Relevant bibliographies by topics / Insects Classification

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  2. Dissertations / Theses
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Academic literature on the topic 'Insects Classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Insects Classification"

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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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Dissertations / Theses on the topic "Insects Classification"

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Chiasson, Hélène. "A Synopsis of the Canadian Thysanoptera." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119175.

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Note: page 74 is missing from archival and circulating copies
The present state af knowledge of the order Thysanaptera in Canada is reviewed. The aoder has been sorely neglected since Provenchar submitted a list af 35 eastern Canadian species in 1890. In contrast to the latest previaus estimate of 102 species (Heming, 1979), the number of species found in this study is 174 (143 described and 31 undescribed species). One new genus and five new species are reported as well as the European species Odontothrips biuncus which has been found for the first time in North America. Descriptions, illustrations and a dichatamaus key to the Canadian genera of the Thysanaptera ara given for the first time. Also included are lists of the species. hitherto found in Canada along with their localities•• and habitats•• In addition, an alternative computer-compatible key is presented for the suborder Tubullfera. The distribution and study af the order in Canada are discussed and notes on collection, classification, biology and economic importance are given.
Ce travail revise l’etat de nos cannais.ance. sur l’ordre de. Thysanopteres au Canada. Ce groupe a fait l’objet de peu d’etudes depuis la publication de Provencher (1890), qui enumerait 35 especes pour l’est du Canada. Dans la presente etude, nous rapportons . 174 especes (143 especes decrites, 31 especes non decrit••) alors qu’Heming (1979) n’en citait que 102 especes. pour ce pays. On y cree un nouveau genre et 5 nouvelles especes, en plus de recolter pour la premiere fois l’espece europeanne, Odonthrips biuncus en Amerique du Nord. L’auteur decrit et illustre pour la premiere fois. les. genres de Thvsanoptere. representes dans la fauna canadienne. De plus, on y trouve une liste des especes presentes au Canada, accompagnee de notes sur leur repartition geographique et leur habitat. On y inclut un tableau dichotomique conventionnel ainsi qu’un tableau de determination adape a l’ordinateur. pour Ie sous-ordre des Tubuliferes. Ce travail fournit egalemant, pour les Thysanopteres du Canada, une discussion sur leur repartition Geographique et des renseignements sur leur recolte et classification, leur biologie et importance economique.
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Morselli, João Paulo. "Taxonomia das espécies de Lutosa Walker, 1869 das florestas do leste do Estado de São Paulo, Brasil : taxonomia, morfologia externa e do complexo fálico (Orthoptera, Stenopelmatoidea, Anostostomatidae) /." Botucatu, 2006. http://hdl.handle.net/11449/99465.

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Orientador: Francisco de Assis Ganeo de Mello
Banca: Francisca Carolina do Val
Banca: Sônia A. Casari
Resumo: O presente trabalho teve por objetivo estudar, do ponto de vista taxonômico, as espécies de Lutosa Walker, 1869 (Orthoptera, Ensifera, Stenopelmatoidea, Anostostomatidae) que ocorrem nas florestas do leste do Estado de São Paulo, Brasil, abrangendo análises da morfologia corporal externa, do complexo fálico e espermateca. Além da re-descrição do gênero e de três espécies já conhecidas, treze novas foram descritas e uma chave dicotômica de identificação é proposta para todas as espécies examinadas. Com base na estrutura de certos elementos que compõem o complexo fálico, dois grupos de espécies foram reconhecidos e definidos. Ainda no que concerne à morfologia da genitália masculina, uma proposta de terminologia para as diferentes áreas do apódema epifálico é apresentada. Dados preliminares sobre hábitos, ecologia e comportamento de espécies de Lutosa são fornecidos.
Abstract: The aim of the present work was to study, from a taxonomic perspective, the species of Lutosa Walker, 1869 (Orthoptera, Ensifera, Stenopelmatoidea, Anostostomatidae) from the eastern forests from the State of São Paulo, Brazil, including analyses of external body morphology, phallic complex and spermatheca. Besides re-describing the genus and three of its valid species, thirteen new ones were described, and a dichotomic identification key for all examined species is presented. Based on the structure of certain elements that comprise the phallic complex, two species groups were recognized and defined. Still regarding male genital morphology, a terminological proposal for the different areas of the epiphallic apodeme is presented. Preliminary data on habits, ecology, and behavior of Lutosa species are given.
Mestre
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Aljmli, Fahmia [Verfasser]. "Classification of oilseed rape visiting insects in relation to the sulphur supply / Fahmia Aljmli." Braunschweig : Bundesforschungsanst. für Landwirtschaft, 2007. http://d-nb.info/996739858/34.

