InsectMamba: Insect Pest Classification with State Space Model

Bibliographic Details
Title:	InsectMamba: Insect Pest Classification with State Space Model
Authors:	Wang, Qianning, Wang, Chenglin, Lai, Zhixin, Zhou, Yucheng
Publication Year:	2024
Collection:	Computer Science
Subject Terms:	Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
More Details:	The classification of insect pests is a critical task in agricultural technology, vital for ensuring food security and environmental sustainability. However, the complexity of pest identification, due to factors like high camouflage and species diversity, poses significant obstacles. Existing methods struggle with the fine-grained feature extraction needed to distinguish between closely related pest species. Although recent advancements have utilized modified network structures and combined deep learning approaches to improve accuracy, challenges persist due to the similarity between pests and their surroundings. To address this problem, we introduce InsectMamba, a novel approach that integrates State Space Models (SSMs), Convolutional Neural Networks (CNNs), Multi-Head Self-Attention mechanism (MSA), and Multilayer Perceptrons (MLPs) within Mix-SSM blocks. This integration facilitates the extraction of comprehensive visual features by leveraging the strengths of each encoding strategy. A selective module is also proposed to adaptively aggregate these features, enhancing the model's ability to discern pest characteristics. InsectMamba was evaluated against strong competitors across five insect pest classification datasets. The results demonstrate its superior performance and verify the significance of each model component by an ablation study. Comment: 13 pages, 5 figures
Document Type:	Working Paper
Access URL:	http://arxiv.org/abs/2404.03611
Accession Number:	edsarx.2404.03611
Database:	arXiv

FullText	Text: Availability: 0 CustomLinks: – Url: http://arxiv.org/abs/2404.03611 Name: EDS - Arxiv Category: fullText Text: View this record from Arxiv MouseOverText: View this record from Arxiv – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsarx&genre=article&issn=&ISBN=&volume=&issue=&date=20240404&spage=&pages=&title=InsectMamba: Insect Pest Classification with State Space Model&atitle=InsectMamba%3A%20Insect%20Pest%20Classification%20with%20State%20Space%20Model&aulast=Wang%2C%20Qianning&id=DOI: Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries
Header	DbId: edsarx DbLabel: arXiv An: edsarx.2404.03611 RelevancyScore: 1098 AccessLevel: 3 PubType: Report PubTypeId: report PreciseRelevancyScore: 1098.01599121094
IllustrationInfo
Items	– Name: Title Label: Title Group: Ti Data: InsectMamba: Insect Pest Classification with State Space Model – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Qianning%22">Wang, Qianning</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Chenglin%22">Wang, Chenglin</searchLink><br /><searchLink fieldCode="AR" term="%22Lai%2C+Zhixin%22">Lai, Zhixin</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Yucheng%22">Zhou, Yucheng</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+Science+-+Computer+Vision+and+Pattern+Recognition%22">Computer Science - Computer Vision and Pattern Recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Artificial+Intelligence%22">Computer Science - Artificial Intelligence</searchLink> – Name: Abstract Label: Description Group: Ab Data: The classification of insect pests is a critical task in agricultural technology, vital for ensuring food security and environmental sustainability. However, the complexity of pest identification, due to factors like high camouflage and species diversity, poses significant obstacles. Existing methods struggle with the fine-grained feature extraction needed to distinguish between closely related pest species. Although recent advancements have utilized modified network structures and combined deep learning approaches to improve accuracy, challenges persist due to the similarity between pests and their surroundings. To address this problem, we introduce InsectMamba, a novel approach that integrates State Space Models (SSMs), Convolutional Neural Networks (CNNs), Multi-Head Self-Attention mechanism (MSA), and Multilayer Perceptrons (MLPs) within Mix-SSM blocks. This integration facilitates the extraction of comprehensive visual features by leveraging the strengths of each encoding strategy. A selective module is also proposed to adaptively aggregate these features, enhancing the model's ability to discern pest characteristics. InsectMamba was evaluated against strong competitors across five insect pest classification datasets. The results demonstrate its superior performance and verify the significance of each model component by an ablation study.<br />Comment: 13 pages, 5 figures – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2404.03611" linkWindow="_blank">http://arxiv.org/abs/2404.03611</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2404.03611
PLink	https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2404.03611
RecordInfo	BibRecord: BibEntity: Subjects: – SubjectFull: Computer Science - Computer Vision and Pattern Recognition Type: general – SubjectFull: Computer Science - Artificial Intelligence Type: general Titles: – TitleFull: InsectMamba: Insect Pest Classification with State Space Model Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Qianning – PersonEntity: Name: NameFull: Wang, Chenglin – PersonEntity: Name: NameFull: Lai, Zhixin – PersonEntity: Name: NameFull: Zhou, Yucheng IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 04 Type: published Y: 2024
ResultId	1