DrawWing, a program for numerical description of insect wings

WingAnalogy: a computer vision-based tool for automated insect wing asymmetry and morphometry analysis

WingAnalogy is a computer tool for automated insect wing morphology and asymmetry analysis. It facilitates project management, enabling users to import pairs of wing images obtained from individual insects, such as left and right, fore- and hindwings. WingAnalogy employs image processing and computer vision to segment wing structures and extract cell boundaries, and junctions. It quantifies essential metrics encompassing cell and wing characteristics, including area, length, width, circularity, and centroid positions. It enables users to scale and superimpose wing images utilizing Particle Swarm Optimization (PSO). WingAnalogy computes regression, Normalized Root Mean Square Error (NRMSE), various cell-based parameters, and distances between cell centroids and junctions. The software generates informative visualizations, aiding researchers in comprehending and interpreting asymmetry patterns. WingAnalogy allows for dividing wings into up to five distinct wing cell sets, facilitating localized comparisons. The software excels in report generation, providing detailed asymmetry measurements in PDF, CSV, and TXT formats.

An image based application in Matlab for automated modelling and morphological analysis of insect wings

Despite extensive research on the biomechanics of insect wings over the past years, direct mechanical measurements on sensitive wing specimens remain very challenging. This is especially true for examining delicate museum specimens. This has made the finite element method popular in studies of wing biomechanics. Considering the complexities of insect wings, developing a wing model is usually error-prone and time-consuming. Hence, numerical studies in this area have often accompanied oversimplified models. Here we address this challenge by developing a new tool for fast, precise modelling of insect wings. This application, called WingGram, uses computer vision to detect the boundaries of wings and wing cells from a 2D image. The app can be used to develop wing models that include complex venations, corrugations and camber. WingGram can extract geometric features of the wings, including dimensions of the wing domain and subdomains and the location of vein junctions. Allowing researchers to simply model wings with a variety of forms, shapes and sizes, our application can facilitate studies of insect wing morphology and biomechanics. Being an open-access resource, WingGram has a unique application to expand how scientists, educators, and industry professionals analyse insect wings and similar shell structures in other fields, such as aerospace.

Three-Dimensional Tracking of Multiple Small Insects by a Single Camera

Many systems to monitor insect behavior have been developed recently. Yet most of these can only detect two-dimensional behavior for convenient analysis and exclude other activities, such as jumping or flying. Therefore, the development of a three-dimensional (3D) monitoring system is necessary to investigate the 3D behavior of insects. In such a system, multiple-camera setups are often used to accomplish this purpose. Here, a system with a single camera for tracking small insects in a 3D space is proposed, eliminating the synchronization problems that typically occur when multiple cameras are instead used. With this setup, two other images are obtained via mirrors fixed at other viewing angles. Using the proposed algorithms, the tracking accuracy of five individual drain flies, Clogmia albipunctata (Williston) (Diptera: Psychodidae), flitting about in a spherical arena (78 mm in diameter) is as high as 98.7%, whereas the accuracy of 10 individuals is 96.3%. With this proposed method, the 3D trajectory monitoring experiments of insects can be performed more efficiently.

Disentangling Ethiopian Honey Bee (Apis mellifera) Populations Based on Standard Morphometric and Genetic Analyses

Simple Summary

We conducted this population study of Ethiopian honey bees, using morphometric and genetic methods, to decipher their controversial classification. These honey bees are highly diverse and showed differentiation based on size and genetic information according to prevailing agro-ecological conditions, demonstrating morphological and molecular signatures of local adaptation. The results of both morphometric and genetic analyses suggest that Ethiopian honey bees differ from populations in the neighboring geographic regions and are characterized by extensive gene flow within the country, enhanced by honey bee colony trade. Consequently, future research that includes studying traits of vitality, behavior and colony performance of honey bees in remaining pocket areas of highland agro-ecological zones could contribute to the development of appropriate conservation management.

