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Comparison of Deep Learning Techniques for Classification of the Insects in Order Level With Mobile Software Application
41/41 checkable references clean · checked 2026-07-21
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 41 checked references that resolve
resolves10.1016/S0168-1699(02)00012-1
Acquisition and analysis of migration data from the digitised display of a scanning entomological radar
resolves10.1016/j.compag.2019.104906
Crop pest classification based on deep convolutional neural network and transfer learning
resolves10.5513/JCEA01/15.4.1533
resolves10.1016/j.cropro.2013.10.022
Crop losses and the economic impact of insect pests on Brazilian agriculture
resolves10.1016/j.compag.2015.10.015
Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning
resolves10.1016/j.cretres.2020.104405
Small but important: A piece of mid-Cretaceous Burmese amber with a new genus and two new insect species (Odonata: Burmaphlebiidae & ‘Psocoptera’: Compsocidae)
resolves10.1002/fee.2190
Disturbance refugia within mosaics of forest fire, drought, and insect outbreaks
resolves10.1016/j.eswa.2014.11.051
An intelligent insect search system based on observation of the insect’s structure
resolves10.1016/j.compag.2018.08.001
Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review
resolves10.1016/j.ecoinf.2020.101135
Image-based insect species and gender classification by trained supervised machine learning algorithms
resolves10.1016/j.neucom.2016.12.038
A survey of deep neural network architectures and their applications
resolves10.1109/ICIP.2019.8803746
Insect Classification Using Squeeze-and-Excitation and Attention Modules - a Benchmark Study
resolves10.1117/12.2588672
Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms
resolves10.1016/j.compag.2020.105450
CNN feature based graph convolutional network for weed and crop recognition in smart farming
resolves10.1109/ICARCV.2018.8581103
Development of Application for Forest Insect Classification using CNN
resolves10.1109/ITME.2008.4744013
The application of AdaBoost-neural network in storedproduct insect classification
resolves10.1016/j.ecoinf.2019.101017
Image-based species identification of wild bees using convolutional neural networks
resolves10.1109/TENCON.2006.344144
Development of a classification algorithm for butterflies and ladybugs
resolves10.1109/ICNC.2010.5583156
Research on insect identification based on pattern recognition technology
resolves10.1109/ICCSCE.2017.8284406
Performance effect analysis for insect classification using convolutional neural network
resolves10.1007/978-3-030-34365-1_10
CDNN Model for Insect Classification Based on Deep Neural Network Approach
resolves10.3390/s18124169
Insect Detection and Classification Based on an Improved Convolutional Neural Network
resolves10.1093/sysbio/syz014
Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks
resolves10.1016/j.asoc.2014.11.046
Automatic identification of butterfly species based on local binary patterns and artificial neural network
resolves10.1016/j.biocon.2009.03.028
Odonata enter the biodiversity crisis debate: The first global assessment of an insect group
resolves10.1186/s12983-014-0070-0
Biodiversity, ecology, and behavior of the recently discovered insect order Mantophasmatodea
resolves10.1016/j.compag.2020.105522
AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection
resolves10.1016/j.asoc.2020.106533
A multi-task Faster R-CNN method for 3D vehicle detection based on a single image
resolves10.1109/IWCIA.2017.8203578
A new deep learning method based on AlexNet model and SSD model for tennis ball recognition
resolves10.3390/s22020464
Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs
resolves10.1007/978-3-540-31865-1_25
A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation
resolves10.3390/s20174938
Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD
The 5 references without a DOI — listed, not checked
no DOI — not checkedThe insects: An outline of entomology/Böcekler: Entomolojinin ana hatları
no DOI — not checkedThe Insects: An Outline of Entomology
no DOI — not checkedInsect pests recognition based on deep transfer learning models
no DOI — not checkedTensorflow: A system for large-scale machine learning
no DOI — not checkedLearning OpenCV: Computer Vision With the OpenCV Library
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