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1
Dimililer, Kamil, and Salah Zarrouk. "ICSPI: Intelligent Classification System of Pest Insects Based on Image Processing and Neural Arbitration." Applied Engineering in Agriculture 33, no. 4 (2017): 453–60. http://dx.doi.org/10.13031/aea.12161.
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Abstract. Detection of insects in agricultural fields is a significant challenge. Minimizing the use of pesticides is necessary for healthier crops and consumers. Therefore, effective and intelligent systems should be designed to fight infestations. This article aims to develop an intelligent insect classification system that would be capable of detecting and classifying the eight insects most commonly found in paddy fields. The developed system comprises two principal stages. In the first stage, the images of the insects are processed using different image processing techniques in order to detect their geometric shapes. The next stage is the classification phase, where a backpropagation neural network is trained and then tested on processed images. Experimentally, the system was tested on different insect images and the results show high efficiency and a classification rate of 93.5%. Keywords: Backpropagation neural networks, Classification, Geometric shapes, Intelligent systems, Pattern averaging, Pest control.
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Xia, Denan, Peng Chen, Bing Wang, Jun Zhang, and Chengjun Xie. "Insect Detection and Classification Based on an Improved Convolutional Neural Network." Sensors 18, no. 12 (November 27, 2018): 4169. http://dx.doi.org/10.3390/s18124169.
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Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.
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Bezliudnyi, Y. S., V. M. Shymkovysh, and A. Yu Doroshenko. "Convolutional neural network model and software for classification of typical pests." PROBLEMS IN PROGRAMMING, no. 4 (December 2021): 095–102. http://dx.doi.org/10.15407/pp2021.04.095.
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A model of a convolutional neural network, a dataset for neural network training, and a software tool for the classification of typical insect pests have been developed, which allows recognizing the class of insect pests from an image. The structure of the neural network model was optimized to improve the classification results. In addition, the user interface, authentication, and authorization, data personalization, the presence of user roles and the appropriate distribution of functionality by role, the ability to view statistics on classified insects in a certain period of time were developed. Functional testing of the developed software application on a heterogeneous set of images of insects of 20 different classes was performed.
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Hobbs, S. E., and G. Hodges. "An optical method for automatic classification and recording of a suction trap catch." Bulletin of Entomological Research 83, no. 1 (March 1993): 47–51. http://dx.doi.org/10.1017/s0007485300041766.
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AbstractA simple optical method for automatic recording and classification of a suction trap catch is described. The insects are illuminated against a dark background as they pass through a detection volume, and the amount of scattered light is used to measure insect size. The design centres on the detection volume, which is a volume through which the insects are made to pass, and within which they may be detected. The design is approached in four stages: 1. Delivery of insects to the detection volume. 2. Illumination of the detection volume. 3. Collection and detection of scattered light. 4. Signal analysis. The analysis could also be applied to related techniques. Results with a prototype demonstrate that classification into broad size categories is straight-forward (e.g. approximately three classes spanning body lengths of 2–7 mm), despite uncertainties of insect reflectivity, aspect and trajectory. Applications of the method are discussed, along with a brief mention of alternative techniques.
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Ong, Song-Quan, and Suhaila Ab Hamid. "Next generation insect taxonomic classification by comparing different deep learning algorithms." PLOS ONE 17, no. 12 (December 30, 2022): e0279094. http://dx.doi.org/10.1371/journal.pone.0279094.
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Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects—order, family, and genus—and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification.
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Zettler, Jennifer A., Scott C. Mateer, Melanie Link-Pérez, Jennifer Brofft Bailey, Geneva DeMars, and Traci Ness. "To Key or Not to Key: A New Key to Simplify & Improve the Accuracy of Insect Identification." American Biology Teacher 78, no. 8 (October 1, 2016): 626–33. http://dx.doi.org/10.1525/abt.2016.78.8.626.
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Insects have extraordinary species richness: over a million species have been identified, and even more await discovery and classification. Given their abundance and diversity, insects are excellent teaching tools for science classrooms. However, accurate insect identification can be especially challenging for beginning students. Accordingly, we have developed a dichotomous key that both precollege and university instructors and students can use efficiently to correctly identify 18 taxonomic orders of insects. Our key was developed to target insects most commonly encountered throughout the coastal southeastern United States, but it can easily be adapted to other regions. This key is novel in that it incorporates not only adult insects but also their immature stages. In addition, we included insects that are likely to be collected in all seasons, facilitating implementation in the classroom throughout the academic year.
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Ogilvie, Brian W. "Attending to insects: Francis Willughby and John Ray." Notes and Records of the Royal Society 66, no. 4 (October 10, 2012): 357–72. http://dx.doi.org/10.1098/rsnr.2012.0051.
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Francis Willughby and John Ray were at the forefront of the natural history of insects in the second half of the seventeenth century. Willughby in particular had a deep interest in insects' metamorphosis, behaviour and diversity, an interest that he passed on to his friend and mentor Ray. By examining Willughby's contributions to John Wilkins's Essay towards a Real Character (1668) and Ray's Methodus insectorum (1705) and Historia insectorum (1710), which contained substantial material from Willughby's manuscript history of insects, one may reconstruct how the two naturalists studied insects, their innovative use of metamorphosis in insect classification, and the sheer diversity of insect forms that they described on the basis of their own collections and those of London and Oxford virtuosi. Imperfect as it was, Historia insectorum was recognized by contemporaries as a significant contribution to the emerging field of entomology.
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Li, Jiangtao, Huiling Zhou, Digvir S. Jayas, and Qingxuan Jia. "Construction of a Dataset of Stored-grain Insects Images for Intelligent Monitoring." Applied Engineering in Agriculture 35, no. 4 (2019): 647–55. http://dx.doi.org/10.13031/aea.13122.
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Abstract. We constructed an image dataset of adults of 10 common species of stored-grain insects. This dataset is very significant for the research on image recognition algorithms for stored-grain insects, in order to implement intelligent monitoring for insects in warehouses. Images were collected using two kinds of devices: a developed automatic insect image acquisition device that can be fitted with different traps in warehouses and the commonly used smart phones. The images in this dataset contained 10 species of insect instances with various sizes, poses, and orientations. Each image corresponded to an xml file to store the species names and bounding boxes of insect instances in images. In total, 3,757 images were collected, and 159,238 insect instances were marked. The fine-grained classification algorithm based on Bilinear CNN and the object detection algorithms based on Faster R-CNN were adopted as baseline algorithms for benchmark experiments. Experiment results indicated that this dataset could support the research of image recognition algorithms of stored-grain insects, but it is a challenging task to detect small, adhesive and overlapped insect instances in images of this dataset. Currently, this dataset can be accessed at rgbinsect.cn. Keywords: Bilinear CNN, Faster R-CNN, Image dataset, Image recognition, Monitoring, Stored-grain Insects.
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Huerta, Ramón, Thomas Nowotny, Marta García-Sanchez, H. D. I. Abarbanel, and M. I. Rabinovich. "Learning Classification in the Olfactory System of Insects." Neural Computation 16, no. 8 (August 1, 2004): 1601–40. http://dx.doi.org/10.1162/089976604774201613.
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We propose a theoretical framework for odor classification in the olfactory system of insects. The classification task is accomplished in two steps. The first is a transformation from the antennal lobe to the intrinsic Kenyon cells in the mushroom body. This transformation into a higher-dimensional space is an injective function and can be implemented without any type of learning at the synaptic connections. In the second step, the encoded odors in the intrinsic Kenyon cells are linearly classified in the mushroom body lobes. The neurons that perform this linear classification are equivalent to hyperplanes whose connections are tuned by local Hebbian learning and by competition due to mutual inhibition. We calculate the range of values of activity and size fo the network required to achieve efficient classification within this scheme in insect olfaction. We are able to demonstrate that biologically plausible control mechanisms can accomplish efficient classification of odors.
