Identification Of Insects Research Articles

Morphological and Molecular Identification of Helopeltis Species on Cocoa from Kaliwining Experimental Station, Jember, Indonesia

Severe crop loss of cocoa in Indonesia has long been reported to be caused by Helopeltis. It has been noted that cocoa and Helopeltis have existed in Kaliwining Experimental Station of Indonesian Coffee and Cocoa Research Institute (ICCRI), Jember, Indonesia, for about 100 years, and the species have developed during that time. Our study aimed for examining the Helopeltis species that attacked cocoa in this experimental station both morphologically, morphometrically, and molecularly and to compare the species with the one attacking cocoa in Java and Nusa Tenggara, preserved at the GenBank. Helopeltis was sampled from infested cocoa pods collected representatively from cocoa at the Kaliwining ES and then reared in the Crop Protection Laboratory of ICCRI. Adults of Helopeltis laboratory-reared insects were identified, 30 females 30 males were selected. Weexamined the morphology and morphometry of the samples, and their identifica tion was based on an insect identification key. Morphometry measurement of male and female body parts includes body length, antenna segments, head, eyes, collar, wing, tibia, and femur. In addition, the molecular identification of Helopeltissp. was made through DNA extraction, amplification, and sequence analysis of DNA target regions using the COI part of mitochondrial DNA. External morphological identification, supported by morphometry of the Helopeltis specimen from Kaliwning, the white bands on all femora, body length, and the ratio of the firstantennal segment to the posterior width of the pronotum suggested that the insect belongs to Helopeltis bradyi. Similarly, molecular identification using PCR amplification through specific primers from COI gene sequences confirmed that Helopeltis specimens from Kaliwining are classified as Helopeltis bradyi.

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. The developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

The impact of climate changes on the ranges of invertebrates (Diptera, Molluska) in Tavush province of Armenia

Aim. The work’s aim is to study the species composition of invertebrates (Insecta, Molluska) of the Tavush province of Armenia and changes of their ranges due to climatic conditions.Material and Methods. Our own collections of 2021–2022 from different areas of the Tavush province and the collections of invertebrates of the Scientific Centre of Zoology and Hydroecology served as study material. The collection and identification of dipterous insects and mollusks was carried out according to generally accepted methods. The work was performed at the Scientific Centre of Zoology and Hydroecology The scientific material is stored in the collection fund of the Scientific Centre.Results. As a result of our studies of invertebrate species composition, 28 species of horseflies (Tabanidae) are currently listed for the Tavush region: 27 species of phytophagous gall midges (Cecidomyiidae) and 10 species of mollusks. As a result of warming climate change new changes of locality have been registered for: 6 horsefly species S. (N.) caucasicus, Ch. (H.) f. flavipes, Ch. ludens, T. unifasciatus, T. indrae vappa, T. miki; 4 phytophagous gall midge species A. verbasci, D. tortrix, S. euphorbiae, R. terminalis; For one species of mollusk, H. buchi, there are new localities. All these species have expanded their habitat: in horseflies, changes in the range along vertical zonality are observed.Conclusions. As a result of this research, it was revealed that as a consequences of climate change, the ranges of some species of invertebrates – horseflies, gall midges‐phytophages and mollusks – have expanded compared to the data of previous years. All species in which there is a direct dependence of range change on climatic conditions can be further considered as bioindicators of climate change.

Genetic Identification and Traceability of Insect Meals.

Insects have been proposed as a rich alternative source of protein for the partial or total replacement of fishmeal in aquaculture. For maximum safety and effectiveness of insect meals, control of the quality composition of these products is considered mandatory. The aim of this study was the genetic analysis of the composition of commercially available insect meals at the species level. Commercially available Hermetia illucens, Tenebrio molitor and Musca domestica individuals, as well as nine insect meals produced from these species, were analyzed. The genetic identification of insects at the species level was based on a COI fragment, and analysis of the insect meals' composition was performed with the processes of cloning and colony PCR. Genetic analysis indicated that the commercially available larvae morphologically identified as Musca domestica belonged to the species Muscina stabulans. In the commercially available insect meals, no other animal species was identified beyond the expected one. However, in the insect meal produced for research purposes, fungal growth was detected. The used methodology, herein, allows for the qualitative genetic identification of insect meals and could be included in the methods of traceability of products containing insects and other animal species.

