seek out the difference between them on the premise of various features like wing size , size of bee , color, life cycle and many more. But altogether the analysis there have been either that specialize in qualitative or quantitative , but to beat this issue , thus researchers came up with an answer which might be both qualitative and quantitative analysis made to classify them. And making use of machine learning algorithm to classify them gives a lift . Now the classification would take less time as these algorithms are pretty fast and accurate . By using machine learning work is made easy . Lots of photographs had to be collected and stored for data set. And by using these machine learning algorithms we would be getting information about the bees which might be employed by researchers in further classification of bees. Manipulation of images had to be done so as on prepare them in such a way that they will be applied to the algorithms and have feature extraction done. As there have been a lot of photographs(data set) which take a lot of space and also the area in which bees were present in these photographs were too small so to accommodate it dimension reduction was done , it might not consider other images like trees , leaves , flowers which were there present in the photograph which we elect as a data set.

X-ray computerised microtomography (MicroCT): a new technique for assessing external and internal morphology of bees

SummaryTraditionally, the classification of bees has been conducted with the aid of dissecting (light) microscopy. In more recent times, detailed information on external and internal morphology for bee classification has been obtained using scanning and transmission electron microscopy. MicroCT is emerging as a new method for non-invasive 3D computerised X-ray tomographic imaging of insects at the microscopic level and, in this study, has been evaluated for its use in morphological studies of bees. A Skyscan 1172 MicroCT system was used to assess the internal and external morphology of the Australian stingless bee Trigona carbonaria with particular focus on the proventricular plates. MicroCT was useful in non-invasively visualising gross external morphological features such as the articulations of the coxae, trochanters, tibiae and tarsi of each leg including broadened hind basitarsi. Image magnification revealed further detail such as antennal scapes and the various parts of the tongue including the proboscis and labium. However, the individual facets of the eye were barely discernable and MicroCT did not reveal fine details of hairs on the body or legs. Internal morphology was clearly visualised, including the tracheal system and details of the proventriculus and proventricular basal plates which form the leaflets of the proventricular valve. Thus, the characteristic features of the proventricular basal plates of Meliponini could be quickly and easily identified non-invasively. Therefore, MicroCT, as one of the emerging techniques of diagnostic radioentomology, has particular advantages for non-invasively and non-destructively imaging bees and, particularly, rare or more scientifically valued insects such as museum specimens and those trapped in amber.

La nouvelle classification des abeilles (Hymenoptera, Apoidea, Apiformes) ou la chute de l'abeille mellifère (Apis mellifera L.) de son piédestal

The new classification of bees (Hymenoptera, Apoidea, Apiformes) or the fall of the honey bee (Apis mellifera L.) from its pedestal. In this article, the author provides a historical review of classification and phylogeny of bees. Recent work suggests the paraphyly of Melittidae and their basal position in the bee tree, questioning the classification of Michener (1944, 2000) and the classic position of Apidae - including the honey bee Apis mellifera (L.).

The effects of different numerical techniques on the phenetic classification of bees of the Hoplitis complex (Megachilidae)*

SUMMARY The characters of the data matrix of the Hoplitis complex of bees were standardized and similarities computed as distance and correlation coefficients. These similarity matrices were clustered by a variety of techniques. Standardization of character states reduces the isolation of unusual OTU's. The classification based on standardized characters agreed well with one prepared by classical taxonomic methods. Classifications resulting from distance coefficients vary more with the clustering procedure than do classifications based on correlation coefficients. Differences in clustering procedures affect the resulting classification. Differential weighting of stems during clustering caused distinct effects, especially with matrices of distance coefficients and to a lesser extent when Spearman's sums of variables method was used. Only minor differences exist between results of pair-group and variable-group clustering methods. The implications of these findings for numerical taxonomy are discussed.

Structure and Classification of Bees

THE Bulletin of the American Museum of Natural History, 82, 1944, contains a very comprehensive memoir on the above subject, written by C. D. Michener, an assistant curator of the Museum. The method which the author has adopted is to make a detailed study of the morphology of a single species of bee, for example, Anthophora edwardsii, and then to compare numerous other bees with this species. Finally, with these comparisons as a basis, the author gives an account of the interrelationships of the various groups of bees followed by a general scheme of classification. The latter deals with all groupings, from families to genera, represented in America north of Mexico. The memoir is one intended for the specialist on the order Hymenoptera. The anatomical section is concerned with external organs and parts only; the internal organs and musculature being outside the scope of the work. A certain number of new terms are used including the expressions mesosoma and metasoma for the regions commonly referred to as thorax and abdomen respectively. Six families of bees are recognized. The Colletidæ and Halictidæ are the two oldest groups. The next in order of antiquity are considered to be the Andrenidæ and Apidæ followed doubtfully by the Megachilidæ. The last family—the Melittidæ—is too imperfectly known to suggest its position in the series. The largest family is the Apidæ which is held to include a large number of bees usually considered to be outside its limits. The author mentions that certain of these have often been placed in separate families largely on the basis of the presence or absence of the pygidial plate. This character is regarded as being unreliable since it is often lost in very different bees.