Abstract

The diversity and local differentiation of honey bees are subjects of broad general interest. In particular, the classification of Ethiopian honey bees has been a subject of debate for decades. Here, we conducted an integrated analysis based on classical morphometrics and a putative nuclear marker (denoted r7-frag) for elevational adaptation to classify and characterize these honey bees. Therefore, 660 worker bees were collected out of 66 colonies from highland, midland and lowland agro-ecological zones (AEZs) and were analyzed in reference to populations from neighboring countries. Multivariate morphometric analyses show that our Ethiopian samples are separate from Apis mellifera scutellata, A. m. jemenitica, A. m. litorea and A. m. monticola, but are closely related to A. m. simensis reference. Linear discriminant analysis showed differentiation according to AEZs in the form of highland, midland and lowland ecotypes. Moreover, size was positively correlated with elevation. Similarly, our Ethiopian samples were differentiated from A. m. monticola and A. m. scutellata based on r7-frag. There was a low tendency towards genetic differentiation between the Ethiopian samples, likely impacted by increased gene flow. However, the differentiation slightly increased with increasing elevational differences, demonstrated by the highland bees that showed higher differentiation from the lowland bees (F_ST = 0.024) compared to the midland bees (F_ST = 0.015). An allelic length polymorphism was detected (denoted as d) within r7-frag, showing a patterned distribution strongly associated with AEZ (X² = 11.84, p < 0.01) and found predominantly in highland and midland bees of some pocket areas. In conclusion, the Ethiopian honey bees represented in this study are characterized by high gene flow that suppresses differentiation.

WingMesh: A Matlab-Based Application for Finite Element Modeling of Insect Wings

The finite element (FE) method is one of the most widely used numerical techniques for the simulation of the mechanical behavior of engineering and biological objects. Although very efficient, the use of the FE method relies on the development of accurate models of the objects under consideration. The development of detailed FE models of often complex-shaped objects, however, can be a time-consuming and error-prone procedure in practice. Hence, many researchers aim to reach a compromise between the simplicity and accuracy of their developed models. In this study, we adapted Distmesh2D, a popular meshing tool, to develop a powerful application for the modeling of geometrically complex objects, such as insect wings. The use of the burning algorithm (BA) in digital image processing (DIP) enabled our method to automatically detect an arbitrary domain and its subdomains in a given image. This algorithm, in combination with the mesh generator Distmesh2D, was used to develop detailed FE models of both planar and out-of-plane (i.e., three-dimensionally corrugated) domains containing discontinuities and consisting of numerous subdomains. To easily implement the method, we developed an application using the Matlab App Designer. This application, called WingMesh, was particularly designed and applied for rapid numerical modeling of complicated insect wings but is also applicable for modeling purposes in the earth, engineering, mathematical, and physical sciences.

Three-dimensional wing structure attenuates aerodynamic efficiency in flapping fly wings

The aerial performance of flying insects ultimately depends on how flapping wings interact with the surrounding air. It has previously been suggested that the wing's three-dimensional camber and corrugation help to stiffen the wing against aerodynamic and inertial loading during flapping motion. Their contribution to aerodynamic force production, however, is under debate. Here, we investigated the potential benefit of three-dimensional wing shape in three different-sized species of flies using models of micro-computed tomography-scanned natural wings and models in which we removed either the wing's camber, corrugation, or both properties. Forces and aerodynamic power requirements during root flapping were derived from three-dimensional computational fluid dynamics modelling. Our data show that three-dimensional camber has no benefit for lift production and attenuates Rankine-Froude flight efficiency by up to approximately 12% compared to a flat wing. Moreover, we did not find evidence for lift-enhancing trapped vortices in corrugation valleys at Reynolds numbers between 137 and 1623. We found, however, that in all tested insect species, aerodynamic pressure distribution during flapping is closely aligned to the wing's venation pattern. Altogether, our study strongly supports the assumption that the wing's three-dimensional structure provides mechanical support against external forces rather than improving lift or saving energetic costs associated with active wing flapping.

Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks

Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached $CDATA[$CDATA[$>$$92% accuracy on one data set (884 face images of 11 families of Diptera, all specimens representing unique species), and $CDATA[$CDATA[$>$$96% accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90% of specimens belonging to unique species). For the second task, our approach outperformed a leading taxonomic expert on one data set (339 images of three species of the Coleoptera genus Oxythyrea; 97% accuracy), and both humans and traditional automated identification systems on another data set (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training data sets are small and computational budgets limited. We conclude by briefly discussing some promising CNN-based research directions in morphological systematics opened up by the success of these techniques in providing accurate diagnostic tools.

