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Journal articles on the topic 'Automatic insect identification systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Wu, Feng, and Yueying Li. "Lightweight Field Insect Recognition and Classification Model Based on Improved Deep Learning under Complex Background." Security and Communication Networks 2023 (April 7, 2023): 1–9. http://dx.doi.org/10.1155/2023/6560747.

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Insect monitoring in the field is an extremely important part of the agricultural production system. Recent advances in computer technology have provided the technical foundation for automatic field insect monitoring. In insect automatic monitoring, insect recognition and classification based on images is one of the most active research areas. Rapid advancements in computer vision technology based on deep learning have provided new ideas for implementing automatic field insect monitoring. Firstly, the field insect images are preprocessed and input to the lightweight algorithm for feature extra

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Zhao, Yue, Lin Zhou, Aocheng Zhou, and Yao Xiao. "Automatic insect identification system based on SE-ResNeXt." International Journal of Systems, Control and Communications 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijscc.2022.10049960.

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Xiao, Yao, Aocheng Zhou, Lin Zhou, and Yue Zhao. "Automatic insect identification system based on SE-ResNeXt." International Journal of Systems, Control and Communications 14, no. 1 (2023): 81. http://dx.doi.org/10.1504/ijscc.2023.127487.

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Cardim Ferreira Lima, Matheus, Maria Elisa Damascena de Almeida Leandro, Constantino Valero, Luis Carlos Pereira Coronel, and Clara Oliva Gonçalves Bazzo. "Automatic Detection and Monitoring of Insect Pests—A Review." Agriculture 10, no. 5 (2020): 161. http://dx.doi.org/10.3390/agriculture10050161.

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Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the m

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Wang, Jing, Minglin Hong, Xia Hu, et al. "Camouflaged Insect Segmentation Using a Progressive Refinement Network." Electronics 12, no. 4 (2023): 804. http://dx.doi.org/10.3390/electronics12040804.

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Accurately segmenting an insect from its original ecological image is the core technology restricting the accuracy and efficiency of automatic recognition. However, the performance of existing segmentation methods is unsatisfactory in insect images shot in wild backgrounds on account of challenges: various sizes, similar colors or textures to the surroundings, transparent body parts and vague outlines. These challenges of image segmentation are accentuated when dealing with camouflaged insects. Here, we developed an insect image segmentation method based on deep learning termed the progressive

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Suto, Jozsef. "Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review." Agriculture 12, no. 10 (2022): 1721. http://dx.doi.org/10.3390/agriculture12101721.

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The codling moth (Cydia pomonella) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. The conventional monitoring approach for insect swarming has been based on traps that are periodically checked by human operators. However, this workflow can be automatized. To achieve this goal, a dedicated image capture device and an accurate insect counter algorithm are necessary which make online insect swarm prediction possible. From the

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Gong, He, Tonghe Liu, Tianye Luo, et al. "Based on FCN and DenseNet Framework for the Research of Rice Pest Identification Methods." Agronomy 13, no. 2 (2023): 410. http://dx.doi.org/10.3390/agronomy13020410.

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One of the most important food crops is rice. For this reason, the accurate identification of rice pests is a critical foundation for rice pest control. In this study, we propose an algorithm for automatic rice pest identification and classification based on fully convolutional networks (FCNs) and select 10 rice pests for experiments. First, we introduce a new encoder–decoder in the FCN and a series of sub-networks connected by jump paths that combine long jumps and shortcut connections for accurate and fine-grained insect boundary detection. Secondly, the network also integrates a conditional

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Dr., Priyanka Solanki, Varma Nalla Anil, Satwika Maharaj, Bhattacharjya Swastik, Sonu Kumar, and Rajput Rajat. "Smart Pest Management in Precision Farming: A Comprehensive Review." International Journal of Environmental & Agriculture Research (IJOEAR) 10, no. 7 (2024): 23–35. https://doi.org/10.5281/zenodo.13133427.