Feasibility of Insect Identification Based on Spectral Fringes Produced by Clear Wings

Due to the growing awareness that insects' diversity and populations are in decline, there is an increased need for monitoring insects. Entomological lidars and photonic sensors can monitor and remotely identify flying insects based on their backscattered signal in terms of oscillations-, polarization-, and spectral content. The backscattered light from insects is predominantly oscillatory and derives from the wings. This part of the signal is also more coherent and co-polarized than the light reflected from the insect's abdomen. Clear membranes can display soap-bubble colors due to thin-film interference, a feature that can be associated with the thickness of the wing. A hyperspectral camera can capture these wing interference patterns with hundreds of spectral bands and accurately identify the wing thickness. Here we investigate whether the spectral fringes can provide complementary information to aid remote species identification. We demonstrate that we can extract wing thickness and modulation depth information from spectral fringes of 87 species of common insect pollinators in Skåne, Sweden. The modulation depth of a fringe provides information related to insect wing thickness homogeneity, wrinkledness, or anti-reflectance features. Our results show that examined species display distinct modulation and wing thickness, and therefore such features can be used to improve the specificity of species identification of photonics sensors.

Insect Identification and Signs of Damage on Organic Monuments from Ancient Egypt-Saqqara

Organic monuments are very susceptible to insect damage. The cumulative effects of this damage can ultimately destroy the organic object. Therefore, it is important to constantly monitor collections for evidence of insect activity. This study aims to iden

Simplified COI barcoding of blow, flesh, and scuttle flies encountered in medicolegal investigations.

Accurate insect identification is critical to the estimation of time of colonization (TOC) and post-mortem interval (PMI) in medicolegal death investigations. DNA testing is advantageous because it enables the identification of immature specimens that may not be identified based on morphology alone. We describe here a simplified DNA barcoding method for identifying relevant species that may be implemented by forensic genetics laboratories. A cytochrome oxidase (COI) fragment is analyzed after PCR amplification with a single primer set. The method is effective for many species commonly encountered in death investigations in the USA: members of blowfly genera Calliphora, Chrysomya, Cochliomyia, Lucilia, and Phormia; members of the flesh fly genera Blaesoxipha, Oxysarcodexia, Ravinia, and Sarcophaga; and the scuttle fly Megaselia scalaris. We tested the method on specimens with verified identifications and used it to build a collection of reference sequences from specimens collected in Harris County, Texas. We show here the correct identification of larvae, pupae, and pupal exuviae from the medicolegal casework.

A study on image processing techniques and deep learning techniques for insect identification

A study on image processing techniques and deep learning techniques for insect identification

Insect species associated with sweet potatoes (Ipomoea batatas L.)

This experiment was conducted in Igboora, southwest Nigeria to identify different insects affecting sweet potatoes in the study area in order to develop suitable control measures for the insect pests. One of the most widely root crops grown in Nigeria is sweet potatoes. It is particularly important in many other countries of the world for consumption. The vine was planted on a plot of 10m by 10m with 6 mounds and replicated 6 times with a spacing of 1m by 1m between each vine on a plot. Data collection on insects that affected the crop commenced five weeks after planting with the aid of a sweep net and hand-picking. All data collected were taken to the laboratory for identification with a hand lens and microscope using insect identification keys and in consultation with the insect collection museum of the Crop Protection and Environmental Biology department, University of Ibadan. The result shows that different insect pests were associated with sweet potatoes at different week intervals, these insects were Orthoptera (Chrotogonus Senegalensis, Dnopherula descampsi) which attacked the leaves, Coleoptera (Lema coelestins, Cheilomenes Lunata, Lunata) also attacked the leaves, Hymenoptera (Crematogaster spp, Pollistes Spilophorus) attacked the vines, while Odynerus spp, componotus spp2, Scapisdepus morginatus were picked on the soil. Farmers and crop scientists should identify insect pests in the field, especially during the cultivation of sweet potatoes, to ascertain the method of control measures to use to improve productivity.

