mlsanityData to 14 Oct 2026

InsectMamba: Insect Pest Classification with State Space Model

arXiv 2404.03611 ↗First posted 4 Apr 2024cs.CV cs.AIcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 4 Apr 2024.

Builds on

23 of 40 references.

References · 23

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11 more
  1. 2023Thread of Thought Unraveling Chaotic Contextscited by 72
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Cited by year

Citing papers per year: 2024: 8; 2025: 3.

11 papers.

  1. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 54
  2. 2024MemoryMamba: Memory-Augmented State Space Model for Defect Recognitioncited by 24
  3. 2024Mamba in Vision: A Comprehensive Survey of Techniques and Applicationscited by 18
  4. 2024Optimizing Psychological Counseling with Instruction-Tuned Large Language Modelscited by 2
  5. 2024Leveraging Language Models for Emotion and Behavior Analysis in Educationcited by 1
  6. 2024Active Learning with Task Adaptation Pre-training for Speech Emotion Recognitioncited by 0
  7. 2024Consultation on Industrial Machine Faults with Large language Modelscited by 0
  8. 2024Selective State Space Memory for Large Vision-Language Modelscited by 0
  9. 2025Cross-Cultural Fashion Design via Interactive Large Language Models and Diffusion Modelscited by 0
  10. 2025Weak Supervision Dynamic KL-Weighted Diffusion Models Guided by Large Language Modelscited by 0
  11. 2025Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sourcescited by 0

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  1. 2024A Deep Learning-based Pest Insect Monitoring System for Ultra-low Power Pocket-sized Drones
  2. 2026PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction
  3. 2025Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priors
  4. 2026Pest-Thinker: Learning to Think and Reason like Entomologists via Reinforcement Learning
  5. 2025ConMamba: Contrastive Vision Mamba for Plant Disease Detection
  6. 2025BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing
  7. 2025ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture
  8. 2025Advancing site-specific disease and pest management in precision agriculture: From reasoning-driven foundation models to adaptive, feedback-based learning