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Precision agriculture has transformed current agricultural techniques by incorporating cutting-edge technology to maximize resource usage, increase crop output, and reduce insect threats. This article delves into the nexus of precision agriculture and pest management, explaining how precise approaches are designed to address pest concerns effectively and sustainably. Precision agriculture takes a multifaceted approach to pest control, using technology and tactics from throughout the agricultural environment. Remote sensing technologies are essential for early pest identification, sensor techno

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Swain, Deepak Kumar, Ved Vrat Verma, Manni Sruthi, Anvesha Garg, and Vaibhav Kaushik. "3D modeling and automated identification of insect species using AI-based systems." Journal of Entomological Research 48, suppl (2024): 673–79. https://doi.org/10.5958/0974-4576.2024.00131.3.

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Chesmore, E. D., and E. Ohya. "Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition." Bulletin of Entomological Research 94, no. 4 (2004): 319–30. http://dx.doi.org/10.1079/ber2004306.

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AbstractRecognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99% accuracy for good qualit

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Sütő, József. "Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm." Electronics 10, no. 15 (2021): 1754. http://dx.doi.org/10.3390/electronics10151754.

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Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the fi

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Sun, Quan, Hongyi Yin, Linsong Guo, Zhaocai Zeng, and Youliang Xiong. "Research on Automatic Identification of Drainage Network Defects based on Transformer and CNN." Journal of Physics: Conference Series 2895, no. 1 (2024): 012048. https://doi.org/10.1088/1742-6596/2895/1/012048.

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Abstract In order to achieve the governance of urban water environment systems and ensure the normal operation of urban drainage systems, it is necessary to regularly inspect the drainage pipe network to detect and identify defects. Traditional methods of manually identifying defects in Close Circuit Television (CCTV) data and annotating them are labor-intensive and inefficient. To address this issue, a method for automatic identification of pipe network defects based on convolutional neural network (CNN) and transformer is proposed. The model is named RFCBAM-CGA-RTDETR and has been applied to

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Sciarretta, Andrea, and Pasquale Calabrese. "Development of Automated Devices for the Monitoring of Insect Pests." Current Agriculture Research Journal 7, no. 1 (2019): 19–25. http://dx.doi.org/10.12944/carj.7.1.03.

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The monitoring of insect pests based on adult trapping systems is part of integrated pest management (IPM) in many crops and of early warning detection programs used to set up appropriate management decisions or eradication responses. Using data obtained from traps to make management decisions is not an easy task and involves significant costs in equipment, transport and labour. Thanks to the spread and the low prices of information and communication technologies, innovative automated capture devices including electronic sensors and connecting components are increasingly being developed, allow

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Grichanov, I.Ya. "Identification of mass pest species from photos using Internet search engines." Zashchita i Karantin Rastenii, no. 7 (July 31, 2020): 33–36. https://doi.org/10.5281/zenodo.3606235.

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The accuracy of identifying some dangerous harmful insects by photos using the Yandex search engine has been evaluated. It is shown that most large agricultural plant pests with well-expressed specific features are automatically determined from photographs with a high probability (60 percent or more). The prospect of automatic recognition of mass pests from images taken by UAVs is discussed.  

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Aljebreen, Mohammed, Hanan Abdullah Mengash, Fadoua Kouki, and Abdelwahed Motwakel. "Improved Artificial Ecosystem Optimizer with Deep-Learning-Based Insect Detection and Classification for Agricultural Sector." Sustainability 15, no. 20 (2023): 14770. http://dx.doi.org/10.3390/su152014770.

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The agricultural industry has the potential to meet the increasing food production requirements and supply nutritious and healthy food products. Since the Internet of Things (IoT) phenomenon has achieved considerable growth in recent years, IoT-based systems have been established for pest detection so as to mitigate the loss of crops and reduce serious damage by employing pesticides. In the event of pest attack, the detection of crop insects is a tedious process for farmers since a considerable proportion of crop yield is affected and the quality of pest detection is diminished. Based on morph

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Baker, Edward, and David Chesmore. "Standardisation of bioacoustic terminology for insects." Biodiversity Data Journal 8 (August 4, 2020): e54222. https://doi.org/10.3897/BDJ.8.e54222.

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After reviewing the published literature on sound production in insects, a standardised terminology and controlled vocabularies have been created. This combined terminology has potential for use in automated identification systems, evolutionary studies, and other use cases where the synthesis of bioacoustic traits from the literature is required. An example implementation has been developed for the BioAcoustica platform. It is hoped that future development of controlled vocabularies will become a community effort.

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Longhi, Valeria, Alessio Martino, Andrea Maria Lingua, Paolo Felice Maschio, and Elena Belcore. "Monitoring the spread of a pathogenic insect on vineyards using UAS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 443–50. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-443-2024.

