2023
DOI: 10.33633/jcta.v1i2.9443
|View full text |Cite
|
Sign up to set email alerts
|

Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation

Harish Trio Adityawan,
Omar Farroq,
Stefanus Santosa
et al.

Abstract: Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 26 publications
0
1
0
Order By: Relevance
“…Advantages include efficient transfer learning, leveraging knowledge from large datasets, and overcoming data limitations. Many popular pre-trained models exist, such as VGG, DenseNet, Inception, ResNet, NasNet, Xception, MobileNet, etc [22]- [27]. MobileNetV2, a variant of MobileNet, is a model specifically designed for mobile applications with high computational efficiency and small model size.…”
Section: Introductionmentioning
confidence: 99%
“…Advantages include efficient transfer learning, leveraging knowledge from large datasets, and overcoming data limitations. Many popular pre-trained models exist, such as VGG, DenseNet, Inception, ResNet, NasNet, Xception, MobileNet, etc [22]- [27]. MobileNetV2, a variant of MobileNet, is a model specifically designed for mobile applications with high computational efficiency and small model size.…”
Section: Introductionmentioning
confidence: 99%