Bibliographies thématiques / Pest Classification

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Littérature scientifique sur le sujet « Pest Classification »

Auteur : Grafiati
Publié le 2 juin 2025
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Pest Classification"

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Sushma D S, Mohammed Alqhama, Aravind M, Jayanth A B, and Rakshith Kumar K. "Pest Detection and Classification in Peanut Crops." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (2024): 1372–79. http://dx.doi.org/10.47392/irjaem.2024.0189.

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Recent advancements in image processing have significantly improved pest detection and classification in peanut crops. Our study introduces an innovative approach that optimizes image features for accurate pest identification. Leveraging insights from successful image analysis methodologies, our model employs a tailored architecture for pest detection, segmentation, and classification tasks. By integrating dual branch segment representations and a dual-layer transformer encoder, we aim to enhance image representations and consolidate pest image segments of varying sizes. We evaluate our approa
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K.H, Sandeep. "Crop and Pest Classification Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43290.

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Crop pests pose a hazard to agriculture by lowering yields and creating large losses. Timely intervention depends on prompt and precise pest identification. Convolutional Neural Networks (CNNs), a type of deep learning, are used in this study to effectively classify pests. To improve performance, the method places a strong emphasis on image preprocessing, accurate pest segmentation, and transfer learning. The algorithm is trained on a large dataset of photos of pests and non-pests to find distinctive characteristics for precise categorization. With an emphasis on improved image quality, segmen
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Myat, Mon Kyaw, San Nwe San, and Myint Yee Myint. "Pest Classification and Pesticide Recommendation System." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2187–91. https://doi.org/10.5281/zenodo.3591203.

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Myanmar is an agricultural country and agriculture constitutes the largest sector of the economy. Recognizing of pests is a vital problem especially for farmers, agricultural researchers, and environmentalists. The proposed system is to classify the types of pest using the CNN model, which is often used when applying deep learning to image processing, and to recommend the most suitable pesticide according to the type of pest. This system will help to know easily information of pests and pesticides which should be used to the user. Using a public dataset of 1265 images of pests, a convolutional
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Doan, Thanh-Nghi. "Large-Scale Insect Pest Image Classification." Journal of Advances in Information Technology 14, no. 2 (2023): 328–41. http://dx.doi.org/10.12720/jait.14.2.328-341.

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Nyrop, Jan P., Michael R. Binns, and Wopke van der Werf. "Sampling for IPM Decision Making: Where Should We Invest Time and Resources?" Phytopathology® 89, no. 11 (1999): 1104–11. http://dx.doi.org/10.1094/phyto.1999.89.11.1104.

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Guides for making crop protection decisions based on assessments of pest abundance or incidence are cornerstones of many integrated pest management systems. Much research has been devoted to developing sample plans for use in these guides. The development of sampling plans has usually focused on collecting information on the sampling distribution of the pest, describing this sampling distribution with a mathematical model, formulating a sample plan, and sometimes, but not always, evaluating the performance of the proposed sample plan. For crop protection decision making, classification of dens
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P, Venkatasaichandrakanth, and Iyapparaja M. "GROUNDNUT CROP PEST DETECTION AND CLASSIFICATION USING COMPREHENSIVE DEEP-LEARNING MODELS." Suranaree Journal of Science and Technology 31, no. 1 (2024): 020028(1–17). http://dx.doi.org/10.55766/sujst-2024-01-e02544.

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Pests pose a significant threat to crops, leading to substantial economic losses and decreased food production. Early detection and accurate classification of pests in crops are crucial for effective pest management strategies. In this study, we propose a method for pest detection and classification in groundnut crops using deep learning models. In this research, we compare the performance of three deep learning models, namely Custom CNN [proposed], LeNet-5, and VGG-16, for groundnut pest detection and classification. A comprehensive dataset containing images of diverse groundnut crop pests, i
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Mr.R., Madanachitran. "DEEP LEARNING-BASED PEST CLASSIFICATION FOR PESTICIDE RECOMMENDATION IN AGRICULTURAL SYSTEMS." International Journal of Advances in Engineering & Scientific Research 10, no. 1 (2023): 17–27. https://doi.org/10.5281/zenodo.14924841.