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Bjerge, Kim, Jakob Bonde Nielsen, Martin Videbæk Sepstrup, Flemming Helsing-Nielsen, and Toke Thomas Høye. "An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning." Sensors 21, no. 2 (January 6, 2021): 343. http://dx.doi.org/10.3390/s21020343.
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Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
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Noda, Juan J., Carlos M. Travieso-González, David Sánchez-Rodríguez, and Jesús B. Alonso-Hernández. "Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion." Applied Sciences 9, no. 19 (October 1, 2019): 4097. http://dx.doi.org/10.3390/app9194097.
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This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of acoustic signals of insects using Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC). These two sets of coefficients are evaluated individually as has been done in previous studies and have been compared with the fusion proposed in this work, showing an outstanding increase in identification and classification at species level reaching a success rate of 98.07% on 343 insect species.
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Gavrilov-Zimin, I. A. "Ontogenesis, morphology and higher classification of archaeococcids (Homoptera: Coccinea: Orthezioidea)." Zoosystematica Rossica, Supplementum 2 (May 31, 2018): 1–260. http://dx.doi.org/10.31610/zsr/2018.supl.2.1.
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The monograph summarizes original research data and published literature data on reproduction, life cycles, individual development and morphology of scale insects of the superfamily Orthezioidea (archaeococcids). The superfamily system is accepted mainly in its traditional concept, i.e. with four well-defined families: Margarodidae s. l., Ortheziidae, Carayonemidae, and Phenacoleachiidae. The tribe Matsucoccini (Margarodidae s. l.: Xylococcinae s. l.) is considered as a most archaic group of scale insects according to morphological, reproductive and ontogenetic characters. A complicated ontogenesis with an alternation of movable/immovable instars and with arostrate imago of both sexes (as in Matsucoccus Cockerell, 1909 and many other Margarodidae s. l.) is presumed to be initial in scale insect evolution and such ontogenesis is supposed to be an apomorphy of suborder Coccinea. Distribution of different variants of ovoviviparity/viviparity amongst scale insect families is overviewed. It is demonstrated that the evolution of scale insects shows multiple cyclic conversions of oviparous reproduction pattern to ovoviviparous/viviparous ones with the appearance of new and new peculiar adaptations to eggs protection; the most ancient scale insects (Matsucoccini and their ancestor) were probably facultatively ovoviviparous, whereas the origin of the whole neococcid phylogenetic line (Coccoidea s. s.) was probably connected with obligate complete ovoviviparity, which also appeared in some “derived” archaeococcids of the tribe Iceryini (Margarodidae s. l.), in the families Phenacoleachiidae and Carayonemidae. New taxonomic additions and changes in generic composition of some tribes are provided for the family Margarodidae s. l., in its subfamilies Monophlebinae and Callipappinae s. l. The tribe Labioproctini tr. nov. (Monophlebinae) is erected for six genera possessing peculiar quadrilocular wax pores: Aspidoproctus Newstead, 1901, Hemaspidoproctus Morrison, 1927, Labioproctus Green, 1922, Lecaniodrosicha Takahashi, 1930, Misracoccus Rao, 1950, and Walkeriana Signoret, 1876. The presence of quadrilocular pores are considered as a synapomorphic character of the Labioproctini tr. nov. and Ortheziidae. Disputable taxonomic position of Xenococcidae Tang, 1992 is discussed and this family is also placed in Orthezioidea. New genera and species are described and illustrated, based mainly on material collected in the Oriental region: Eremostoma klugei gen. et sp. nov., Crambostoma largecicatricosum gen. et sp. nov. (both in Callipappinae s. l.: Coelostomidiini s. l.), Buchnericoccus reynei sp. nov., Monophlebus neglectus sp. nov. (both in Monophlebinae: Monophlebini), Crypticerya ovivivipara sp. nov., Icerya oculicicatricata sp. nov., I. siamensis sp. nov. (all three in Monophlebinae: Iceryini).
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Mellado-Carretero, J., N. García-Gutiérrez, M. Ferrando, C. Güell, D. García-Gonzalo, and S. De Lamo-Castellví. "Rapid discrimination and classification of edible insect powders using ATR-FTIR spectroscopy combined with multivariate analysis." Journal of Insects as Food and Feed 6, no. 2 (April 8, 2020): 141–48. http://dx.doi.org/10.3920/jiff2019.0032.
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Insects are being proposed as an alternative way to ensure world’s food and feed security. Methods to determine edible insect powder’s origin and species will be needed for quality control purposes. Infrared spectroscopy has been extensively used in rapid chemical fingerprinting of food products. The present research explores a new approach to discriminate and classify commercial edible insect powders using attenuated total reflectance mid-infrared spectroscopy combined with multivariate analysis. Infrared spectra of seven commercial edible insect powders from different species (Tenebrio molitor, Alphitobius diaperinus, Gryllodes sigillatus, Acheta domesticus and Locusta migratoria) and origins (the Netherlands and New Zealand) were collected to build up soft independent modelling of class analogy (SIMCA) models. SIMCA models clearly discriminated insects by their species and origin linking their differences to lipids and chitin. SIMCA models performance was tested using five spectra of each class not used to build up the training set. 100% correct predictions were obtained for all the samples analysed with the exception of one sample of Alphitobius diaperinus. Infrared spectroscopy coupled to multivariate analysis provided a powerful method for the assurance of insect powder’s authenticity.
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Scholtz, C. H. "The Higher Classification of Southern African Insects." African Entomology 24, no. 2 (September 2016): 545–55. http://dx.doi.org/10.4001/003.024.0545.
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Edde, Peter A. "Principal Insects Affecting Tobacco Plants in the Field." Beiträge zur Tabakforschung International/Contributions to Tobacco Research 28, no. 3 (October 1, 2018): 117–65. http://dx.doi.org/10.2478/cttr-2018-0013.
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SummaryTobacco, Nicotiana tabacum, is a high-value crop grown in many temperate and tropical countries of the world. Several insects attack tobacco throughout the season, from transplant production, growth in the field, during storage, and in the marketed product. This review focuses on economically important insects of the seedling tobacco or the growing crop in major tobacco-producing regions of the world. The species covered herein are tobacco aphid, black cutworm, tobacco budworm, tobacco hornworm, tobacco flea beetle, thrips, Japanese beetle, and tobacco wireworm. The occurrence and economic importance of these insects vary from region to region.For each insect discussed, the following information is provided: the scientific name and taxonomic position of the insect; its geographical distribution; the stage that causes the damage and plant hosts; a brief discussion on classification and description of the species; a summary of the biology and ecology; details regarding pest management, which include scouting-/monitoring methods, action threshold, cultural (non-chemical) methods, natural enemies, and chemical control. In addition, a concluding paragraph is presented on insect pest management for tobacco.
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Marković, Dušan, Dejan Vujičić, Snežana Tanasković, Borislav Đorđević, Siniša Ranđić, and Zoran Stamenković. "Prediction of Pest Insect Appearance Using Sensors and Machine Learning." Sensors 21, no. 14 (July 16, 2021): 4846. http://dx.doi.org/10.3390/s21144846.
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The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.
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Riu, Jordi, Alba Vega, Ricard Boqué, and Barbara Giussani. "Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy." Foods 11, no. 21 (November 5, 2022): 3524. http://dx.doi.org/10.3390/foods11213524.
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Insects have been a food source for humans for millennia, and they are actively consumed in various parts of the world. This paper aims to ascertain the feasibility of portable near-infrared (NIR) spectroscopy as a reliable and fast candidate for the classification of insect powder samples and the prediction of their major components. Commercially-available insect powder samples were analyzed using two miniaturized NIR instruments. The samples were analyzed as they are and after grinding, to study the effect of the granulometry on the spectroscopic analyses. A homemade sample holder was designed and optimized for making reliable spectroscopic measurements. Classification was then performed using three classification strategies, and partial least squares (PLS) regression was used to predict the macronutrients. The results obtained confirmed that both spectroscopic sensors were able to classify insect powder samples and predict macronutrients with an adequate detection limit.