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Silva, Diego Furtado. "Classificação de séries temporais por similaridade e extração de atributos com aplicação na identificação automática de insetos." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28042014-142456/.

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Um dos grandes desafios em mineração de dados é a integração de dados temporais ao seu processo. Existe um grande número de aplicações emergentes que envolvem dados temporais, incluindo a identificação de transações fraudulentas em cartões de crédito e ligações telefônicas, a detecção de intrusão em sistemas computacionais, a predição de estruturas secundárias de proteínas, a análise de dados provenientes de sensores, entre muitas outras. Neste trabalho, tem-se interesse na classificação de séries temporais que representam sinais de áudio. Como aplicação principal, tem-se interesse em classificar sinais de insetos coletados por um sensor óptico, que deve ser capaz de contar e classificar os insetos de maneira automática. Apesar de serem coletados opticamente, os sinais capturados se assemelham a sinais de áudio. O objetivo desta pesquisa é comparar métodos de classificação por similaridade e por extração de atributos que possam ser utilizados no contexto da classificação de insetos. Para isso, foram empregados os principais métodos de classificação de sinais de áudio, que têm sido propostos para problemas como reconhecimento de instrumentos musicais, fala e espécies animais. Neste trabalho, é mostrado que, de modo geral, a abordagem por extração de atributos é mais eficaz do que a classificação por similaridade. Mais especificamente, os melhores resultados são obtidos com a utilização de coeficientes mel-cepstrais. Este trabalho apresenta contribuições significativas em outras aplicações, também relacionadas à análise de séries temporais e sinais de áudio, por similaridade e por extração de atributos
One of the major challenges in data mining is the integration of temporal data to its process. There are a number of emerging applications that involve temporal data, including fraud detection in credit card transactions and phone calls, intrusion detection in computer systems, the prediction of secondary structures of proteins, the analysis of data from sensors, and many others. In this work, our main interest is the classification of time series that represent audio signals. Our main interest is an application for classifying signals of insects collected from an optical sensor, which should count and classify insects automatically. Although these signals are optically collected, they resemble audio signals. The objective of this research is to compare classification methods based on similarity and feature extraction in the context of insects classification. For this purpose, we used the main classification methods for audio signals, which have been proposed for problems such as musical instrument, speech and animal species recognition. This work shows that, in general, the approach based on feature extraction is more accurate than the classification by similarity. More specifically, the best results are obtained with mel-frequency cepstrum coefficients. This work also presents significant contributions in other applications, also related to the analysis of time series and audio signals by similarity and feature extraction
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Bellingan, Terence Andrew. "The diversity of aquatic insects in the Tsitsikamma region, with implications for aquatic ecosystem conservation." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005348.