Body mass but not wing size or symmetry correlates with life span of honey bee drones

In social insects such as the honey bee, the quality of drones at the time of their emergence can affect their maintenance in the colony until maturity. Body mass, wing size and wing asymmetry of emerging honey bee drones were measured and correlated with their life span in the colony and compared between individuals reaching maturity or not. The life span of drones differed among colonies in which they were maintained after emergence but not between colonies in which they were reared. More drones heavier at emergence reached sexual maturity at 15 days and had a longer life span compared with light-weight drones of lower mass. The size and symmetry of drone forewings was not correlated with their life span. Our results suggest that body mass at emergence is a good predictor of drone survival in the colony.

PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network

Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown Planthopper (BPH) causes high levels of economic loss in Malaysia. Identification of BPH presence and monitoring of its abundance has been conducted manually by experts and is time-consuming, fatiguing and tedious. Automated detection of BPH has been proposed by many studies to overcome human fallibility. However, all studies regarding automated recognition of BPH are investigated based on intact specimen although most of the specimens are imperfect, with missing parts have distorted shapes. The automated recognition of an imperfect insect image is more difficult than recognition of the intact specimen. This study proposes an automated, deep-learning-based detection pipeline, PENYEK, to identify BPH pest in images taken from a readily available sticky pad, constructed by clipping plastic sheets onto steel plates and spraying with glue. This study explores the effectiveness of a convolutional neural network (CNN) architecture, VGG16, in classifying insects as BPH or benign based on grayscale images constructed from Euclidean Distance Maps (EDM). The pipeline identified imperfect images of BPH with an accuracy of 95% using deep-learning’s hyperparameters: softmax, a mini-batch of 30 and an initial learning rate of 0.0001.

Hind wing variation in Leptura annularis complex among European and Asiatic populations (Coleoptera, Cerambycidae)

The ability to quantify morphological variation is essential for understanding the processes of species diversification. The geometric morphometrics approach allows reliable description of variation in animals, including insects. Here, this method was used to quantify the morphological variation among European and Asiatic populations of Lepturaannularis Fabricius, 1801 and its closely related species L.mimica Bates, 1884, endemic for Japan and Sakhalin islands. Since the taxonomic status of these two taxa is differently interpreted by taxonomists, they are collectively called “Lepturaannularis complex” in this paper. The analysis was based on the measurements of hind wings of 269 specimens from six populations from Europe and Asia. The level of morphological divergence between most of continental European and Asiatic populations was relatively small and proportional to the geographic distance between them. However, distinct morphotype was detected in Sakhalin Is. and Japan. These data confirm the morphological divergence of the endemic L.mimica species. Obtained results highlight the potential of the geometric morphometric method in studying morphological variation in beetles.

Semi-automated quantitative Drosophila wings measurements

BACKGROUND

Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data.

RESULTS

We have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system.

CONCLUSION

Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy.

A tool for developing an automatic insect identification system based on wing outlines

For some insect groups, wing outline is an important character for species identification. We have constructed a program as the integral part of an automated system to identify insects based on wing outlines (DAIIS). This program includes two main functions: (1) outline digitization and Elliptic Fourier transformation and (2) classifier model training by pattern recognition of support vector machines and model validation. To demonstrate the utility of this program, a sample of 120 owlflies (Neuroptera: Ascalaphidae) was split into training and validation sets. After training, the sample was sorted into seven species using this tool. In five repeated experiments, the mean accuracy for identification of each species ranged from 90% to 98%. The accuracy increased to 99% when the samples were first divided into two groups based on features of their compound eyes. DAIIS can therefore be a useful tool for developing a system of automated insect identification.

Patriline Differences Reveal Genetic Influence on Forewing Size and Shape in a Yellowjacket Wasp (Hymenoptera: Vespidae: Vespula flavopilosa Jacobson, 1978)

The wing venation is frequently used as a morphological marker to distinguish biological groups among insects. With geometric morphometrics, minute shape differences can be detected between closely related species or populations, making this technique useful for taxonomy. However, the direct influence of genetic differences on wing morphology has not been explored within colonies of social insects. Here, we show that the father's genotype has a direct effect on wing morphology in colonies of social wasps. Using geometric morphometrics on the venation pattern, we found significant differences in wing size and shape between patrilines of yellowjackets, taking allometry and measurement error into account. The genetic influence on wing size accounted for a small part of the overall size variation, but venation shape was highly structured by the differences between patrilines. Overall, our results showed a strong genetic influence on wing morphology likely acting at multiple levels of venation pattern development. This confirmed the pertinence of this marker for taxonomic purposes and suggests this phenotype as a potentially useful marker for phylogenies. This also raises doubts about the strength of selective pressures on this phenotype, which highlights the need to understand better the role of wing venation shape in insect flight.