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von der Emde, Gerhard, and Hans-Ulrich Schnitzler. "Classification of insects by echolocating greater horseshoe bats." Journal of Comparative Physiology A 167, no. 3 (August 1990): 423–30. http://dx.doi.org/10.1007/bf00192577.
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Toar, Wisje Lusia, Laurentius Rumokoy, Ivonne Maria Untu, and Geertruida Assa. "Insect Crude Thoraxial Antigen-G Extracted from Apis mellifera to Enhance Serum Immunoglobulin of Goats: An Entomology Contribution in Animal Science." ANIMAL PRODUCTION 20, no. 2 (July 30, 2019): 133. http://dx.doi.org/10.20884/1.jap.2018.20.2.608.
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This research was conducted to evaluate the influence of insect crude thoraxial antigen-G (CTA) extracted from Apis mellifera L. (Hymenoptera: Apidae) to enhance goat’s serum immunoglobulin level. The first part of this study was the determination of insect CTA proportion level. The insects were collected from four different places: Tomohon, Minahasa, North-Minahasa and Manado areas. The second part of the study was the application of A. mellifera CTA substance on serum immunoglobulin level classification. In this part, twelve young goats handled with traditional maintenance. The animals experiment were divided in two groups: control group and the other treated with 100 µg CTA extract. The proportion of serum immunoglobulins level of goats was detected at 14th days after immunization with insects CTA extract, and compared with the animals immunoglobulin levels at the starting day of treatment. The data of CTA extract proportion level of the insects collected were subjected to statistically analysis using the general linear model (GLM) procedure of SPSS 22. Concerning the classification level of the animal treated with CTA was statistically analyzed according to Mann-Whitney test. The results showed that the proportion level of thoraxial antigens-G of A. mellifera from all areas observed were not significant different (P>0.05). This crude thoraxial antigens-G of this insect were able to increase serum antibody level of the experiment animal after 14 days of immunization. The immunoglobulin level qualification of animals in treated group were significant higher (P<0.05) than in control group. We concluded that the CTA extract of the Apis mellifera could be empowered to improve the young goat immunity against the pathogenic microbes in their environment.
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Florença, Sofia G., Paula M. R. Correia, Cristina A. Costa, and Raquel P. F. Guiné. "Edible Insects: Preliminary Study about Perceptions, Attitudes, and Knowledge on a Sample of Portuguese Citizens." Foods 10, no. 4 (March 26, 2021): 709. http://dx.doi.org/10.3390/foods10040709.
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This study investigated the knowledge, attitudes, consumption habits, and degree of acceptability of edible insects (EI) or derived products among Portuguese consumers. This work consisted of a questionnaire survey, undertaken on a sample of 213 participants. For the treatment of data, basic descriptive statistics were used, complemented with chi-square tests to assess some associations between categorical variables. Moreover, a tree classification analysis was carried out using a classification and regression tree (CRT) algorithm with cross-validation. The results indicated that people tend to have correct perceptions about the sustainability issues associated with the use of insects as alternative sources of protein; however, the level of knowledge and overall perception about their nutritive value is low. Regarding the consumption of EI, it was found that only a small part of the participants had already eaten them, doing it mostly abroad, by self-initiative, in a restaurant or at a party or event. Additionally, it was found that the reluctance to consume insects is higher if they are whole, but when they are transformed into ingredients used in food formulations, the level of acceptance increases. Furthermore, men have shown to have a better perception about EI, be more informed about sustainability, and have a higher level of acceptability when compared to women. As a final conclusion, it was observed that the Portuguese still show some resistance to adhere to the use of insects as replacements for meat products, but the market of insect based products can be a good alternative to overpass the neophobia associated with this type of food.
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Moreno-Lucio, Mireya, Celina Lizeth Castañeda-Miranda, Gustavo Espinoza-García, Carlos Alberto Olvera-Olvera, Luis F. Luque-Vega, Antonio Del Rio-De Santiago, Héctor A. Guerrero-Osuna, Ma del Rosario Martínez-Blanco, and Luis Octavio Solís-Sánchez. "Extraction of Pest Insect Characteristics Present in a Mirasol Pepper (Capsicum annuum L.) Crop by Digital Image Processing." Applied Sciences 11, no. 23 (November 25, 2021): 11166. http://dx.doi.org/10.3390/app112311166.
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One of the main problems in crops is the presence of pests. Traditionally, sticky yellow traps are used to detect pest insects, and they are then analyzed by a specialist to identify the pest insects present in the crop. To facilitate the identification, classification, and counting of these insects, it is possible to use digital image processing (DIP). This study aims to demonstrate that DIP is useful for extracting invariant characteristics of psyllids (Bactericera cockerelli), thrips (Thrips tabaci), whiteflies (Bemisia tabaci), potato flea beetles (Epitrix cucumeris), pepper weevils (Anthonomus eugenii), and aphids (Myzus persicae). The characteristics (e.g., area, eccentricity, and solidity) help classify insects. DIP includes a first stage that consists of improving the image by changing the levels of color intensity, applying morphological filters, and detecting objects of interest, and a second stage that consists of applying a transformation of invariant scales to extract characteristics of insects, independently of size or orientation. The results were compared with the data obtained from an entomologist, reaching up to 90% precision for the classification of these insects.
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GULLAN, P. J., and L. G. COOK. "Phylogeny and higher classification of the scale insects (Hemiptera: Sternorrhyncha: Coccoidea)*." Zootaxa 1668, no. 1 (December 21, 2007): 413–25. http://dx.doi.org/10.11646/zootaxa.1668.1.22.
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The superfamily Coccoidea contains nearly 8000 species of plant-feeding hemipterans comprising up to 32 families divided traditionally into two informal groups, the archaeococcoids and the neococcoids. The neococcoids form a monophyletic group supported by both morphological and genetic data. In contrast, the monophyly of the archaeococcoids is uncertain and the higher level ranks within it have been controversial, particularly since the late Professor Jan Koteja introduced his multi-family classification for scale insects in 1974. Recent phylogenetic studies using molecular and morphological data support the recognition of up to 15 extant families of archaeococcoids, including 11 families for the former Margarodidae sensu lato, vindicating Koteja’s views. Archaeococcoids are represented better in the fossil record than neococcoids, and have an adequate record through the Tertiary and Cretaceous but almost no putative coccoid fossils are known from earlier. In contrast, the sister group of the scale insects (Aphidoidea) has a more informative Jurassic and Triassic record. Relationships among most scale insect families are unresolved in phylogenetic trees based on nuclear DNA sequences, and most nodes in trees based on morphological data, including those from adult males, are poorly supported. Within the neococcoids, the Eriococcidae is not monophyletic and the monophyly of the Coccidae and Diaspididae may be compromised by the current family-level recognition of a few species-poor autapomorphic groups.
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Wen, Chenglu, Daniel E. Guyer, and Wei Li. "Local feature-based identification and classification for orchard insects." Biosystems Engineering 104, no. 3 (November 2009): 299–307. http://dx.doi.org/10.1016/j.biosystemseng.2009.07.002.
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Zhang, Xianrong, and Gang Chen. "An Automatic Insect Recognition Algorithm in Complex Background Based on Convolution Neural Network." Traitement du Signal 37, no. 5 (November 25, 2020): 793–98. http://dx.doi.org/10.18280/ts.370511.
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The existing insect recognition methods mostly segment the target region by traditional classification technology, failing to achieve a high accuracy in complex background. To solve the problem, this paper introduces the morphology-based edgeless active contour strategy to segment insects in complex background. The strategy integrates the morphological operation of gray image, and detects insect contours by narrow-band fast method. To enhance the background diversity of new samples, the authors improved the synthetic minority over-sampling technique (SMOTE) algorithm into a variable weight edge enhancement algorithm. Based on the SMOTE algorithm, the proposed algorithm increases the weight of the edge area as adjacent images are superimposed into a new image, making the background of the new image more complex. Finally, the proposed method was coupled with DenseNet-121 to recognize insects in images with complex background. The results show that the accuracy of the network was nearly 10% higher on the balanced set than on the unbalanced set, suggesting that our method is feasible and accurate.