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<em>Pest identification plays a crucial role in agricultural pest management, influencing pesticide selection and crop protection strategies. This study introduces Deep Pest Net, a deep learning-based model designed for efficient pest classification and pesticide recommendation. The proposed methodology consists of four key steps: data augmentation, image resizing, dataset partitioning, and model training/testing. To overcome data scarcity, augmentation techniques such as rotation, scaling, and translation were applied, enhancing model generalization. The DeepPestNet architecture comprises ele
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Binns, Michael R., Jan P. Nyrop, and Wopke Van Der Werf. "Monitoring Pest Abundance by Cascading Density Classification." American Entomologist 42, no. 2 (1996): 113–21. http://dx.doi.org/10.1093/ae/42.2.113.

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D. R, Mrs Dr Thirupurasundari. "Agriculture Pest Classification using Deep CNN Model." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 307–17. https://doi.org/10.22214/ijraset.2025.68207.

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Agricultural pests are spurs of economic, social and sorrowful environmental impacts around the globe. To control these pests proper identification and categorization is prudent in strategies used to tackle them. This work highlights the DeepPestNet, a CNN built specifically for accurately identifying nine classes of pests important in agriculture. Base onto the transfer learning of EfficientNetB0 which was developed to boost the performance of pest recognition, DeepPestNet has more convolutional and attention layers incorporated into the framework. Training and evaluation on this broad set sh
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S. Sabapathi, N. Vijayalakshmi. "A Unified Deep Learning Framework for Accurate Pest Detection and Classification in Agriculture." Journal of Information Systems Engineering and Management 10, no. 31s (2025): 599–612. https://doi.org/10.52783/jisem.v10i31s.5115.

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Introduction: Agriculture is an important role in sustaining human life and ensuring high quality food production is essential for economic growth. Among the main difficulties farmers encounter the rapid spread of insect and pest infestations which can significantly impact crop yields. Objectives: While, existing approaches have explored pest detection and classification, often suffer from inaccuracies and inefficiencies. To address these issues, this paper propose a unified Approach for PEST detection and classification model called SAMYNET (Segment Anything Model + YOLO8 + EfficientNet syste
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Thèses sur le sujet "Pest Classification"

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Nguyen, Trung Dung. "Economic and environmental effects of Integrated Pest Management program: A case study of Hau Giang province (Mekong Delta)." Technische Universität Dresden, 2018. https://tud.qucosa.de/id/qucosa%3A32720.

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Since many years, the agro-technical programs such as '3 reduction 3 increase', '1 must 5 reduction' and System of Rice Intensification (SRI) have been carried out with definite results. Recently, IPM program (integrated pest management) – a more comprehensive measure (using all possible techniques and methods to keep the pest populations below a level causing economic injury) – has been firstly piloted on a large scale in the Mekong Delta. This paper presents the main results of piloting IPM program in 2,610 hectares during 2014-2017 in Hau Giang province. As results, farmers have better econ
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Georgi, Richard, Klara Pohlink, and Michael Müller. "Einfluss des Pappelblattkäfers in Kurzumtriebsplantagen." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-237215.

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Der Wiederaustrieb beernteter Pappeln in Kurzumtriebsplantagen erfolgt nicht selten stark verzögert. Ob und inwieweit hierbei der Große Rote Pappelblattkäfer eine Rolle spielt, wurde in einem Versuch mit zwei unterschiedlichen Dichten des Schadinsekts untersucht.
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Sun, Feng. "Computational Hydrosystem Analysis: Applications to the Meijiang and Nankou Catchments in China." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-70417.

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Hydrosystems are important elements of the hydraulic cycle. With population growth and climatic change impacting water resources, the existing water transport systems need to be reproduced in order to optimize the usage of the precious water resources. The individual flow process i.e. unsaturated soil flow and groundwater flow in porous and fractured media can be expressed by partial differential equations mathematically. The numerical models are generally used to give solutions of these equations with specific conditions. In this work, the numerical solutions are carried out using the scient
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Černocký, Tomáš. "Řízení rizik ve stavebním podniku." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2013. http://www.nusl.cz/ntk/nusl-225973.