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As a result of research carried out within the last decade to assess the diversity of macroinvertebrates of the Salt River in the Western Cape Province, South Africa, surveys of macroinvertebrates of 20 sites on 11 selected rivers from the same mountain range source were undertaken. This was done to make a preliminary assessment of the conservation status of the rivers of this region. Aquatic insects from the orders Ephemeroptera, Odonata, Plecoptera, Megaloptera, Trichoptera and the dipteran family Simuliidae were collected using techniques to maximize the number of taxa found. The insects collected were identified to species level where possible. Water physicochemical parameters were recorded at all sites for each sampling trip to characterize these rivers and to establish a set of baseline data for future comparisons. These parameters included measurements made on site and analysis of the concentrations of all the major ions in water samples in the laboratory. Multivariate analyses including Principle Components Analysis and Canonical Correspondence Analysis were used to reveal patterns within the water physicochemistry and species distributional data. Results include the identification of 123 species from 70 genera and 30 families. A total of 31 species were found to be undescribed, of which 17 were not previously collected and are thus completely new to science. In addition, four of these species could not be placed into any known genus. The analysis of water physicochemistry showed a clear distinction between rivers of Table Mountain sandstone and Bokkeveld shale origin. Downstream effects of anthropogenic influences were discernable too. Distinct macroinvertebrate assemblages were found based upon stream order and water chemistry composition. pH proved to be the most important driver of invertebrate assemblage composition. The high levels of endemism of the macroinvertebrates found within the upper reaches of these rivers and their degree of ecological specialization make these systems a priority for the conservation of aquatic biodiversity on a national and global scale.
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Thi, Nhi Pham, Tru Hoang Vu, and Phu Pham Van. "Impact of forest fire on diversity of hymenopteran insects – a study at Copia species-used forest, Son La Province." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-227769.

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Besides the unsustainable exploitation, farming activities and economic development activities, forest fire is considered as one of the major threats to biodiversity and deforestation in Vietnam. In forest ecosystems, any changes in insect communities can affect species composition, nutrient cycling and numerous other ecological processes. The impact of forest fires, however, is not equal to different insect groups. In this paper, we study the impact of forest fires to hymenopteran insects at Copia species-use forest, Son La Province. This is the first time this kind of study has been taken place in Vietnam
Bên cạnh việc khai thác không bền vững, tập quán canh tác và các hoạt động phát triển kinh tế, cháy rừng được xem là một trong những mối đe dọa chính ảnh hưởng tiêu cực tới đa dạng sinh học và mất rừng ở Việt Nam. Trong các hệ sinh thái rừng, những thay đổi trong cấu trúc của quần xã côn trùng có ảnh hưởng tới thành phần loài, chu trình dinh dưỡng và rất nhiều quá trình sinh thái khác. Tuy nhiên mức độ ảnh hưởng của cháy rừng tới các nhóm côn trùng khác nhau là khác nhau. Trong bài báo này, chúng tôi nghiên cứu tác động của cháy rừng tới nhóm côn trùng cánh màng tại khu rừng đặc dụng Copia, tỉnh Sơn La. Đây cũng là lần đầu tiên tác động của cháy rừng tới nhóm côn trùng cánh màng được thực hiện ở Việt Nam
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Geus, André Reis de. "Classificação de insetos em milho à granel por meio de análise de vídeos endoscópicos." Universidade Federal de Goiás, 2016. http://repositorio.bc.ufg.br/tede/handle/tede/7511.

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Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG
Insects cause significant losses of stored grains in both quantity and quality. In the scenary, it is of paramount importance an early identification of insects in grains to take control measures. Instead of sampling and visual/laboratory analysis of grains, we propose to carry out the insects identification task automatically, using computational methods to perform endoscopic video analysis. The videos are recorded inside of grains warehouses by an endoscopic camera. As the classification process of moving objects in video rely heavily on precise segmentation of moving objets, we propose a new method of background subtraction and compared their results with the main methods of the literature according to a recent review. The main innovation of the background subtractionmethod rely on the binarization process that uses two thresholds: a global and a local threshold. The binarized results are combined by adding details of the object obtained by the local threshold in the result of the global threshold. Experimental results performed through visual analysis of the segmentation results and using a SVM classifier, suggest that the proposed segmentation method produces more accurate results than the state-of-art background subtraction methods.
Insetos causam perdas quantitativas e qualitativas significantesemgrãos armazenados. Neste cenário, é de vital importância uma identificação rápida de insetos em grãos para que sejam tomadas medidas de controle. Ao invés de coletar amostras de grãos para análise visual/laboratorial, é proposta a realização desta tarefa de identificação de formaautomática, usando métodos computacionais para a análise de vídeos endoscópicos. Os vídeos são gravados dentro de armazéns de grãos usando câmera endoscópica. Como o processo de classificação de objetos em movimento em vídeo depende fundamentalmente de uma segmentação de objeto precisa, é proposto um novo método de segmentação por subtração de plano de fundo e comparado seus resultados com os principais métodos da literatura de acordo com um estudo de revisão recente. A principal inovação neste método de subtração de plano de fundo está no processo de binarização que usa dois thresholds: um global e um local. Os resultados binarizados são combinados pela adição de detalhes do objeto obtido pelo threshold local no resultado do threshold global. Resultados experimentais, realizados através de análise visual dos resultados de segmentação e usandoumclassificadorSVMindicamque o método de segmentação proposto produz melhores resultados que métodos do estado da arte atual da literatura de subtração de plano de fundo.
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Brunkau, Moritz, Roberto Cruz-García, Denie Gerold, Johannes Kalbe, Tobias Scharnweber, and Jan Wilkens. "Waldinventur und Klimawandel." Deutscher Landwirtschaftsverlag, 2019. https://tud.qucosa.de/id/qucosa%3A36517.