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ENUSHCHENKO, ILYA V., and АNDREY O. FROLOV. "Revision of existing classification of fossil insect feeding traces and description of new ichnotaxa from Middle Jurassic sediments of Eastern Siberia (Russia)." Zootaxa 4758, no. 2 (March 30, 2020): 347–59. http://dx.doi.org/10.11646/zootaxa.4758.2.8.
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This paper revises the existing system of formal classification by Vialov and Vasilenko for fossil plants involving insect feeding and oviposition. The classification of these fossil traces has been amended and supplemented in accordance with the requirements of the International Code of Zoological Nomenclature. The following nomenclatural combinations are made: Insectophagichnata (Vialov) Enushchenko and Frolov, comb. et stat. (classis) nov., Phagolignichnidina (Vialov) Enushchenko and Frolov, comb. et stat. (subordo) nov., Phagophytichnidina (Vialov) Enushchenko and Frolov, comb. et stat. (subordo) nov. Fossil traces of these interactions were found and illustrated for Mesozoic insects and leafes of Ginkgo tapkensis from Middle Jurassic sediments of the Irkutsk Coal Basin in Eastern Siberia, Russia. The examined traces consist of ovipositions (traces of insect egg laying), galls (traces of insect caused teratologies) and epidermal punctures (traces of piercing and sucking). The following ichnotaxa are described and illustrated: Paleoovidinae Enushchenko and А. Frolov, ichnosubfam. nov., Paleoexoovoidinae Enushchenko and Frolov, ichnosubfam. nov., Sugophytichninae Enushchenko and А. Frolov, ichnosubfam. nov., Sugophytichnida pertusura Enushchenko and Frolov, ichnogen. et ichnospec. nov., Paleoovidus vasilenkoi Enushchenko and А. Frolov, ichnospec. nov., Paleoexoovoida ovoidea Enushchenko and А. Frolov, ichnogen. et ichnosp. nov., Paleogallus vialovi Enushchenko and Frolov, ichnospec. nov. Punctures of the leaf epidermis probably belong Mesozoic cicadas of the Palaeontini, which dominate the adjacent strata of the studied location. The extremely low occurrence of interaction between insects and plants in these Jurassic deposits of Eastern Siberia have ichnotaxonomic importance for understanding the functioning of Jurassic terrestrial paleoecosystems.
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Costa-Neto, Eraldo M., and Henrique F. Magalhães. "The ethnocategory ''insect'' in the conception of the inhabitants of Tapera County, São Gonçalo dos Campos, Bahia, Brazil." Anais da Academia Brasileira de Ciências 79, no. 2 (June 2007): 239–49. http://dx.doi.org/10.1590/s0001-37652007000200007.
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This article deals with the construction of the "insect" ethnozoological dominium by the inhabitants of Tapera County, which is located in the municipality of São Gonçalo dos Campos, Bahia State. Data were obtained from March to May 2005 through open-ended interviews carried out with 23 men and 8 women, whose ages ranged from 6 to 66 years old. Interviewees were asked about how they perceived and defined the animals considered as "insects", which types they knew, and if they used them as food resource. Most of the interviews were tape-recorded, and semi-literal transcriptions are kept at the Ethnobiology Laboratoty of the Universidade Estadual de Feira de Santana. Considering the ethnozoological classification system of the inhabitants of Tapera, the term "insect" is a broad semantic category that brings together animals of different and not systematically related taxonomic groups. Apparently, these animals are culturally perceived and categorized as "insects" because they are usually considered as noxious, disgusting, and disease carrier creatures. True insects can be excluded from this ethnocategory due to the perception people have that such animals do not cause "injuries" or because they are useful. Perceptions toward these animals imply ambiguous behavior and feelings, which range from more positive attitudes (conservative) to more negative (destructive).
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Buckley, Thomas R., Dilini Attanayake, and Sven Bradler. "Extreme convergence in stick insect evolution: phylogenetic placement of the Lord Howe Island tree lobster." Proceedings of the Royal Society B: Biological Sciences 276, no. 1659 (December 16, 2008): 1055–62. http://dx.doi.org/10.1098/rspb.2008.1552.
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The ‘tree lobsters’ are an enigmatic group of robust, ground-dwelling stick insects (order Phasmatodea) from the subfamily Eurycanthinae, distributed in New Guinea, New Caledonia and associated islands. Its most famous member is the Lord Howe Island stick insect Dryococelus australis (Montrouzier), which was believed to have become extinct but was rediscovered in 2001 and is considered to be one of the rarest insects in the world. To resolve the evolutionary position of Dryococelus , we constructed a phylogeny from approximately 2.4 kb of mitochondrial and nuclear sequence data from representatives of all major phasmatodean lineages. Our data placed Dryococelus and the New Caledonian tree lobsters outside the New Guinean Eurycanthinae as members of an unrelated Australasian stick insect clade, the Lanceocercata. These results suggest a convergent origin of the ‘tree lobster’ body form. Our reanalysis of tree lobster characters provides additional support for our hypothesis of convergent evolution. We conclude that the phenotypic traits leading to the traditional classification are convergent adaptations to ground-living behaviour. Our molecular dating analyses indicate an ancient divergence (more than 22 Myr ago) between Dryococelus and its Australian relatives. Hence, Dryococelus represents a long-standing separate evolutionary lineage within the stick insects and must be regarded as a key taxon to protect with respect to phasmatodean diversity.
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Gerber F. Incacari Sancho, Aura Guerrero-Luzuriaga, Dr Mukta Jagdish, Andres Medina Guzman,. "Detection and Classification of Caterpillar using Image Processing with K-Nearest Neighbor Classification Technique." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 719–28. http://dx.doi.org/10.17762/turcomat.v12i5.1475.
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Caterpillars are the developmental stage of the flying insect called butterfly. The moths are the beautiful creature of earth which comes under the class of insects. They are recognized by their beautiful and colorful forewings body and legs. Caterpillars are the larval stage of the moth which are found in the leaf and stem of the plants when the moth laid eggs on the leaves after their mating. Caterpillar after fully developed from its eggs draw out a flimsy, soft cocoon made up of dark coarse silk on leaves and stem for their shelter. Caterpillar is also a beautiful creature that is found with different colors and strips with spines and urticating hair in their body for releasing venom for self-defense from external predators. The present study works on the detection and classification of the caterpillar using image processing with a k-NN classifier.This research help in characterizing the type of caterpillar image classification for particular three classes such as accuracy of Buck Moth Caterpillar, the accuracy of Saddleback Caterpillar, and the accuracy of Io moth Caterpillar. The following stages have been considered are preprocessing, segmentation, feature extraction, and classification methods using K- Nearest Neighbor classifier. The present investigation results that SYMLET5 analysis works well in the classification of the caterpillar with an accuracy of 96% using K- Nearest Neighbor classifier compare with other measures during investigation and analysis.
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NORMARK, BENJAMIN B., AKIKO OKUSU, GEOFFREY E. MORSE, DANIEL A. PETERSON, TAKAO ITIOKA, and SCOTT A. SCHNEIDER. "Phylogeny and classification of armored scale insects (Hemiptera: Coccomorpha: Diaspididae)." Zootaxa 4616, no. 1 (June 17, 2019): 1–98. http://dx.doi.org/10.11646/zootaxa.4616.1.1.