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The thesis deals with risk management in the construction company. There are described the concepts related to the issue, that is characteristic of the construction company, risk classification, marking out the risk factors, the determination of significance of risk factors and identification of potential risk factors, consequences on the economic situation of the construction company. In the practical part there are certain risk factors analyzed in the selected building company. In addition to their significance are determined based on the method of risk assessment matrix and consequently the
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Anwar, Syaiful. "Characterization and Classification of Tropical Peat Soils." Kyoto University, 2003. http://hdl.handle.net/2433/148569.

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Kyoto University (京都大学)<br>0048<br>新制・課程博士<br>博士(農学)<br>甲第10388号<br>農博第1367号<br>新制||農||878(附属図書館)<br>学位論文||H15||N3841(農学部図書室)<br>UT51-2003-L29<br>京都大学大学院農学研究科地域環境科学専攻<br>(主査)教授 小﨑 隆, 教授 櫻谷 哲夫, 教授 東 順一<br>学位規則第4条第1項該当
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Georgi, Richard, Christiane Helbig, Michael Müller, Karolin Graichen, and Martin Schubert. "Blattwespen-Massenvermehrung in Kurzumtriebsplantagen mit Pappel." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-115790.

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Bei dem Begang einer Kurzumtriebsplantage bei Großschirma (Sachsen) im August 2011 fiel in einem 500 m x 25 m breiten Streifen der Sorte Max 4 starker Fraß durch Blattwespenlarven auf. Andere Sorten waren nur an den Randbereichen zu Max betroffen. Die Blattwespen hatten, von unten beginnend, 50 % bis 70 % der dreijährigen Pappeltriebe kahl gefressen. Der Hymenoptera-Experte Dr. A. Taeger vom Senkenberg-Institut in Müncheberg bestimmte die Art als Nematus papillosus (Retzius, 1783) (Syn. Nematus melanapsis Hartig 1840). 2012 breitete sich die Massenvermehrung rasch auf angrenzende Flächen aus u
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Georgi, Richard, Christiane Helbig, and Martin Schubert. "Der Rote Pappelblattkäfer in Kurzumtriebsplantagen." Deutscher Landwirtschaftsverlag, 2012. https://tud.qucosa.de/id/qucosa%3A26055.

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Kurzumtriebsplantagen (KUP) bieten für eine Vielzahl von Organismen einen idealen Lebensraum. Einige Insektenarten reagieren darauf mit einer massenhaften Vermehrung. Besonders der Einfluss des Roten Pappelblattkäfers (Chrysomela (=Melasoma) populi) führte in den vergangenen Jahren vermehrt zur Schädigung von KUP. Bisher nehmen die Schäden noch selten bestandesbedrohende Ausmaße an, sind in der Tendenz jedoch klar zunehmend. Immer häufiger kommt es zu relevanten Zuwachsverlusten, verzögertem Austrieb und dem Ausfall einzelner Pflanzen. Daher werden im Rahmen des BMBF-Verbundprojektes „AgroForN
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Helbig, Christiane, Richard Georgi, and Michael Müller. "Die Weidenblattkäfer an Weiden und Pappeln im Kurzumtrieb." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-115903.

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Während der letzten Jahre ist die mit Kurzumtriebsplantagen bestockte Fläche in Deutschland deutlich angestiegen. Derzeit geht man von etwa 5 000 ha aus [1]. Der Hauptteil dieser Flächen ist mit Pappeln bestockt, während Weiden einen geringeren Anteil einnehmen. In anderen Ländern wie Großbritannien oder Schweden ist das Verhältnis dagegen umgekehrt und es finden sich große, zusammenhängende Weidenkulturen. Hier tritt der Blaue Weidenblattkäfer (Phratora vulgatissima) schon seit Jahren als Hauptschadinsekt auf und verursacht die Entlaubung ganzer Plantagen. Auch in Deutschland ist der Blaue We
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Sharif, Mhd Saeed. "An artificial intelligent system for oncological volumetric medical PET classification." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/13095.

