# InsectMamba: Insect Pest Classification with State Space Model

arXiv: https://arxiv.org/abs/2404.03611 · HTML: https://mlsanity.com/paper/2404.03611/

## 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 (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Thread of Thought Unraveling Chaotic Contexts](https://mlsanity.com/paper/2311.08734/) (2023)
- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [Residual-based Language Models are Free Boosters for Biomedical Imaging](https://mlsanity.com/paper/2403.17343/) (2024)
- [Particle Filter SLAM for Vehicle Localization](https://mlsanity.com/paper/2402.07429/) (2024)
- [Task-Agnostic Detector for Insertion-Based Backdoor Attacks](https://mlsanity.com/paper/2403.17155/) (2024)
- [Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection](https://mlsanity.com/paper/2403.13335/) (2024)
- [Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts](https://mlsanity.com/paper/2403.13786/) (2024)
- [News Recommendation with Attention Mechanism](https://mlsanity.com/paper/2402.07422/) (2024)
- [On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training](https://mlsanity.com/paper/2305.12224/) (2023)
- [The New Agronomists: Language Models are Experts in Crop Management](https://mlsanity.com/paper/2403.19839/) (2024)
- [An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models](https://mlsanity.com/paper/2107.12189/) (2021)

## Built on by (most cited first, 11 of 11)

- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [MemoryMamba: Memory-Augmented State Space Model for Defect Recognition](https://mlsanity.com/paper/2405.03673/) (2024)
- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [Optimizing Psychological Counseling with Instruction-Tuned Large Language Models](https://mlsanity.com/paper/2406.13617/) (2024)
- [Leveraging Language Models for Emotion and Behavior Analysis in Education](https://mlsanity.com/paper/2408.06874/) (2024)
- [Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition](https://mlsanity.com/paper/2405.00307/) (2024)
- [Consultation on Industrial Machine Faults with Large language Models](https://mlsanity.com/paper/2410.03223/) (2024)
- [Selective State Space Memory for Large Vision-Language Models](https://mlsanity.com/paper/2412.09875/) (2024)
- [Cross-Cultural Fashion Design via Interactive Large Language Models and Diffusion Models](https://mlsanity.com/paper/2501.15571/) (2025)
- [Weak Supervision Dynamic KL-Weighted Diffusion Models Guided by Large Language Models](https://mlsanity.com/paper/2502.00826/) (2025)
- [Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sources](https://mlsanity.com/paper/2502.05944/) (2025)

## 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.