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Armored scale insects (Hemiptera: Coccomorpha: Diaspididae) are major economic pests and are among the world's most invasive species. Here we describe a system of specimen and identification management that establishes a basis for well-vouchered molecular identification. We also present an expanded Bayesian phylogenetic analysis based on concatenated fragments of 4 genetic loci: the large ribosomal subunit (28S), elongation factor-1 alpha (EF-1α), cytochrome oxidase I and II (COI‒II), and the small ribosomal subunit (16S) of the primary endosymbiont, Uzinura diaspidicola (Bacteroidetes: Flavobacteriales). Our sample includes 1,389 individuals, representing 11 outgroup species and at least 311 described and 61 undescribed diaspidid species. The results broadly support Takagi's 2002 classification but indicate that some revisions are needed. We propose a revised classification recognizing 4 subfamilies: Ancepaspidinae Borchsenius, new rank, Furcaspidinae Balachowsky, new rank, Diaspidinae Targioni Tozzetti, and Aspidiotinae Westwood. Within Aspidiotinae, in addition to the existing tribes Aspidiotini Westwood, Parlatoriini Leonardi, Odonaspidini Ferris, Leucaspidini Atkinson, and Smilacicolini Takagi, we recognize as tribes Gymnaspidini Balachowsky, new rank, and Aonidiini Balachowsky, new rank. Within Diaspidinae we recognize the 2 tribes Lepidosaphidini Shimer and Diaspidini Targioni Tozzetti, and within Diaspidini we recognize three subtribes: Diaspidina Targioni Tozzetti, Fioriniina Leonardi, and Chionaspidina Brues & Melander. We regard Kuwanaspidina Borchsenius as a junior synonym of Fioriniina, Thysanaspidini Takagi as a junior synonym of Leucaspidini, and Protodiaspidina Takagi and Ulucoccinae Takagi as junior synonyms of Chionaspidina. To clarify the composition of the higher taxa we describe 2 new genera for Australian species heretofore misplaced in the genus Ancepaspis Ferris: Brimblecombia Normark (Aonidiini) and Hendersonaspis Normark (Leucaspidini). We also propose many additional minor modifications to the taxonomy of Diaspididae, including the following new combinations, revived combinations, and replacement names: Aonidia edgerleyi (Mamet), new combination (from Bigymnaspis Balachowsky); Aonidomytilus espinosai Porter, revived combination (from Porterinaspis González); Aspidiotus badius (Brain), new combination (this and the next 5 Aspidiotus species all from Aonidia Targioni Tozzetti); Aspidiotus biafrae (Lindinger), new combination; Aspidiotus chaetachmeae (Brain), new combination; Aspidiotus laticornis (Balachowsky), new combination; Aspidiotus rhusae (Brain), new combination; Aspidiotus sclerosus (Munting), new combination; Brimblecombia asperata (Brimblecombe), new combination (this and the next 5 Brimblecombia species all from Ancepaspis); Brimblecombia longicauda (Brimblecombe), new combination; Brimblecombia magnicauda (Brimblecombe), new combination; Brimblecombia reticulata (Brimblecombe), new combination; Brimblecombia rotundicauda (Brimblecombe), new combination; Brimblecombia striata (Brimblecombe), new combination; Cooleyaspis pseudomorpha (Leonardi), new combination (from Dinaspis Leonardi); Cupidaspis wilkeyi (Howell & Tippins), new combination (from Paracupidaspis Howell & Tippins); Cupressaspis isfarensis Borchsenius, revived combination (this species, the next 2 species in Cupressaspis Borchsenius, revived genus, and the next 9 species in Diaspidiotus Cockerell all from Aonidia); Cupressaspis mediterranea (Lindinger), revived combination; Cupressaspis relicta (Balachowsky), new combination; Diaspidiotus atlanticus (Ferris), new combination; Diaspidiotus marginalis (Brain), new combination; Diaspidiotus maroccanus (Balachowsky), new combination; Diaspidiotus mesembryanthemae (Brain), new combination; Diaspidiotus opertus (De Lotto), new combination; Diaspidiotus shastae (Coleman), new combination; Diaspidiotus simplex (Leonardi), new combination; Diaspidiotus visci (Hall), new combination; Diaspidiotus yomae (Munting), new combination; Diaspis arundinariae (Tippins & Howell), new combination (from Geodiaspis Tippins & Howell); Duplachionaspis arecibo (Howell), new combination (this and the next 10 Duplachionaspis MacGillivray species all from Haliaspis Takagi); Duplachionaspis asymmetrica Ferris, revived combination; Duplachionaspis distichlii (Ferris), revived combination; Duplachionaspis litoralis Ferris, revived combination; Duplachionaspis mackenziei McDaniel, revived combination; Duplachionaspis milleri (Howell), new combination; Duplachionaspis nakaharai (Howell), new combination; Duplachionaspis peninsularis (Howell), new combination; Duplachionaspis spartinae (Comstock), revived combination; Duplachionaspis texana (Liu & Howell) new combination; Duplachionaspis uniolae (Takagi), new combination; Duplachionaspis mutica (Williams) (from Aloaspis Williams), new combination; Epidiaspis doumtsopi (Schneider), new combination (from Diaspis Costa); Fiorinia ficicola (Takahashi), new combination (from Ichthyaspis Takagi); Fiorinia macroprocta (Leonardi), revived combination (this and the next 2 species of Fiorinia Targioni Tozzetti all from Trullifiorinia Leonardi); Fiorinia rubrolineata Leonardi, revived combination; Fiorinia scrobicularum Green, revived combination; Genaparlatoria pseudaspidiotus (Lindinger), revived combination (from Parlatoria); Greeniella acaciae (Froggatt), new combination (this and the next 4 Greeniella Cockerell species all from Gymnaspis Newstead); Greeniella cassida (Hall & Williams), new combination; Greeniella grandis (Green), new combination; Greeniella perpusilla (Maskell), new combination; Greeniella serrata (Froggatt), new combination; Hendersonaspis anomala (Green), new combination (from Ancepaspis); Hulaspis bulba (Munting), new combination (this and the next Hulaspis Hall species both from Andaspis MacGillivray); Hulaspis formicarum (Ben-Dov), new combination; Lepidosaphes antidesmae (Rao in Rao & Ferris), new combination (this and the next 19 species all from Andaspis); Lepidosaphes arcana (Matile-Ferrero), new combination; Lepidosaphes betulae (Borchsenius), new combination; Lepidosaphes citricola (Young & Hu), new combination; Lepidosaphes conocarpi (Takagi), new combination; Lepidosaphes crawi (Cockerell), revived combination; Lepidosaphes erythrinae Rutherford, revived combination; Lepidosaphes incisor Green, revived combination; Lepidosaphes indica (Borchsenius), new combination; Lepidosaphes kashicola Takahashi, revived combination; Lepidosaphes kazimiae (Williams), new combination; Lepidosaphes laurentina (Almeida), new combination; Lepidosaphes maai (Williams & Watson), new combination; Lepidosaphes mackieana McKenzie, revived combination; Lepidosaphes micropori (Borchsenius), new combination; Lepidosaphes punicae Laing, revived combination; Lepidosaphes quercicola (Borchsenius), new combination; Lepidosaphes recurrens (Takagi & Kawai), new combination; Lepidosaphes viticis (Takagi), new combination; Lepidosaphes xishuanbannae (Young & Hu), new combination; Lepidosaphes giffardi (Adachi & Fullaway), new combination (from Carulaspis MacGillivray); Lepidosaphes garciniae (Young & Hu), new combination (this and the next 2 species all from Ductofrontaspis Young & Hu); Lepidosaphes huangyangensis (Young & Hu), new combination; Lepidosaphes jingdongensis (Young & Hu), new combination; Lepidosaphes recurvata (Froggatt), revived combination (from Metandaspis Williams); Lepidosaphes ficicola Takahashi, revived combination (this and the next 2 species all from Ungulaspis MacGillivray); Lepidosaphes pinicolous Chen, revived combination; Lepidosaphes ungulata Green, revived combination; Lepidosaphes serrulata (Ganguli), new combination (from Velataspis Ferris); Lepidosaphes huyoung Normark, replacement name for Andaspis ficicola Young & Hu; Lepidosaphes tangi Normark, replacement name for Andaspis schimae Tang; Lepidosaphes yuanfeng Normark, replacement name for Andaspis keteleeriae Yuan & Feng; Leucaspis ilicitana (Gómez-Menor), new combination (from Aonidia); Lopholeucaspis spinomarginata (Green), new combination (from Gymnaspis); Melanaspis campylanthi (Lindinger), new combination (from Aonidia); Mohelnaspis bidens (Green), new combination (from Fiorinia); Parlatoria affinis (Ramakrishna Ayyar), new combination (this and the next 4 Parlatoria species all from Gymnaspis); Parlatoria ficus (Ramakrishna Ayyar), new combination; Parlatoria mangiferae (Ramakrishna Ayyar), new combination; Parlatoria ramakrishnai (Green), new combination; Parlatoria sclerosa (Munting), new combination; Parlatoria bullata (Green), new combination (from Bigymnaspis); Parlatoria leucaspis (Lindinger), new combination (this and the next species both from Cryptoparlatorea Lindinger); Parlatoria pini (Takahashi), new combination; Parlatoria tangi Normark, replacement name for Parlatoria pini Tang; Pseudoparlatoria bennetti (Williams), new combination (from Parlagena McKenzie); Pseudoparlatoria chinchonae (McKenzie), new combination (from Protodiaspis Cockerell); Pseudoparlatoria larreae (Leonardi), revived combination (from Protargionia Leonardi); Quernaspis lepineyi (Balachowsky), new combination (from Chionaspis); Rhizaspidiotus nullispinus (Munting), new combination (from Aonidia); Rolaspis marginalis (Leonardi), new combination (from Lepidosaphes); Salicicola lepelleyi (De Lotto), new combination (from Anotaspis Ferris); Tecaspis giffardi (Leonardi), new combination (from Dinaspis); Trullifiorinia geijeriae (Froggatt), new combination (from Fiorinia); Trullifiorinia nigra (Lindinger), new combination (from Crypthemichionaspis Lindinger); and Voraspis olivina (Leonardi), new combination (from Lepidosaphes).