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Positron emission tomography (PET) imaging is an emerging medical imaging modality. Due to its high sensitivity and ability to model physiological function, it is effective in identifying active regions that may be associated with different types of tumour. Increasing numbers of patient scans have led to an urgent need for the development of new efficient data analysis system to aid clinicians in the diagnosis of disease and save decent amount of the processing time, as well as the automatic detection of small lesions. In this research, an automated intelligent system for oncological PET volum
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Mårtensson, Fredrik, and Erik Westberg. "Prioritization of Informative Regions in PET Scans for Classification of Alzheimer's Disease." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45370.

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Alzheimer’s Disease (AD) is a widespread neurodegenerative disease. The disease causes brain atrophy, resulting in memory loss, decreased cognitive ability, and eventually death. There is currently no cure for the disease, but treatment may delay the onset. Therefore, it is crucial to detect the disease at an early stage. Medical imaging techniques, such as Positron Emission Tomography (PET), are heavily applied for this task. In recent years, machine learning approaches have shown success in identifying AD from such images. The thesis presents a pipeline approach to detect, extract and evalua
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Livres sur le sujet "Pest Classification"

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(Korea), Kungnip Minsok Pangmulgwan, ed. Pangmulgwan kwa yuhae saengmul kawlli =: Museum and pest management. Kungnip Minsok Pangmulgwan, 2008.

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(Korea), Kungnip Minsok Pangmulgwan, ed. Pangmulgwan kwa yuhae saengmul kawlli =: Museum and pest management. Kungnip Minsok Pangmulgwan, 2008.

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(Korea), Kungnip Minsok Pangmulgwan, ed. Pangmulgwan kwa yuhae saengmul kawlli =: Museum and pest management. Kungnip Minsok Pangmulgwan, 2008.

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Ghosh, L. K. Handbook on Hemipteran pests in India. Zoological Survey of India, 2008.

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Ėrtevt͡si͡an, E. K. Ėnt͡sirtidy (Encyrtidae) Armi͡anskoĭ SSR. Izd-vo AN Armi͡anskoĭ SSR, 1986.

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Reeves-Stevens, Garfield. Life: How are they the same? Gage, 1990.

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Gdalich, Dolin Vladimir, and Ukraïnsʹke entomolohichne tovarystvo, eds. Ėkologii͡a︡ i taksonomii͡a︡ nasekomykh Ukrainy: Sbornik nauchnykh trudov. Nauk. dumka, 1988.

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Wilson Sampaio de Azevedo Filho. Cigarrinhas de citros no Rio Grande do Sul: Taxonomia. EDIPUCRS, 2006.

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Wilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. EDIPUCRS, 2004.

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Wilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. EDIPUCRS, 2004.

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Chapitres de livres sur le sujet "Pest Classification"

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Rakhonde, Vinith, K. Srujan Raju, Nuthanakanti Bhaskar, and A. Raji Reddy. "Automated Pest Detection Using Image Classification." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9442-7_68.

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Wu, Jinglin, Shiqi Liang, Ning Bi, and Jun Tan. "Attention Embedding ResNet for Pest Classification." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09037-0_48.

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Dharmasastha, K. N. S., K. Sharmila Banu, G. Kalaichevlan, B. Lincy, and B. K. Tripathy. "Classification of Pest in Tomato Plants Using CNN." In Meta Heuristic Techniques in Software Engineering and Its Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11713-8_6.

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Malik, Parveen, and Manoj Kumar Parida. "Classification of Insect Pest Using Transfer Learning Mechanism." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58535-7_7.

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Kathole, Atul B., Sonali D. Patil, Kapil N. Vhatkar, Dinesh Chaudhari, and Avinash P. Jadhav. "Pest identification and classification using IoT enable technique." In Internet of Things in Modern Computing. CRC Press, 2023. http://dx.doi.org/10.1201/9781003407300-11.

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Kala, K. U., M. Nandhini, M. N. Kishore Chakkravarthi, M. Thangadarshini, and S. Madhusudhana Verma. "WOAGE-MobileNetV2 Model for Plantain Tree Pest Classification and Recognition." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5081-8_6.

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Elakya, R., and T. Manoranjitham. "Pest Classification in Paddy by Using Deep ConvNets and VGG19." In Recent Trends in Computational Intelligence and Its Application. CRC Press, 2023. http://dx.doi.org/10.1201/9781003388913-10.