Parasite pressures on feral honey bees (Apis mellifera sp.)

Feral honey bee populations have been reported to be in decline due to the spread of Varroa destructor, an ectoparasitic mite that when left uncontrolled leads to virus build-up and colony death. While pests and diseases are known causes of large-scale managed honey bee colony losses, no studies to date have considered the wider pathogen burden in feral colonies, primarily due to the difficulty in locating and sampling colonies, which often nest in inaccessible locations such as church spires and tree tops. In addition, little is known about the provenance of feral colonies and whether they represent a reservoir of Varroa tolerant material that could be used in apiculture. Samples of forager bees were collected from paired feral and managed honey bee colonies and screened for the presence of ten honey bee pathogens and pests using qPCR. Prevalence and quantity was similar between the two groups for the majority of pathogens, however feral honey bees contained a significantly higher level of deformed wing virus than managed honey bee colonies. An assessment of the honey bee race was completed for each colony using three measures of wing venation. There were no apparent differences in wing morphometry between feral and managed colonies, suggesting feral colonies could simply be escapees from the managed population. Interestingly, managed honey bee colonies not treated for Varroa showed similar, potentially lethal levels of deformed wing virus to that of feral colonies. The potential for such findings to explain the large fall in the feral population and the wider context of the importance of feral colonies as potential pathogen reservoirs is discussed.

Veins improve fracture toughness of insect wings

During the lifetime of a flying insect, its wings are subjected to mechanical forces and deformations for millions of cycles. Defects in the micrometre thin membranes or veins may reduce the insect’s flight performance. How do insects prevent crack related material failure in their wings and what role does the characteristic vein pattern play? Fracture toughness is a parameter, which characterises a material’s resistance to crack propagation. Our results show that, compared to other body parts, the hind wing membrane of the migratory locust S. gregaria itself is not exceptionally tough (1.04±0.25 MPa√m). However, the cross veins increase the wing’s toughness by 50% by acting as barriers to crack propagation. Using fracture mechanics, we show that the morphological spacing of most wing veins matches the critical crack length of the material (1132 µm). This finding directly demonstrates how the biomechanical properties and the morphology of locust wings are functionally correlated in locusts, providing a mechanically ‘optimal’ solution with high toughness and low weight. The vein pattern found in insect wings thus might inspire the design of more durable and lightweight artificial ‘venous’ wings for micro-air-vehicles. Using the vein spacing as indicator, our approach might also provide a basis to estimate the wing properties of endangered or extinct insect species.

Parametric structural modeling of insect wings

Insects produce thrust and lift forces via coupled fluid-structure interactions that bend and twist their compliant wings during flapping cycles. Insight into this fluid-structure interaction is achieved with numerical modeling techniques such as coupled finite element analysis and computational fluid dynamics, but these methods require accurate and validated structural models of insect wings. Structural models of insect wings depend principally on the shape, dimensions and material properties of the veins and membrane cells. This paper describes a method for parametric modeling of wing geometry using digital images and demonstrates the use of the geometric models in constructing three-dimensional finite element (FE) models and simple reduced-order models. The FE models are more complete and accurate than previously reported models since they accurately represent the topology of the vein network, as well as the shape and dimensions of the veins and membrane cells. The methods are demonstrated by developing a parametric structural model of a cicada forewing.

Thrips (Thysanoptera) identification using artificial neural networks

We studied the use of a supervised artificial neural network (ANN) model for semi-automated identification of 18 common European species of Thysanoptera from four genera: Aeolothrips Haliday (Aeolothripidae), Chirothrips Haliday, Dendrothrips Uzel, and Limothrips Haliday (all Thripidae). As input data, we entered 17 continuous morphometric and two qualitative two-state characters measured or determined on different parts of the thrips body (head, pronotum, forewing and ovipositor) and the sex. Our experimental data set included 498 thrips specimens. A relatively simple ANN architecture (multilayer perceptrons with a single hidden layer) enabled a 97% correct simultaneous identification of both males and females of all the 18 species in an independent test. This high reliability of classification is promising for a wider application of ANN in the practice of Thysanoptera identification.