2019
DOI: 10.1007/978-3-030-34365-1_10
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CDNN Model for Insect Classification Based on Deep Neural Network Approach

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Cited by 4 publications
(3 citation statements)
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“…Huynh et al [16] developed the CDNN technique for insect classification related to NN and DL. Firstly, insect images are gathered and extracted according to the Dense Scale-Invariant Feature Transform.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Huynh et al [16] developed the CDNN technique for insect classification related to NN and DL. Firstly, insect images are gathered and extracted according to the Dense Scale-Invariant Feature Transform.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Despite these advances, research regarding the automatic detection and classification of ladybird beetles is scarce in the literature. Examples using deep learning classifiers as in [ 28 , 31 , 33 ] provide good performance but incur in high computational costs as they skip the classification space reduction, for example, sometimes preferring to slide a subwindow into the whole image to attempt any detection or classification task. On the other hand, classifiers based on shallow learning as in [ 34 , 35 ] are less complex but with inferior performances.…”
Section: Introductionmentioning
confidence: 99%
“…In [ 30 ], a combination of a deep CNN ResNet and an SVM model was used for feature extraction and classification of insect pest species, respectively, obtaining an ACC value of 49.5%. Similarly, in [ 31 ], dense scale-invariant features and a deep CNN model was employed to classify brown plant-hoppers and ladybirds in rice crops, attaining an ACC score of 97%. In [ 42 ], a combination of You Only Look Once (YOLO) and SVM models were employed to segment and classify six species of flying insects, reaching ACC scores of 93.71% and 92.50% in the segmentation and classification stages, respectively.…”
Section: Introductionmentioning
confidence: 99%