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Pattnaik, Gayatri, and K. Parvathi. "A Review on Advanced Techniques on Plant Pest Detection and Classification." In Smart Intelligent Computing and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9282-5_63.

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Bellei, E., D. Guidotti, R. Petacchi, L. M. Reyneri, and I. Rizzi. "Evaluation, Classification and Clustering with NeuroFuzzy Techniques in Integrate Pest Management." In Bio-Inspired Applications of Connectionism. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45723-2_74.

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Chen, Chia-Pang, Min-Sheng Liao, and Joe-Air Jiang. "Adaptive Classification of Special Events in Agroecological Monitoring Systems for Pest Management." In Smart Sensors, Measurement and Instrumentation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36365-8_11.

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Actes de conférences sur le sujet "Pest Classification"

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Aziz, Faruq, Daniati Uki Eka Saputri, Sri Hadianti, Daning Nur Sulistyowati, Siti Fauziah, and Nissa Almira Mayangky. "Image Enhancement with DBPN for Plant Pest Classification." In 2024 International Conference on Information Technology Research and Innovation (ICITRI). IEEE, 2024. http://dx.doi.org/10.1109/icitri62858.2024.10698823.

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S, Senthil Pandi, Reshmy A. K, Vinodh Kumar S, and Kumar P. "Effective Deep Learning Framework for Crop Pest Classification." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690463.

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Thuse, Sanjyot, Meena Chavan, and Shreyasi Watve. "Pest Classification with BoVW Using SIFT and ORB." In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET). IEEE, 2025. https://doi.org/10.1109/icaet63349.2025.10932325.

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Kumar, M. Sirish, R. Usha, B. Rama Kantha Reddy, S. Ramachandra, P. Jaya Prakash, and B. Swetha. "Deep Learning Driven - Pest Classification and Sustainable Agriculture." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10940311.

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Akhil, Pamarthi, Pennera Dinesh Kumar, and Lakshmi Priya S. "Automated Pest Classification of Paddy Field using Drone Imagery." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013354.

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Yanli, Tan, Azliza Mohd Ali, Sharifalillah Nordin, Wang Jin, and Li Guoqin. "Crop Pest and Diseases Classification Using ResNet and Inception Network." In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10729964.

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Longo, Antonello, Maria Rizzi, Pierluigi Longo, Germano Pansini, and Cataldo Guaragnella. "Crop Monitoring via Deep-Learning-Based Pest Classification for Pesticide Reduction." In 2024 IEEE International Humanitarian Technologies Conference (IHTC). IEEE, 2024. https://doi.org/10.1109/ihtc61819.2024.10855066.

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Vyas, Soham, Gunjan Thakur, Rajeev Gupta, and Honey Mehta. "IoT based Soyabean Pest Classification using Transfer Learning and Explainable AI." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677291.

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Sharma, Jatin. "Efficiency and Scalability of EfficientNetV2-L for Large-Scale Pest Classification." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10924022.

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Bhuvaneswari, P., K. Srilatha, A. Kavya, T. Anitha, and V. Sasikala. "Enhanced Plant Pest Classification by Leveraging CBAM Attention in ResNet-9." In 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR). IEEE, 2025. https://doi.org/10.1109/stcr62650.2025.11020548.

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Rapports d'organisations sur le sujet "Pest Classification"

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Lemly, Joanna, Lauren Laughlin, and Gabrielle Smith. Inventory of fens and rare fen-indicator plant occurrences at Grand Teton National Park and John D. Rockefeller, Jr. Memorial Parkway. National Park Service, 2025. https://doi.org/10.36967/2313526.

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Fens are groundwater-fed, peat-accumulating wetlands that form where the accumulation of organic material exceeds decomposition. While fens are common at northern latitudes, fens in the Rocky Mountains are relatively rare, small features that support numerous rare plant species. Little is known about fens in Grand Teton National Park (GRTE) and John D. Rockefeller, Jr. Memorial Parkway (JODR). While GRTE and JODR are managed for natural resource preservation and recreation, the parks have a history of other land uses and have seen increasing visitors in recent years. To better manage fen resou
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