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Abstract. Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV

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Suwarningsih, Wiwin, Purnomo Husnul Khotimah, Andri Fachrur Rozie, et al. "Ide-cabe: chili varieties identification and classification system based leaf." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 445–53. http://dx.doi.org/10.11591/eei.v11i1.3276.

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Identifying good quality chili varieties can be done by observing their leaves. It is required for seed testing and certification processes. Currently, a manual leaf identification method is used in which human experts inspect a wide range of leaves every one to two months. An automatic method could increase the identification process. Deep learning has proven to be a prominent method for image classification. We investigate the performance of deep CNN models, as: AlexNet, VGG16, Inception-v3 and DenseNet-121; to classify chili variety. In this paper, we took images of leaves aged 10 days. A p

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Li, Qiming. "Optimizing Token Fusion Mechanisms in Swin Transformer for Improved Feature Representation and Fine-Grained Insect Classification." Applied and Computational Engineering 142, no. 1 (2025): 151–60. https://doi.org/10.54254/2755-2721/2025.kl22294.

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Effective pest monitoring is critical for ensuring global food security, as insect pests pose significant threats to crop yields and agricultural sustainability. Traditional pest identification methods rely on manual inspection, which is time-consuming, labor-intensive, and susceptible to human error. Deep learning, particularly Convolutional Neural Networks (CNNs), has been widely applied to automate insect classification; however, these models exhibit limitations in capturing long-range dependencies and hierarchical feature representations. Transformer-based architectures, such as Swin Trans

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Zhao, Jumin, Fangfang Xue, and Deng-ao Li. "Intelligent Management of Chemical Warehouses with RFID Systems." Sensors 20, no. 1 (2019): 123. http://dx.doi.org/10.3390/s20010123.

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At present, most chemical warehouses rely on human management, which is a time-consuming and laborious process. Therefore, it is very meaningful to use radio frequency identification (RFID) systems for the intelligent management of chemicals. Detecting the remaining amount of chemicals is an important process in the management of a chemical warehouse. It helps managers find the chemicals that are going to run out and replenish them in time. However, in a traditional chemical warehouse, managers usually inspect each chemical on the shelf in turn manually, which is a waste of time and labor. Alt

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Parraga-Alava, Jorge, Roberth Alcivar-Cevallos, Jéssica Morales Carrillo, et al. "LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves." Data 6, no. 5 (2021): 51. http://dx.doi.org/10.3390/data6050051.

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Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation sever

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V., Korolev, and Zhoraeva O. "AUTOMATED START-UP SYSTEM FOR ATHLETES AND CONTROL AT TRAINING AND COMPETITIONS." Health, Physical Culture and Sports 29, no. 1 (2023): 91–96. http://dx.doi.org/10.14258/zosh(2023)1.13.

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This paper is about developing a time event system at competitions with the athletes and the start-finish identification. To implement the system, widely used integrated systems-on-achip or modules are used, which are a microcontroller with a digital transceiver, accelerometers, gyroscopes and other integrated or external microdevices and sensors, for example, an oximeter, breath sensor, galvanic skin response and others, which are connected to analog -digital converter of the controller or digital port.

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H, Godavari, Vidya Madhuri E, Tulasi B, et al. "Precision Farming Solutions: Integrating Technology for Sustainable Pest Management." Journal of Advances in Biology & Biotechnology 27, no. 8 (2024): 33–54. http://dx.doi.org/10.9734/jabb/2024/v27i81119.

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Precision agriculture has revolutionized modern farming practices by integrating advanced technologies to optimize resource utilization, enhance crop productivity, and mitigate pest pressures. This article explores the intersection of precision agriculture and pest management, elucidating how precision techniques are tailored to combat pest challenges efficiently and sustainably. Precision agriculture employs a multi-faceted approach to pest management, leveraging various technologies and strategies across the agricultural landscape. Key components include remote sensing technologies for early

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Weeks, A. R., N. M. Endersby, C. L. Lange, A. Lowe, M. P. Zalucki, and A. A. Hoffmann. "Genetic variation among Helicoverpa armigera populations as assessed by microsatellites: a cautionary tale about accurate allele scoring." Bulletin of Entomological Research 100, no. 4 (2009): 445–50. http://dx.doi.org/10.1017/s0007485309990460.