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Chará-Serna, Ana M., Julián D. Chará, María Del Carmen Zúñiga, Gloria X. Pedraza, and Lina P. Giraldo. "Clasificación trófica de insectos acuáticos en ocho quebradas protegidas de la ecorregión cafetera colombiana." Universitas Scientiarum 15, no. 1 (January 1, 2010): 27. http://dx.doi.org/10.11144/javeriana.sc15-1.tcoa.
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<p><strong>Objective. </strong>To determine the trophic structure of an aquatic insect assembly associated to eight streams in the Colombian coffee-growing ecoregion. <strong>Materials and methods. </strong>Aquatic insects were collected in eight forested streams located in La Vieja river basin. The taxa collected were assigned to dietary groups according to a regional classification based on the gut content analysis of aquatic insects associated to forested streams of the Otún river basin. <strong>Results. </strong>2019 individuals belonging to 73 taxa were collected and 60 were classified into dietary groups. The most abundant group was collectors (55%), followed by shredders (31%) and predators (10%). Scrapers represented only 0.05% of the sample and the remaining 3.95% could not be classified due to lack of information. <strong>Conclusions. </strong>The dominance of collectors and shredders reveals the importance of coarse particulate organic matter (leaf litter) as a food resource for the insect fauna. Similarities between the trophic structure of this community and other communities studied in similar streams, suggest the possibility of a common pattern for Andean streams. This study evidenced the absence of knowledge on trophic ecology of tropical aquatic insects; 50% of the taxa collected had no associated information for the tropics and 20% had no information neither for the tropics nor temperate zones.</p> <p><strong>Key words</strong>: Andean streams, aquatic insects, dietary groups, trophic structure, tropical ecosystems.</p><br />
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Bozdoğan, Hakan, and José Alejandro Cuellar Cardozo. "Neglected factor in the challenges of insect taxonomists: Is human attribute a barrier faced by entomologists?" REVISTA CHILENA DE ENTOMOLOGÍA 47, no. 4 (October 29, 2021): 721–22. http://dx.doi.org/10.35249/rche.47.4.21.08.
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In this study, some human-oriented problems and solution proposals that insect taxonomists encounter in field studies are explained. The problems today’s modern entomologists encounter in the collection, classification, and all other field studies of insects are human-based. Moreover, these human-induced problems fall into two groups: physical and behavioral. In other words, it is necessary not to see the human footprint only as a biological and ecological diversity. In addition, the human factor also complicates the work of entomologists in terms of sensory, perceptual, and behavioral aspects.
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Langor, David W., H. E. James Hammond, John R. Spence, Joshua Jacobs, and Tyler P. Cobb. "Saproxylic insect assemblages in Canadian forests: diversity, ecology, and conservation." Canadian Entomologist 140, no. 4 (August 2008): 453–74. http://dx.doi.org/10.4039/n07-ls02.
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AbstractSaproxylic insect assemblages inhabiting dead wood in Canadian forests are highly diverse and variable but quite poorly understood. Adequate assessment of these assemblages poses significant challenges with respect to sampling, taxonomy, and analysis. Their assessment is nonetheless critical to attaining the broad goals of sustainable forest management because such species are disproportionately threatened elsewhere by the reductions in dead wood generally associated with commercial exploitation of northern forests. The composition of the saproxylic fauna is influenced by many factors, including tree species, degree of decay, stand age, and cause of tree death. Wildfire and forest harvesting have differential impacts on saproxylic insect assemblages and on their recovery in postdisturbance stands. Exploration of saproxylic insect responses to variable retention harvesting and experimental burns is contributing to the development of prescriptions for conserving saproxylic insects in boreal forests. Understanding of processes that determine diversity patterns and responses of saproxylic insects would benefit from increased attention to natural history. Such work should aim to provide a habitat-classification system for dead wood to better identify habitats (and associated species) at risk as a result of forest management. This tool could also be used to improve strategies to better maintain saproxylic organisms and their central nutrient-cycling functions in managed forests.
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Ghougali, Fayssal, Abdelkrim Si Bachir, Nassima Chaabane, Imen Brik, Rachid Ait Medjber, and Abdelhak Rouabah. "Diversity and distribution patterns of benthic insects in streams of the Aurès arid region (NE Algeria)." Oceanological and Hydrobiological Studies 48, no. 1 (March 26, 2019): 31–42. http://dx.doi.org/10.1515/ohs-2019-0004.
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Abstract The objective of the present study was to document the knowledge about the biodiversity of benthic insect communities and their distribution patterns in the semi-arid bioclimatic stage in the streams of the Aurès Region (NE Algeria). The distribution patterns of communities were analyzed in relation to some environmental factors: physicochemical water parameters and global habitat characteristics, including human impact. The taxonomic biodiversity of six sampled streams (wadis) comprises 42 insect taxa, belonging to seven orders and 30 families, of which Coleoptera is the most diverse order (15 taxa), whereas Diptera, Trichoptera and Ephemeroptera dominate in terms of abundance. The human impact, flow velocity and some quality parameters of water (potential of hydrogen, nitrite, concentration of orthophosphates and conductivity) were identified as the most influential environmental variables, which allows the prediction of taxonomic diversity indicators. The classification and regression tree analysis (CART) for benthic insects shows the effect of environmental variables (habitat parameters and human impact in the arid region) on the diversity and distribution of insect orders. The RDA analysis showed that altitude, substrate type, human impact and physicochemical parameters of water (pH, flow velocity, conductivity and total dissolved solids) are the most important predictor variables that play an important role in the distribution patterns of benthic insects.
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Palmieri, Nadia, Maria Angela Perito, Maria Carmela Macrì, and Claudio Lupi. "Exploring consumers’ willingness to eat insects in Italy." British Food Journal 121, no. 11 (October 24, 2019): 2937–50. http://dx.doi.org/10.1108/bfj-03-2019-0170.