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Experten dreier deutscher Hochschulen entwickelten gemeinsam mit der Ostdeutschen Gesellschaft für Forstplanung mbH ein neues, forstliches Monitoringsystem. Das Verbundprojekt „Entwicklung eines forstlichen Monitoringsystems unter Berücksichtigung von Kohlenstoffspeicherung und Klimaanpassung“ (FOMOSY-KK) wird vorgestellt.
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Pham, Mai Quynh. "Estimation of a longan stink bug, Tessaratoma papillosa in Son La Province, Vietnam." Technische Universität Dresden, 2016. https://tud.qucosa.de/id/qucosa%3A32485.

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Many insects are considered as potential sources of food for humans because of their high content of protein, fat, minerals and vitamins. Tessaratoma papillosa Drury, a stink bug of longan tree has long been consumed by many people in Vietnam as a supplemental source of nutrition. This insect is one of the most severe pests of longan tree. The use of this insect as supplementary nutrient could actively reduce the pests on longans, contributing to increase productivity and quality of longan fruit. The aim of this paper is to estimate the volume of mature and young Tessaratoma papillosa on longan trees in Son La Province, Vietnam.
Nhiều loài côn trùng được coi là nguồn thực phẩm tiềm năng của con người vì chúng có chứa hàm lượng cao protein, chất béo, chất khoáng và vitamin. Loài bọ xít nhãn Tessaratoma papillosa Drury, từ lâu đã được nhiều người dân ở Việt Nam sử dụng như là một nguồn thực phẩm bổ sung dinh dưỡng. Loài bọ xít nhãn là một trong những loài gây hại nghiêm trọng nhất đối với cây nhãn. Việc sử dụng loài côn trùng này không những bổ sung chất dinh dưỡng cho con người mà còn góp phần chủ động giảm loài sâu hại trên nhãn, góp phần tăng năng suất và chất lượng trái cây nhãn. Mục đích của bài viết này là để ước tính khối lượng trưởng thành và ấu trùng loài Tessaratoma papillosa trên cây nhãn ở tỉnh Sơn La, Việt Nam.
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Chérot, Frédéric. "Eléments de classification générique et de phylogénie de Mirinae (Insecta, Heteroptera :Miridae)." Doctoral thesis, Universite Libre de Bruxelles, 2002. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211443.

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Books on the topic "Insects Classification"

1

Arnett, Ross H. American insects: A handbook of the insects of America north of Mexico. New York: Van Nostrand Reinhold, 1985.

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Solway, Andrew. Insects. 2nd ed. Oxford: Heinemann Library, 2009.

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American insects: A handbook of the insects of America north of Mexico. Gainesville, Fla: Sandhill Crane Press, 1993.

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Thapa, V. K. An inventory of Nepal's insects. Kathmandu: IUCN-The World Conservation Union, 1997.

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Baltazar, Clare R. An inventory of Philippine insects. Laguna, Philippines: University of the Philippines Los Baños, University Publications Office, 1990.

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Surdick, Rebecca F. Nearctic genera of Chloroperlinae (Plecoptera--Chloroperlidae). Urbana: University of Illinois Press, 1985.

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Semmens, T. D. Catalogue of the insects of Tasmania. [Tasmania, Australia]: Dept. of Primary Industry, Tasmania, 1992.