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AbstractThe existence of genetic differences among Australian populations of the pest moth Helicoverpa armigera based on microsatellite markers is contentious. To resolve this issue, we analyzed microsatellite variation in moth samples from multiple locations simultaneously in two laboratories that have previously reported contrasting patterns. Alleles and allele numbers detected in the laboratories differed, as did the genetic differences found between the samples. The automated scoring system used in one of the laboratories combined with non-denaturing polyacrylamide gels led to inaccurate i

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Pallepati Vasavi, Et al. "Image based Chili Crop Disease Prediction Using Deep Transfer Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1145–49. http://dx.doi.org/10.17762/ijritcc.v11i10.8635.

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Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challeng

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Praharsha, Chittathuru Himala, Alwin Poulose, and Chetan Badgujar. "Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images." Sensors 24, no. 23 (2024): 7858. https://doi.org/10.3390/s24237858.

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Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (Solanum lycopersicum), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture. This paper explores using Convolutional Neural Networks (CNNs) to classify tomato pest images automatically. Specifically, we investigate

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Putze, Lina, and Eckard Böde. "Systematic Identification and Analysis of Hazards for Automated Systems." INSIGHT 25, no. 4 (2022): 100–104. http://dx.doi.org/10.1002/inst.12421.

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Putze, Lina, and Eckard Böde. "Systematic Identification and Analysis of Hazards for Automated Systems." INSIGHT 28, no. 1 (2025): 69–74. https://doi.org/10.1002/inst.12530.

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ABSTRACTThe introduction of automation into technical systems promises many benefits, including performance increase, improved resource economy, and fewer harmful accidents. In particular, in the automotive sector, automated driving is seen as one key element in Vision Zero by eliminating common accident causes such as driving under the influence, reckless behavior, or distracted drivers. However, this is contrasted by new failure modes and hazards from the latest technologies. In this article, we address the problems of finding common sources of criticality for specific application classes an

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Al-Smadi, Adnan. "Automatic identification of ARMA systems." International Journal of General Systems 38, no. 1 (2009): 29–41. http://dx.doi.org/10.1080/03081070701655259.

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Fonseka, C. L. S. C., and J. A. K. S. Jayasinghe. "Localization of component lead inside a THT solder joint for solder defects classification." Journal of Achievements in Materials and Manufacturing Engineering 2, no. 83 (2017): 57–66. http://dx.doi.org/10.5604/01.3001.0010.7033.

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Purpose: Automatic Optical Inspection (AOI) systems, used in electronics industry have been primarily developed to inspect soldering defects of Surface Mount Devices (SMD) on a Printed Circuit Board (PCB). However, no commercially available AOI system exists that can be integrated to a desktop soldering robotic system, which is capable of identifying soldering defects of Through Hole Technology (THT) solder joints along with the soldering process. In our research, we have implemented an AOI platform that is capable of performing automatic quality assurance of THT solder joints in a much effici

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Smith, J. S. C., and J. C. Register III. "Genetic purity and testing technologies for seed quality: a company perspective." Seed Science Research 8, no. 2 (1998): 285–94. http://dx.doi.org/10.1017/s0960258500004189.

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AbstractA high level of genetic purity in crop varieties must be achieved and maintained for agronomic performance as well as to encourage investment and innovation in plant breeding and to ensure that the improvements in productivity and quality imparted by breeders are delivered to the farmer and, ultimately, to the consumer. Traditionally, morphological comparisons have formed the basis for genetic purity evaluations. However, replicated field observations are time-consuming, expensive and unreliable. Morphology cannot provide information on the purity of specific genetic attributes that re

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Qafmolla, Nejla. "Automatic Language Identification." European Journal of Language and Literature 7, no. 1 (2017): 140. http://dx.doi.org/10.26417/ejls.v7i1.p140-150.

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Automatic Language Identification (LID) is the process of automatically identifying the language of spoken utterance or written material. LID has received much attention due to its application to major areas of research and long-aspired dreams in computational sciences, namely Machine Translation (MT), Speech Recognition (SR) and Data Mining (DM). A considerable increase in the amount of and access to data provided not only by experts but also by users all over the Internet has resulted into both the development of different approaches in the area of LID – so as to generate more efficient syst

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Timoshenko, I. V. "The principles of unique identification of library documents in automatic proximity identification systems." Scientific and Technical Libraries 1, no. 2 (2021): 65–80. http://dx.doi.org/10.33186/1027-3689-2021-2-65-80.

