  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.