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Slade, Suzanne. Insects: Six-legged animals. Minneapolis, Minn: Picture Window Books, 2010.

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Dear, James P. Calliphoridae (Insecta--Diptera). Wellington, N.Z: Science Information Pub. Centre, DSIR, 1986.

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Dant͡sig, Evlina Markovna. Coccids of the Far-Eastern USSR (Homoptera, Coccinea): Phylogenetic analysis of coccids in the world fauna. New Delhi: Oxonian Press, 1986.

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Book chapters on the topic "Insects Classification"

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Borowiec, Marek L., Corrie S. Moreau, and Christian Rabeling. "Ants: Phylogeny and Classification." In Encyclopedia of Social Insects, 52–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-28102-1_155.

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Bourguignon, Thomas, and Nathan Lo. "Termites: Phylogeny and Classification." In Encyclopedia of Social Insects, 963–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-28102-1_150.

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Bourguignon, Thomas, and Nathan Lo. "Termites: Phylogeny and Classification." In Encyclopedia of Social Insects, 1–6. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-90306-4_150-1.

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Borowiec, Marek L., Corrie S. Moreau, and Christian Rabeling. "Ants: Phylogeny and Classification." In Encyclopedia of Social Insects, 1–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-90306-4_155-1.

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Brothers, Denis J. "Aculeate Hymenoptera: Phylogeny and Classification." In Encyclopedia of Social Insects, 3–11. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-28102-1_1.

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Brothers, Denis J. "Aculeate Hymenoptera: Phylogeny and Classification." In Encyclopedia of Social Insects, 1–9. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-90306-4_1-1.

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Ogilvie, Brian W. "Beasts, Birds, and Insects. Folkbiology and Early Modern Classification of Insects." In Wissenschaftsgeschichte und Geschichte des Wissens im Dialog - Connecting Science and Knowledge, 295–316. Göttingen: V&R Unipress, 2013. http://dx.doi.org/10.14220/9783737001717.295.

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de Geus, André R., Marcos A. Batista, Marcos N. Rabelo, Celia Z. Barcelos, and Sérgio F. da Silva. "Maize Insects Classification Through Endoscopic Video Analysis." In Advances in Artificial Intelligence, 251–62. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18305-9_20.

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Visalli, Francesco, Teresa Bonacci, and N. Alberto Borghese. "Insects Image Classification Through Deep Convolutional Neural Networks." In Progresses in Artificial Intelligence and Neural Systems, 217–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5093-5_21.

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Kumar, Ranjeet. "Classification and Identification of Important Stored Grain/Seed Insects." In Insect Pests of Stored Grain, 19–60. Waretown, NJ : Apple Academic Press, 2017.: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315365695-3.

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Conference papers on the topic "Insects Classification"

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Batista, Gustavo E. A. P. A., Yuan Hao, Eamonn Keogh, and Agenor Mafra-Neto. "Towards Automatic Classification on Flying Insects Using Inexpensive Sensors." In 2011 Tenth International Conference on Machine Learning and Applications (ICMLA). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.145.

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Zhang, Hongtao, and Yuxia Hu. "Extension Theory for Classification of the Stored-Grain Insects." In 2010 International Conference on Machine Vision and Human-machine Interface. IEEE, 2010. http://dx.doi.org/10.1109/mvhi.2010.40.

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Challa, Ramaiah, Rajesh Yamparala, Kotam Raju Siva Kumar, and Satya Sandeep Kanumalli. "Computer Based advanced Classification of Insects using M3 filtering and GAN." In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2021. http://dx.doi.org/10.1109/icesc51422.2021.9532641.

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Varma, Alluri L. S. V. Siddhartha, Vishal Bateshwar, Anubuthi Rathi, and Anurag Singh. "Acoustic Classification of Insects using Signal Processing and Deep Learning Approaches." In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2021. http://dx.doi.org/10.1109/spin52536.2021.9566121.

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Teixeira, Ana Claudia, Jose Ribeiro, Alexandre Neto, Raul Morais, Joaquim J. Sousa, and Antonio Cunha. "Using Deep Learning for Detection and Classification of Insects on Traps." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884452.