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The author examines prospects for applying the full-range standard functionalities of automatic proximity identification systems. He discusses their performance capabilities for unique identification of library documents in the library information systems of different scaling plateau; using standards library standard identifiers in non-library information systems; the functionality of automatic proximity identification systems for library automation. The RFID technology plays the key role in developing automatic proximity identification. The library application features are examined from the v

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Valan, Miroslav, Karoly Makonyi, Atsuto Maki, Dominik Vondráček, and Fredrik Ronquist. "Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks." Systematic Biology 68, no. 6 (2019): 876–95. http://dx.doi.org/10.1093/sysbio/syz014.

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Abstract Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervent

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Hung, Pham Ngoc, Trinh Van Loan, and Nguyen Hong Quang. "AUTOMATIC IDENTIFICATION OF VIETNAMESE DIALECTS." Journal of Computer Science and Cybernetics 32, no. 1 (2016): 19–30. http://dx.doi.org/10.15625/1813-9663/32/1/7905.

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The dialect identification was studied for many languages over the world nevertheless the research on signal processing for Vietnamese dialects is still limited and there were not many published works. There are many different dialects for Vietnamese. The influence of dialectal features on speech recognition systems is important. If the information about dialects is known during speech recognition process, the performance of recognition systems will be better because the corpus of these systems is normally organized according to different dialects. This paper will present the combination of MF

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Hou, Ying, and Gui Cai Wang. "Research on Grain Storage Insect Image for Automatic Identification." Applied Mechanics and Materials 602-605 (August 2014): 1723–27. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1723.

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According to prevention level of grain storage requirements and granary actual situation, a novel grain storage insect image for automatic identification algorithm was presented based on features point and stereo matching. Firstly, pyramid delaminating was used to preprocess target image. Then, image normalized could be obtained according to formula. Secondly, the SIFT features points could be extracted from reference image and target image. Finally, according to epipolar constraint and the straight line slope of pass through features matching points, The paper could be successful to automatic

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Jansen, Rolf, and Andreas Krabs. "Automatic identification in packaging — radio frequency identification in multiway systems." Packaging Technology and Science 12, no. 5 (1999): 229–34. http://dx.doi.org/10.1002/(sici)1099-1522(199909/10)12:5<229::aid-pts479>3.0.co;2-6.

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Polyvoda, O. V., and Yu A. Lebedenko. "Identification of automatic systems for acoustic emission diagnostics." METHODS AND DEVICES OF QUALITY CONTROL, no. 1(44) (June 28, 2020): 35–45. http://dx.doi.org/10.31471/1993-9981-2020-1(44)-35-45.

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The difficulties of automation control, which are connected with variability of input parameters and complexity of mathematical description of internal dynamic processes in the controlled object, are noted. The features of categorical concepts of automation of control of acoustic-emission diagnostics control are formulated and marked: control object, control system, control functions. The mathematical formalism of modeling the energy spectrum of acoustic emission signals is presented. A discrete and continuous approach to describing the oscillatory properties of the internal structure of a mat

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Perera-Lluna, Alexandre, Karthick Manivannan, Peng Xu, Ricardo Gutierrez-Osuna, Carl Benner, and B. Don Russell. "Automatic capacitor bank identification in power distribution systems." Electric Power Systems Research 111 (June 2014): 96–102. http://dx.doi.org/10.1016/j.epsr.2014.02.003.

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Noor, Amaleena Mohamad, Awang Jusoh Noorain, Zaw Htike Zaw, and Shoon Lei Win and. "BACTERIA IDENTIFICATION FROM MICROSCOPIC MORPHOLOGY: A SURVEY." International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 3, no. 2 (2014): 01–12. https://doi.org/10.5281/zenodo.6837164.

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Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. There have been several attempts to perform automatic background identification. This paper reviews state-of-the-art automatic bacteria identification techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria identification systems and recommends future direction of automatic bacteria identification.

