Works matching Classification of insects
Results: 2914
Leveraging Hyperspectral Images for Accurate Insect Classification with a Novel Two-Branch Self-Correlation Approach.
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- Agronomy, 2024, v. 14, n. 4, p. 863, doi. 10.3390/agronomy14040863
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A Classification method for Insects using Data Augmentation and Deep Neural Networks.
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- International Journal of Advanced Research in Computer Science, 2024, v. 15, n. 2, p. 21, doi. 10.26483/ijarcs.v15i2.7070
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Polymorphic Clustering and Approximate Masking Framework for Fine-Grained Insect Image Classification.
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- Electronics (2079-9292), 2024, v. 13, n. 9, p. 1691, doi. 10.3390/electronics13091691
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Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion.
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- Applied Sciences (2076-3417), 2019, v. 9, n. 19, p. 4097, doi. 10.3390/app9194097
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Flying Insect Classification with Inexpensive Sensors.
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- Journal of Insect Behavior, 2014, v. 27, n. 5, p. 657, doi. 10.1007/s10905-014-9454-4
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Improved Artificial Ecosystem Optimizer with Deep-Learning-Based Insect Detection and Classification for Agricultural Sector.
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- Sustainability (2071-1050), 2023, v. 15, n. 20, p. 14770, doi. 10.3390/su152014770
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Lightweight Field Insect Recognition and Classification Model Based on Improved Deep Learning under Complex Background.
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- Security & Communication Networks, 2023, p. 1, doi. 10.1155/2023/6560747
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A COMPARATIVE PERFORMANCE EVALUATION OF VARIOUS CLASSIFICATION MODELS FOR DETECTION AND CLASSIFICATION OF FLYING INSECTS.
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- Interdisciplinary Description of Complex Systems, 2023, v. 21, n. 1, p. 52, doi. 10.7906/indecs.21.1.5
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Enhancing Insect Sound Classification Using Dual-Tower Network: A Fusion of Temporal and Spectral Feature Perception.
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- Applied Sciences (2076-3417), 2024, v. 14, n. 7, p. 3116, doi. 10.3390/app14073116
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The higher classification of southern African insects.
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- African Entomology, 2016, v. 24, n. 2, p. 545, doi. 10.4001/003.024.0545
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Next generation insect taxonomic classification by comparing different deep learning algorithms.
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- PLoS ONE, 2022, v. 17, n. 12, p. 1, doi. 10.1371/journal.pone.0279094
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ARTIFICIAL INTELLIGENCE FOR CLASSIFICATION AND DETECTION OF MAJOR INSECT PESTS OF BRINJAL.
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- Indian Journal of Entomology, 2023, v. 85, n. 3, p. 563, doi. 10.55446/IJE.2023.1388
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Classification of flying insects in polarimetric weather radar using machine learning and aphid trap data.
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- Atmospheric Measurement Techniques Discussions, 2023, p. 1, doi. 10.5194/amt-2023-69
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Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm.
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- Intelligent Automation & Soft Computing, 2023, v. 36, n. 2, p. 1881, doi. 10.32604/iasc.2023.031573
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Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 ( COI) DNA barcode sequences using a naïve Bayesian classifier.
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- Molecular Ecology Resources, 2014, v. 14, n. 5, p. 929, doi. 10.1111/1755-0998.12240
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Image‐based taxonomic classification of bulk insect biodiversity samples using deep learning and domain adaptation.
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- Systematic Entomology, 2023, v. 48, n. 3, p. 387, doi. 10.1111/syen.12583
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Cutting-edge ensemble framework of deep convolutional neural networks for high-precision insect pest classification.
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- Journal of Plant Diseases & Protection, 2024, v. 131, n. 6, p. 2207, doi. 10.1007/s41348-024-00986-y
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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops.
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- Computer Systems Science & Engineering, 2023, v. 46, n. 3, p. 3847, doi. 10.32604/csse.2023.036552
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Machine learning ensemble with image processing for pest identification and classification in field crops.
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- Neural Computing & Applications, 2021, v. 33, n. 13, p. 7491, doi. 10.1007/s00521-020-05497-z
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- Article
Stridulatory Organs and Sound Recognition of Three Species of Longhorn Beetles (Coleoptera: Cerambycidae).
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- Insects (2075-4450), 2024, v. 15, n. 11, p. 849, doi. 10.3390/insects15110849
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A Systematic Review on Automatic Insect Detection Using Deep Learning.
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- Agriculture; Basel, 2023, v. 13, n. 3, p. 713, doi. 10.3390/agriculture13030713
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A Multi-Layer Feature Fusion Method for Few-Shot Image Classification.
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- Sensors (14248220), 2023, v. 23, n. 15, p. 6880, doi. 10.3390/s23156880
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GÖRÜNTÜ ÖN İŞLEME TEKNİKLERİ VE DERİN ÖĞRENME İLE BİTKİ ZARARLILARININ SINIFLANDIRILMASI.
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- SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi, 2024, v. 12, n. 2, p. 455, doi. 10.21923/jesd.1490176
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- Article
Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification.
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- Agronomy, 2022, v. 12, n. 8, p. N.PAG, doi. 10.3390/agronomy12081733
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Turning wingbeat sounds into spectrum images for acoustic insect classification.