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Purpose The purpose of this paper is to investigate the main factors that may affect Italian consumers’ willingness to eat insects. Italy is a fairly special case among Western countries: in many Italian regions, there is old traditional food with insects. Design/methodology/approach Data come from a sample of 456 consumers living in four Italian regions. The empirical investigation involves several steps: modification of class distributions to obtain a balanced sample; model estimation using the least absolute shrinkage and selection operator; model evaluation using out-of-sample classification performance measures; and estimation of the “effect” of each explanatory variable via average predictive comparisons. The uncertainty associated with the whole procedure is evaluated using the bootstrap. Findings The interviewed consumers are generally unwilling to eat insect-based food. However, factors such as previous experience, taste expectations and attitude towards both new food experiences and sustainable food play an important role in shaping individual inclination towards eating insects. Research limitations/implications The sample analysed in this study is not representative of the whole national population, as it happens in most papers dealing with entomophagy. Originality/value The paper revisits the issue using a relatively large sample and sophisticated statistical methods. The likely average effect of each explanatory variable is estimated and discussed in detail. The results provide interesting insights on how to approach a hypothetical Italian consumer in view of the possible development of a new market for edible insects.
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May, Michael. "Odonata: Who They Are and What They Have Done for Us Lately: Classification and Ecosystem Services of Dragonflies." Insects 10, no. 3 (February 28, 2019): 62. http://dx.doi.org/10.3390/insects10030062.
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Odonata (dragonflies and damselflies) are well-known but often poorly understood insects. Their phylogeny and classification have proved difficult to understand but, through use of modern morphological and molecular techniques, is becoming better understood and is discussed here. Although not considered to be of high economic importance, they do provide esthetic/spiritual benefits to humans, and may have some impact as predators of disease vectors and agricultural pests. In addition, their larvae are very important as intermediate or top predators in many aquatic ecosystems. More recently, they have been the objects of study that have yielded new information on the mechanics and control of insect flight.
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Chen, Xue-xin, and Cornelis van Achterberg. "Systematics, Phylogeny, and Evolution of Braconid Wasps: 30 Years of Progress." Annual Review of Entomology 64, no. 1 (January 7, 2019): 335–58. http://dx.doi.org/10.1146/annurev-ento-011118-111856.
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The parasitoid wasp family Braconidae is likely the second-most species-rich family in the animal kingdom. Braconid wasps are widely distributed and often encountered. They constitute one of the principal groups of natural enemies of phytophagous insects, of which many are serious pest species. The enormous biological diversification of braconid wasps has led to many homoplasies, which contributed widely to instabilities in historical classifications. Recent studies using combinations of genetic markers or total mitochondrial genomes allow for better founded groupings and will ultimately lead to a stable classification. We present the current status of the phylogenetics of the Braconidae in a historical perspective and our understanding of the effects on higher classification.
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Ridgway, Christopher, and John Chambers. "Detection of Insects inside Wheat Kernels by NIR Imaging." Journal of Near Infrared Spectroscopy 6, no. 1 (January 1998): 115–19. http://dx.doi.org/10.1255/jnirs.128.
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The research reported here forms the basis to extend the capabilities of a newly-developed grain contaminant inspection system to include detection of kernels infested internally with insects. It was found that wheat kernels infested internally with larvae of Sitophilus granarius (grain weevil) had markedly different appearances from uninfested kernels (controls) when imaged at certain wavelengths in the NIR. Imaging at the single wavelength 1202 nm highlighted consistent differences between all 10 infested kernels and all 10 controls in the sample. Infested kernels exhibited light patches which covered a large proportion of the surface. Uninfested kernels appeared uniformly dark. At this wavelength, possible interference from dark mould on the germ of the kernel (black point) was also removed. Imaging at two wavelengths with subtraction (1202 – 1300 nm) appeared to give further enhancement of differences between infested and control kernels. However, one infested kernel remained indistinguishable from the controls which may have been due to poor lighting or signal-to-noise. The findings are consistent with previous spectroscopic studies which indicated that similar wavelengths had potential to resolve the insect from the kernel. Although the infested kernels were seen by the naked eye to be slightly different to the control kernels, these visible differences were not obvious or consistent. It is unlikely that reliable classification of kernels by visual inspection would prove possible. This study suggests that imaging in the NIR region improves differences in appearance to a point where reliable and rapid classification is possible. The next step will be to test this approach on unknown samples and obtain accuracy in classification.
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Jia, Qi, Rui Min, Shao Guang Huang, and Ying Wei. "A Method of Insect Recognition Based on Spectrogram." Applied Mechanics and Materials 618 (August 2014): 362–66. http://dx.doi.org/10.4028/www.scientific.net/amm.618.362.
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A novel approach to insect recognition is presented in this paper. The difference between the proposed method with traditional methods is that it starts from the perspective of image and combines voice processing algorithms with image processing algorithms. The classification is done based on voice activity detection (VAD) and spectrogram. We show, by means of example that this approach can recognize different insects correctly. However, despite the potential of correct recognition, further justification of the reliability of the method need to be provided by a larger scale of experiments. Hence, some improvements will be proposed latterly.
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Szwedo, Jacek. "The unity, diversity and conformity of bugs (Hemiptera) through time." Earth and Environmental Science Transactions of the Royal Society of Edinburgh 107, no. 2-3 (June 2016): 109–28. http://dx.doi.org/10.1017/s175569101700038x.
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ABSTRACTThis paper outlines and discusses the fossil record of the Hemiptera – the fifth most diverse insect order. The diversity of these insects in comparison with the “Big Four” group is given, together with a short history of its classification. Updated information is presented about the fossil record of particular families, with a brief analysis. The main evolutionary traits of the major Hemiptera lineages are briefly described. The influence of biotic interactions with endosymbionts, shaping the evolution of the hemipterans as well as abiotic events and major global changes, is disputed. The innovations and perils of the evolutionary history of the Hemiptera are presented.
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Zilli, D., O. Parson, G. V. Merrett, and A. Rogers. "A Hidden Markov Model-Based Acoustic Cicada Detector for Crowdsourced Smartphone Biodiversity Monitoring." Journal of Artificial Intelligence Research 51 (December 30, 2014): 805–27. http://dx.doi.org/10.1613/jair.4434.
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In recent years, the field of computational sustainability has striven to apply artificial intelligence techniques to solve ecological and environmental problems. In ecology, a key issue for the safeguarding of our planet is the monitoring of biodiversity. Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest, where this insect was historically found, will help to monitor its presence by means of a smartphone app that can detect its mating call. Existing research in the field of acoustic insect detection has typically focused upon the classification of recordings collected from fixed field microphones. Such approaches segment a lengthy audio recording into individual segments of insect activity, which are independently classified using cepstral coefficients extracted from the recording as features. This paper reports on a contrasting approach, whereby we use crowdsourcing to collect recordings via a smartphone app, and present an immediate feedback to the users as to whether an insect has been found. Our classification approach does not remove silent parts of the recording via segmentation, but instead uses the temporal patterns throughout each recording to classify the insects present. We show that our approach can successfully discriminate between the call of the New Forest cicada and similar insects found in the New Forest, and is robust to common types of environment noise. A large scale trial deployment of our smartphone app collected over 6000 reports of insect activity from over 1000 users. Despite the cicada not having been rediscovered in the New Forest, the effectiveness of this approach was confirmed for both the detection algorithm, which successfully identified the same cicada through the app in countries where the same species is still present, and of the crowdsourcing methodology, which collected a vast number of recordings and involved thousands of contributors.
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Hushmatov, Norkul, and Dilyara Shamshetova. "ECONOMIC LOSSES CAUSED BY INSECTS IN AGRICULTURAL PRODUCTION AND THEIR CLASSIFICATION." JOURNAL OF AGRO PROCESSING 6, no. 1 (June 30, 2019): 62–66. http://dx.doi.org/10.26739/2181-9904-2019-6-11.
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Huerta, Ramón, Shankar Vembu, José M. Amigó, Thomas Nowotny, and Charles Elkan. "Inhibition in Multiclass Classification." Neural Computation 24, no. 9 (September 2012): 2473–507. http://dx.doi.org/10.1162/neco_a_00321.
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The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches.
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Zhu, Shiming, Elin Malmqvist, Yiyun Li, Samuel Jansson, Wansha Li, Zheng Duan, Wei Fu, et al. "Insect remote sensing using a polarization sensitive cw lidar system in chinese rice fields." EPJ Web of Conferences 176 (2018): 07001. http://dx.doi.org/10.1051/epjconf/201817607001.