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Amaleena, Mohamad, A. Jusoh Noorain, Zaw Htike Zaw, and Lei Win Shoon. "ADVANCES IN PROKARYOTE CLASSIFICATION FROM MICROSCOPIC IMAGES." Electrical & Computer Engineering: An International Journal (ECIJ) 3, no. 2 (2014): 13–24. https://doi.org/10.5281/zenodo.3385675.

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Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. There have been several attempts to perform automatic background identification. This paper reviews state-of-the-art automatic bacteria identification techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria identification systems and recommends future direction of automatic bacteria identification.

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Hussein, Enas. "Fingerprint Identification using Multiwavelet Transform." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 26, 2021): 131–38. http://dx.doi.org/10.55562/jrucs.v23i2.485.

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identification systems have been developed to achieve automatic identification of a person based on his physiological or behavioral characteristics. Biometric systems are critical in a wide range of applications such as banking system, E-commerce, smart cards, and access control to secure system. Automatic fingerprint identification is one of the most reliable biometric system, which is used for identifying persons. This study, aim is to design a fingerprint identification system, which is capable of identifying a fingerprint with high level of accuracy. Therefore, this system can be applied t

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Chernyi, Sergei. "Parametric Identification Components Maritime Systems of Automatic Control Systems with Microcontrollers." International Journal of Continuing Engineering Education and Life-Long Learning 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijceell.2021.10029856.

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Kovalčík, Jakub, and Michal Balog. "AUTOMATIC IDENTIFICATION SYSTEMS FOR MANAGEMENT - MATERIAL FLOW CONTROL AND STOCK STATUS." Acta logistica 8, no. 1 (2021): 55–64. http://dx.doi.org/10.22306/al.v8i1.202.

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The subject of the article is to define the current state of the information system, to describe the use of technology in practice and to understand the principle and benefits of smart logistics. To describe the operation of RFID technology and systems that are necessary for the effective operation of a comprehensive automatic identification system. The issue is to define the current state of information technology and create a project for automatic identification. The aim of the smart logistics project is the overall improvement of the production process, registration of parts and the overall

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Dhanamathi, A., K. Ajith, V. Balamurugan, and S. Sridhar. "A framework for wood quality assessment using DenseNet algorithm." i-manager’s Journal on Pattern Recognition 11, no. 1 (2024): 30. http://dx.doi.org/10.26634/jpr.11.1.21060.

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Wood defect detection is a critical aspect of quality control in the woodworking industry. This work introduces Deep Wood Inspect, a pioneering system that leverages the capabilities of deep learning for the precise identification and classification of defects in wooden materials. The proposed methodology utilizes densely connected Convolutional Neural Networks (CNNs), specifically DenseNet, to analyze high-resolution images of wood surfaces, providing an automated and efficient solution for defect detection. By integrating advanced image processing techniques with machine learning algorithms,

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vadlenka, Libor, Petra Jurankova, and Roman Hruska. "Challenges for automatic identification systems in the supply chain." International Journal of Learning and Change 11, no. 2 (2019): 1. http://dx.doi.org/10.1504/ijlc.2019.10014200.

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Hruska, Roman, Libor Svadlenka, and Petra Jurankova. "Challenges for automatic identification systems in the supply chain." International Journal of Learning and Change 11, no. 3 (2019): 252. http://dx.doi.org/10.1504/ijlc.2019.103324.

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Ntalampiras, Stavros. "Automatic identification of integrity attacks in cyber-physical systems." Expert Systems with Applications 58 (October 2016): 164–73. http://dx.doi.org/10.1016/j.eswa.2016.04.006.

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Ameli, Amir, Ali Hooshyar, Ehab F. El-Saadany, and Amr M. Youssef. "Attack Detection and Identification for Automatic Generation Control Systems." IEEE Transactions on Power Systems 33, no. 5 (2018): 4760–74. http://dx.doi.org/10.1109/tpwrs.2018.2810161.

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Dai, Hong Wei. "Automatic Recognition of Undersea Target Detection." Applied Mechanics and Materials 329 (June 2013): 457–60. http://dx.doi.org/10.4028/www.scientific.net/amm.329.457.

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In order to solve the problem of undersea detection target identification systems low recognition rate, this article introduces the momentum item and uses the invariable length of stride in the undersea detection system's neural network. Through computer simulation, results show that: improved identification system has been greatly improved in the recognition time. This is extremely advantageous to realize real-time automatic target detection undersea identification.

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