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- Electronics Letters (Wiley-Blackwell), 2017, v. 53, n. 25, p. 1674, doi. 10.1049/el.2017.3334
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Species-Specific Discrimination of Insect Meals for Aquafeeds by Direct Comparison of Tandem Mass Spectra.
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- Animals (2076-2615), 2019, v. 9, n. 5, p. 222, doi. 10.3390/ani9050222
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Detection and recognition of the invasive species, Hylurgus ligniperda, in traps, based on a cascaded convolution neural network.
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- Pest Management Science, 2024, v. 80, n. 9, p. 4223, doi. 10.1002/ps.8126
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Detecting common coccinellids found in sorghum using deep learning models.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-36738-5
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Acoustic Classification of Mosquitoes using Convolutional Neural Networks Combined with Activity Circadian Rhythm Information.
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- International Journal of Interactive Multimedia & Artificial Intelligence, 2021, v. 7, n. 2, p. 59, doi. 10.9781/ijimai.2021.08.009
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Taxonomy of Insects in San Juan, Ilocos Sur, Philippines.
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- Asian Journal of Biodiversity, 2023, v. 14, n. 1, p. 55, doi. 10.7828/ajob.v14i1.1546
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Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection.
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- PLoS ONE, 2023, v. 18, n. 10, p. 1, doi. 10.1371/journal.pone.0286732
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Deep Learning Based Detector YOLOv5 for Identifying Insect Pests.
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- Applied Sciences (2076-3417), 2022, v. 12, n. 19, p. 10167, doi. 10.3390/app121910167
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Insect Detection and Classification Based on an Improved Convolutional Neural Network.
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- Sensors (14248220), 2018, v. 18, n. 12, p. 4169, doi. 10.3390/s18124169
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Using Web-Based Key Character and Classification Instruction for Teaching Undergraduate Students Insect Identification.
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- Journal of Science Education & Technology, 2013, v. 22, n. 4, p. 509, doi. 10.1007/s10956-012-9410-z
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(DNA) تق ويم كفاءة أربع ط ا رئق مختلفة لاستخلاص الحمض النووي الريبي المنزوع الأوكسيجين Sitophilus granarius من سوسة القمح
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- Arab Journal of Plant Protection, 2019, v. 37, n. 4, p. 359, doi. 10.22268/AJPP-037.4.359364
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Collecting insects in South Sudan.
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- Bulletin of the Amateur Entomologists Society, 2018, v. 77, n. 528, p. 54
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Comparison of deductive classification techniques for predicting potential spatial distribution of quarantine insects.
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- Revista de la Sociedad Entomológica Argentina, 2019, v. 78, n. 4, p. 14, doi. 10.25085/rsea.780403
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- Article
Capacity building for freshwater insect studies in northern Patagonia, Argentina: DARWIN Initiative programme.
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- Revista de la Sociedad Entomológica Argentina, 2009, v. 68, n. 1/2, p. 145
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The Rutgers University Insect Collection (1888-2019): History of a New Jersey Treasure Twice Saved.
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- New Jersey Studies: An Interdisciplinary Journal, 2019, v. 5, n. 2, p. 185, doi. 10.14713/njs.v5i2.172
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Classifying the unknown: Insect identification with deep hierarchical Bayesian learning.
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- Methods in Ecology & Evolution, 2023, v. 14, n. 6, p. 1515, doi. 10.1111/2041-210X.14104
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- Article
Technological Advances to Address Current Issues in Entomology: 2020 Student Debates.
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- Journal of Insect Science, 2021, v. 21, n. 2, p. 1, doi. 10.1093/jisesa/ieab025
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- Article
Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy.
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- Foods, 2022, v. 11, n. 21, p. 3524, doi. 10.3390/foods11213524
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Automatic acoustic recognition of pollinating bee species can be highly improved by Deep Learning models accompanied by pre-training and strong data augmentation.
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- Frontiers in Plant Science, 2023, p. 1, doi. 10.3389/fpls.2023.1081050
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Insect detect: An open-source DIY camera trap for automated insect monitoring.
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- PLoS ONE, 2024, v. 19, n. 4, p. 1, doi. 10.1371/journal.pone.0295474
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- Article
Improved YOLOv8 and SAHI Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea.
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- Agronomy, 2024, v. 14, n. 5, p. 1034, doi. 10.3390/agronomy14051034
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Fast and Powerful: Biomechanics and Bite Forces of the Mandibles in the American Cockroach Periplaneta americana.
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- PLoS ONE, 2015, v. 10, n. 10, p. 1, doi. 10.1371/journal.pone.0141226
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Self‐supervised learning improves classification of agriculturally important insect pests in plants.
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- Plant Phenome Journal, 2023, v. 6, n. 1, p. 1, doi. 10.1002/ppj2.20079
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- Article
A ResNet attention model for classifying mosquitoes from wing-beating sounds.
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- Scientific Reports, 2022, v. 12, n. 1, p. 1, doi. 10.1038/s41598-022-14372-x
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Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network.
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- Crop & Pasture Science, 2023, v. 74, n. 6, p. 615, doi. 10.1071/CP21710
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Implementation of an Intelligent Trap for Effective Monitoring and Control of the Aedes aegypti Mosquito.
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- Sensors (14248220), 2024, v. 24, n. 21, p. 6932, doi. 10.3390/s24216932
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