The Most Effective Method to Recognize and Assess Bugs in Food Grains

Abstract: Food grains that are stored are significantly damaged by insects. Additionally, the nutritional and marketability of stored food grains are impacted by insect pestilence. The application of corrective measures necessitates the prompt identification and surveillance of insects in the stored food grains. Insect traps, Berlese funnels, visual lures, pheromone devices, and other methods of visual inspection are a few of the most common approaches that are extensively utilized in commercial grain storage facilities or granaries. Of late, electronic nose, strong stage miniature extraction, warm imaging, acoustic recognition, and so on. have been shown to be effective at identifying insects. The most important aspects to take into account when choosing a method are its capacity for in-situ early detection, monitoring, cost, dependability, and labor requirements. An important concern for reducing losses in bulk storage warehouses is the detection of hidden infestations, whose population may be many times greater than that of free-living insects. This allows for prompt fumigation or grain disposal. This paper surveys a portion of the broadly involved location techniques for early recognition of bugs' pestilential exercises in put away food grains as well as a portion of the clever advances with an accentuation on acoustic strategy, which has a decent business potential.

Development of Web Based Digital Key from Dichotomous Key through Computer Programming: Model Aphid Predator Neuroptera (INSECTA)

Abstract: In fact, the majority of scientific questions asked by biologists could never be formulated or even conceived without reference to some sort of hierarchical arrangement of taxa, making classification the most fundamental of all biological sciences. The majority of biologists might agree that taxonomy's goal should be to identify the components of biodiversity, especially since accurate identification of the organisms is essential for biosecurity and best crop management practices. However, field knowledge in both taxonomy and application is dwindling. As a result, it is critical to identify any pests or other organisms. Since adopting new taxonomic technology in 2011, USDA Animal and Plant Health Inspection Service, Plant Protection and Quarantine (APHIS-PPQ) has created an impressive array of identification tools, many of which are gaining enormous popularity. Indian algorithms have not yet been used to create India's own software or appropriate database for this purpose. We created our own algorithm by using Neuroptera (Insecta) of West Bengal, India as our model organism because it is region-specific database dependent, both for the creation of our own "Digital Key" experiment and for the identification of insects that can control aphids, as bio control agents, we have chosen the predator group of Aphids (Homoptera: Aphididae). As a result, we experimentally created www.lacewingsofwestbengal.in, a web platform, and a "Digital Key" of "Genus" of a chosen Neuropteran group.

Taxonomic identification accuracy from BOLD and GenBank databases using over a thousand insect DNA barcodes from Colombia.

Recent declines of insect populations at high rates have resulted in the need to develop a quick method to determine their diversity and to process massive data for the identification of species of highly diverse groups. A short sequence of DNA from COI is widely used for insect identification by comparing it against sequences of known species. Repositories of sequences are available online with tools that facilitate matching of the sequences of interest to a known individual. However, the performance of these tools can differ. Here we aim to assess the accuracy in identification of insect taxonomic categories from two repositories, BOLD Systems and GenBank. This was done by comparing the sequence matches between the taxonomist identification and the suggested identification from the platforms. We used 1,160 COI sequences representing eight orders of insects from Colombia. After the comparison, we reanalyzed the results from a representative subset of the data from the subfamily Scarabaeinae (Coleoptera). Overall, BOLD systems outperformed GenBank, and the performance of both engines differed by orders and other taxonomic categories (species, genus and family). Higher rates of accurate identification were obtained at family and genus levels. The accuracy was higher in BOLD for the order Coleoptera at family level, for Coleoptera and Lepidoptera at genus and species level. Other orders performed similarly in both repositories. Moreover, the Scarabaeinae subset showed that species were correctly identified only when BOLD match percentage was above 93.4% and a total of 85% of the samples were correctly assigned to a taxonomic category. These results accentuate the great potential of the identification engines to place insects accurately into their respective taxonomic categories based on DNA barcodes and highlight the reliability of BOLD Systems for insect identification in the absence of a large reference database for a highly diverse country.