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A joint Chinese-Swedish field campaign of Scheimpflug continuous-wave lidar monitoring of rice-field flying pest insects was pursued in very hot July weather conditions close to Guangzhou, China. The occurrence of insects, birds and bats with almost 200 hours of round-the-clock polarization-sensitive recordings was studied. Wing-beat frequency recordings and depolarization properties were used for target classification. Influence of weather conditions on the flying fauna was also investigated.
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Roma, Rocco, Giovanni Ottomano Palmisano, and Annalisa De Boni. "Insects as Novel Food: A Consumer Attitude Analysis through the Dominance-Based Rough Set Approach." Foods 9, no. 4 (March 27, 2020): 387. http://dx.doi.org/10.3390/foods9040387.
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In Western societies, the unfamiliarity with insect-based food is a hindrance for consumption and market development. This may depend on neophobia and reactions of disgust, individual characteristics and socio-cultural background, and risk-perceptions for health and production technologies. In addition, in many European countries, the sale of insects for human consumption is still illegal, although European Union (EU) and the European Food Safety Authority (EFSA) are developing regulatory frameworks and environmental and quality standards. This research aims to advance the knowledge on entomophagy, providing insights to improve consumer acceptance in Italy. This is done by carrying out the characterization of a sample of consumers according to their willingness to taste several types of insect-based food and taking into account the connections among the consumers’ features. Thus, the dominance-based rough set approach is applied using the data collected from 310 Italian consumers. This approach provided 206 certain decision rules characterizing the consumers into five groups, showing the consumers’ features determining their specific classification. Although many Italian consumers are willing to accept only insects in the form of feed stuffs or supplements, this choice is a first step towards entomophagy. Conversely, young Italian people are a niche market, but they can play a role in changing trends.
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Schulz, Ashley N., Angela M. Mech, Craig R. Allen, Matthew P. Ayres, Kamal J. K. Gandhi, Jessica Gurevitch, Nathan P. Havill, et al. "The impact is in the details: evaluating a standardized protocol and scale for determining non-native insect impact." NeoBiota 55 (April 3, 2020): 61–83. http://dx.doi.org/10.3897/neobiota.55.38981.
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Assessing the ecological and economic impacts of non-native species is crucial to providing managers and policymakers with the information necessary to respond effectively. Most non-native species have minimal impacts on the environment in which they are introduced, but a small fraction are highly deleterious. The definition of ‘damaging’ or ‘high-impact’ varies based on the factors determined to be valuable by an individual or group, but interpretations of whether non-native species meet particular definitions can be influenced by the interpreter’s bias or level of expertise, or lack of group consensus. Uncertainty or disagreement about an impact classification may delay or otherwise adversely affect policymaking on management strategies. One way to prevent these issues would be to have a detailed, nine-point impact scale that would leave little room for interpretation and then divide the scale into agreed upon categories, such as low, medium, and high impact. Following a previously conducted, exhaustive search regarding non-native, conifer-specialist insects, the authors independently read the same sources and scored the impact of 41 conifer-specialist insects to determine if any variation among assessors existed when using a detailed impact scale. Each of the authors, who were selected to participate in the working group associated with this study because of their diverse backgrounds, also provided their level of expertise and uncertainty for each insect evaluated. We observed 85% congruence in impact rating among assessors, with 27% of the insects having perfect inter-rater agreement. Variance in assessment peaked in insects with a moderate impact level, perhaps due to ambiguous information or prior assessor perceptions of these specific insect species. The authors also participated in a joint fact-finding discussion of two insects with the most divergent impact scores to isolate potential sources of variation in assessor impact scores. We identified four themes that could be experienced by impact assessors: ambiguous information, discounted details, observed versus potential impact, and prior knowledge. To improve consistency in impact decision-making, we encourage groups to establish a detailed scale that would allow all observed and published impacts to fall under a particular score, provide clear, reproducible guidelines and training, and use consensus-building techniques when necessary.
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Sanda, Pavel, Tiffany Kee, Nitin Gupta, Mark Stopfer, and Maxim Bazhenov. "Classification of odorants across layers in locust olfactory pathway." Journal of Neurophysiology 115, no. 5 (May 1, 2016): 2303–16. http://dx.doi.org/10.1152/jn.00921.2015.
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Olfactory processing takes place across multiple layers of neurons from the transduction of odorants in the periphery, to odor quality processing, learning, and decision making in higher olfactory structures. In insects, projection neurons (PNs) in the antennal lobe send odor information to the Kenyon cells (KCs) of the mushroom bodies and lateral horn neurons (LHNs). To examine the odor information content in different structures of the insect brain, antennal lobe, mushroom bodies and lateral horn, we designed a model of the olfactory network based on electrophysiological recordings made in vivo in the locust. We found that populations of all types (PNs, LHNs, and KCs) had lower odor classification error rates than individual cells of any given type. This improvement was quantitatively different from that observed using uniform populations of identical neurons compared with spatially structured population of neurons tuned to different odor features. This result, therefore, reflects an emergent network property. Odor classification improved with increasing stimulus duration: for similar odorants, KC and LHN ensembles reached optimal discrimination within the first 300–500 ms of the odor response. Performance improvement with time was much greater for a population of cells than for individual neurons. We conclude that, for PNs, LHNs, and KCs, ensemble responses are always much more informative than single-cell responses, despite the accumulation of noise along with odor information.
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Khondhodjayeva, Nondira, and Nurmamat Rajabov. "ECOLOGICAL SIGNIFICANCE OF PHEROMONES." JOURNAL OF AGRO PROCESSING 6, no. 2 (June 30, 2020): 64–68. http://dx.doi.org/10.26739/2181-9904-2020-6-11.
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This article reveals the essence of the concept of "pheromones" and their biological significance. In the article variants of their application in agriculture for struggle against insects-pests are presented. The definition of term and classification of pheromones and their types are given: feromons of insects, feromons of fish, feromons of vertebrates, feromons of humans, fermons of plants, sexual feromons, anxiety feromons, trace feromons, epidemic feromons and their functions and significance for the representative's organism and the environment as a whole
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Moyetta, Natalia R., Fabián O. Ramos, Jimena Leyria, Lilián E. Canavoso, and Leonardo L. Fruttero. "Morphological and Ultrastructural Characterization of Hemocytes in an Insect Model, the Hematophagous Dipetalogaster maxima (Hemiptera: Reduviidae)." Insects 12, no. 7 (July 14, 2021): 640. http://dx.doi.org/10.3390/insects12070640.
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Hemocytes, the cells present in the hemolymph of insects and other invertebrates, perform several physiological functions, including innate immunity. The current classification of hemocyte types is based mostly on morphological features; however, divergences have emerged among specialists in triatomines, the insect vectors of Chagas’ disease (Hemiptera: Reduviidae). Here, we have combined technical approaches in order to characterize the hemocytes from fifth instar nymphs of the triatomine Dipetalogaster maxima. Moreover, in this work we describe, for the first time, the ultrastructural features of D. maxima hemocytes. Using phase contrast microscopy of fresh preparations, five hemocyte populations were identified and further characterized by immunofluorescence, flow cytometry and transmission electron microscopy. The plasmatocytes and the granulocytes were the most abundant cell types, although prohemocytes, adipohemocytes and oenocytes were also found. This work sheds light on a controversial aspect of triatomine cell biology and physiology setting the basis for future in-depth studies directed to address hemocyte classification using non-microscopy-based markers.
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Divya, B., and M. Santhi. "Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm." Intelligent Automation & Soft Computing 36, no. 2 (2023): 1881–98. http://dx.doi.org/10.32604/iasc.2023.031573.
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Fuentes, Sigfredo, Eden Tongson, Ranjith R. Unnithan, and Claudia Gonzalez Viejo. "Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling." Sensors 21, no. 17 (September 4, 2021): 5948. http://dx.doi.org/10.3390/s21175948.
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Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5–99.3% for NIR and between 94.2–99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.