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Noda, Juan J., Carlos M. Travieso, David Sanchez-Rodriguez, Malay Kishore Dutta, and Anushikha Singh. "Using bioacoustic signals and Support Vector Machine for automatic classification of insects." In 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2016. http://dx.doi.org/10.1109/spin.2016.7566778.

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Rebelo, Allan Rodrigues, Joao Marcos Garcia Fagundes, Luciano Antonio Digiampietri, and Helton Hideraldo Bíscaro. "Methods for Automatic Image-Based Classification of Winged Insects Using Computational Techniques." In SBSI'20: XVI Brazilian Symposium on Information Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3411564.3411641.

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Silva, Diego, and Gustavo Batista. "Signal classification by similarity and feature extraction with application in automatic insect identification." In XXVIII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2015.10006.

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Insects have a strong relationship with the human-beings. For example, some species of mosquito transmit diseases that kill millions of people around the world. At the same time, the presence of certain insects is essential for the ecological balance and food production. For this reason, we are developing a novel sensor as a tool to efficiently control disease vectors and agricultural pests without harming other species. In this paper, we demonstrate how we overtook the most important challenge to make this sensor practical: the creation of accurate classification systems. Despite the short duration and the very simple structure of the signal, we managed to successfully identify relevant features using speech and audio analysis techniques. We show that we can achieve an accuracy of 98% in the task of disease vector mosquitoes identification.
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Evangelista, Ivan Roy S. "Bayesian Wingbeat Frequency Classification and Monitoring of Flying Insects Using Wireless Sensor Networks." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650550.

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Munoz, J. Pablo, Rebecca Boger, Scott Dexter, Russanne Low, and Justin Li. "Image Recognition of Disease-Carrying Insects: A System for Combating Infectious Diseases Using Image Classification Techniques and Citizen Science." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2018. http://dx.doi.org/10.24251/hicss.2018.359.

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Reports on the topic "Insects Classification"

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Ander, Kjell. An abdominal stridulation organ in Cyphoderris (Prophalangopsidae) and concerning the systematic classification of the Ensifera (Saltatoria). MacEwan University Library, January 2021. http://dx.doi.org/10.31542/r.gm:2687.

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Ensiferan insects (crickets, katydids, grigs and allies) are well known for rubbing parts of their cuticle together to produce sound: a process called stridulation. In this article Swedish entomologist Kjell Ander describes a novel (at the time) stridulatory apparatus in the great grig, Cyphoderris monstrosa (Prophalangopsidae), a relict ensiferan found in the mountainous regions of western North America. Ander used preserved specimens to predict the sound-producing function of a pair of abdominal file-scraper apparatuses, although he was never able to directly test his proposed mechanism nor did he speculate as to the adaptive significance of the structures. The article concludes with a review of the systematic placement of various higher level taxa within the order Orthoptera, of which Ensifera is one suborder.
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Ander, Kjell. An abdominal stridulation organ in Cyphoderris (Prophalangopsidae) and concerning the systematic classification of the Ensifera (Saltatoria). MacEwan University Library, January 2021. http://dx.doi.org/10.31542/r.gm:2687.

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Ensiferan insects (crickets, katydids, grigs and allies) are well known for rubbing parts of their cuticle together to produce sound: a process called stridulation. In this article Swedish entomologist Kjell Ander describes a novel (at the time) stridulatory apparatus in the great grig, Cyphoderris monstrosa (Prophalangopsidae), a relict ensiferan found in the mountainous regions of western North America. Ander used preserved specimens to predict the sound-producing function of a pair of abdominal file-scraper apparatuses, although he was never able to directly test his proposed mechanism nor did he speculate as to the adaptive significance of the structures. The article concludes with a review of the systematic placement of various higher level taxa within the order Orthoptera, of which Ensifera is one suborder.
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Hernandez, J. E., C. J. Frerking, and D. W. Myers. INSENS classification algorithm report. Office of Scientific and Technical Information (OSTI), July 1993. http://dx.doi.org/10.2172/10181154.

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