Performance analysis of AI-based solutions for crop disease identification, detection, and classification

Agriculture is an integral part of human civilization. Apart from providing Food, Agriculture also contributes to the economy. During crop production, crops are susceptible to insects. The identification and treatment of these insects have become a severe issue. The optimal way to reduce this cost is by early detection of the insects and taking appropriate measures to minimize the damage to crop plants. However, the traditional method lacks to examine the diseases and insects' presence in real-time. This study provides deep learning-based solutions for real-time identification and detection of insects in the Soybean crop. Various Transfer-learning models’ performances are examined to explore the proposed solution's feasibility and reliability to find the insect's identification and detection accuracy. The accuracy achieved with the proposed solution is 98.75%, 97%, and 97% using YoloV5, InceptionV3, and CNN, respectively. Among them, the YoloV5 algorithm's performance in the solution is very fast and can run at 53fps, making them fit for real-time detection. Moreover, a dataset of crop insects was collected and labeled by mixing the images collected using different devices. The proposed study helps reduce the producer's workload and is much simpler, and provides better results.

Classifying the unknown: Insect identification with deep hierarchical Bayesian learning

Abstract Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.

Evidence of two mitochondrial lineages and genetic variability in forensically important Lucilia eximia (Diptera: Calliphoridae) in Colombia.

Lucilia eximia (Wiedemann, 1819) (Diptera: Calliphoridae) is a blowfly with medical and forensic importance that shows genetic and color variation, however, these variations have not justified the description of new species. But in forensic entomology an accurate identification of species and subpopulations is crucial. We explored the genetic variation of L. eximia from eight localities, in five natural regions in Colombia using two mitochondrial fragments, including the standard locus for insect identification COI and the Cytb-tRNA-Ser-ND1 region. We found significant differentiation at COI and Cytb-tRNA-Ser-ND1 level, characterizing two lineages and revealing a deep and significant genetic split. High values of FST and genetic distances supported the two lineages. The origin of the divergence of L. eximia remains to discover. Examining whether the lineages have diverse ecological and biological behaviors could be a significant impact on the use of L. eximia in forensic and medical science. Our results could have relevant implications for the use of post-mortem interval estimation based on insect evidence, as well as our sequences improve the database used in DNA-based methods for identifying forensically important flies.

Lightweight Field Insect Recognition and Classification Model Based on Improved Deep Learning under Complex Background

Insect monitoring in the field is an extremely important part of the agricultural production system. Recent advances in computer technology have provided the technical foundation for automatic field insect monitoring. In insect automatic monitoring, insect recognition and classification based on images is one of the most active research areas. Rapid advancements in computer vision technology based on deep learning have provided new ideas for implementing automatic field insect monitoring. Firstly, the field insect images are preprocessed and input to the lightweight algorithm for feature extraction, and the prediction networks of different sizes are output by multiscale feature fusion; then, the joint cross-merge ratio is introduced for automatic identification and classification of field insects. Compared with other algorithms, the simulation results show that the proposed algorithm has higher accuracy, less time consumption, and stronger robustness. It effectively solves the insect accumulation and background interference problems and can identify field insects online in real time.

Rapid detection of insect pest with CRISPR‐Cas13a‐Based Lateral Flow Strip: Using Locusta migratoria manilensis

AbstractThe Clustered Regularly Interspaced Short Palindromic Repeats‐associated (CRISPR‐Cas) system has been used as a powerful diagnostic tool because of its trans‐cleavage ability to accurately recognize and cleave specific nucleic acid targets. However, its application in insect pest detection has rarely been reported. The aim of the current study was to develop a new molecular identification method for insect based on CRISPR‐Cas13a system. In this study, we have developed a highly specific and sensitive detection system that combines polymerase chain reaction, CRISPR‐Cas13a system and lateral flow dipstick (PCR‐Cas13a‐LFD) to rapidly detect the presence of Locusta migratoria manilensis (Orthoptera: Acrididae) DNA and discriminate it from other species, by using material from different stages of development. A pair of specific primers and crRNA were designed based on the Gene DEAD‐box DDX10 mRNA sequence. For visual readout, a FAM‐RNA‐biotin reporter was used for detection. The CRISPR‐Cas13a‐based detection method utilizes the visual identification of L. migratoria manilensis at different developmental stages or with the use of incomplete samples. The test results could be distinguished within 2 h. The sensitivity of the PCR‐Cas13a‐LFD detection system reached 0.1 ng/μL, a 10‐fold increase compared with that of the PCR with electrophoresis. The LFD coupled with CRISPR‐Cas13a (Cas13a‐LFD) is promising for the portable, onsite and fast identification of insect pests. This study provides a reference for the development of a new method for insect identification.

Identification of ectoparasitic insects among domestic goats in Bulgaria.

Ectoparasitic entomoses cause serious economic losses to small-scale farmers. Parasites have both direct and indirect impacts on hosts. Domestic goats are a common target of infestation with ectoparasitic insects. This study aimed to identify the species of ectoparasitic insects in domestic goats in Bulgaria. The study was performed in 34 farms from 29 settlements in 16 regions of Bulgaria. A total of 4599 goats from eight breeds, naturally infested with ectoparasitic insects were included in the study. The goats were inspected with a magnifying glass for the presence of skin changes (alopecia, dandruff, crusts, and nodules), eggs and adult ectoparasites. The detected insects were collected individually with tweezers and preserved in containers with 70% ethanol. Over the study period, 5,651 insects were collected; their species, sex, and developmental stage were identified by their morphological features and biometric measurements. Six species from 5 genera were detected: Linognathus stenopsis Burmeister, 1838; Linognathus africanus Kellog and Paine, 1911; Bovicola caprae Gurlt, 1843; Pulex irritans Linnaeus, 1758; Hippobosca equina Linnaeus, 1758; and Lipoptena cervi Linnaeus, 1758. Linognathus stenopsis were the most prevalent, followed by B. caprae and P. irritans. In detected lice populations, female insects were more numerous; female: male ratios ranged from 2.2 to 7.2 and imagines prevailed over the nymphs. In fleas, male imagines were more numerous than females (1:0.8). The study demonstrated that the species L. stenopsis, L. africanus, B. caprae, and P. irritans were encountered in more than 40% of surveyed farms, situated in 68.75% of regions in Bulgaria. The most intense infestation was by species from the Linognathus genus (907 insects), whereas the highest extensity of infestation was registered for P. irritans (32.3%). This study detected P. irritans as the only flea species.

A Note on the Insects Associated with Stored Onion in Khartoum State

A survey was conducted during 2011-2012 in Khartoum State. The surveyfocused on the identification of insects associated with stored onion. Insects encounteredwere, the fig moth (Ephestia cautella), the dried fruit beetle (Carpophilus hemipterus),the sap beetle (Carpophilus obsolitus), the humpbacked fly (Megaselia scalaris), the antbeetle (Anthicus floralis), the hairy fungus beetle (Typhaea stercorea), the confused flourbeetle (Triboleum confusum), the scavenger fly (Chrysomyza demandata), the dungbeetle (Aphodius lividus), the minute pirate bug (Orius leavigatus), and the rove beetles(Aleochara bipustulata, four Quidius spp. and Lithocharis sp.).

Use of an Arboretum and DNA Barcoding for the Detection and Identification of Leaf-Mining Insects on Alien Woody Plants

Arboreta serve as effective tools for identifying alien insect pests and novel trophic associations. In this study, we used an arboretum in Slovenia to survey woody plants and identify both alien and native leaf miners. The leaves and twigs of 50 woody plant species and their cultivars were examined for characteristic damage. We used an integrative approach that combined identification based on leaf mines and DNA barcoding of the larvae and pupae found in the mines. In total, 62 leaf-mining species were identified, including eight alien species, of which the heliozelid Coptodisca lucifluella (Clemens, 1860) and the agromyzid Cerodontha unisetiorbita Zlobin, 1992 were documented for Slovenia for the first time. Additionally, three presumably native Gracillariidae moths Phyllocnistis labyrinthella (Bjerkander, 1790), P. ramulicola Langmaid & Corley, 2007 and P. saligna (Zeller, 1839) represented the first record for Slovenia. Furthermore, we documented 23 novel-to-science trophic associations, 20 of which involved native insects and alien woody plants, primarily from Asia. This study highlights the importance of arboreta and botanical gardens for the interception of invasive alien insects and the early detection of trophic shifts of native insects to alien plants, which can aid in predicting their potential spread.