# An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models

arXiv: https://arxiv.org/abs/2107.12189 · HTML: https://mlsanity.com/paper/2107.12189/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition](https://mlsanity.com/paper/1903.06150/) (2019)
- [Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization](https://mlsanity.com/paper/2003.09150/) (2020)
- [Insect pest image detection and recognition based on bio-inspired methods](https://mlsanity.com/paper/1910.00296/) (2019)

## Built on by (most cited first, 7 of 7)

- [InsectMamba: Insect Pest Classification with State Space Model](https://mlsanity.com/paper/2404.03611/) (2024)
- [High performing ensemble of convolutional neural networks for insect pest image detection](https://mlsanity.com/paper/2108.12539/) (2021)
- [RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition](https://mlsanity.com/paper/2410.12718/) (2024)
- [Deep-Wide Learning Assistance for Insect Pest Classification](https://mlsanity.com/paper/2409.10445/) (2024)
- [Mushroom image recognition and distance generation based on attention-mechanism model and genetic information](https://mlsanity.com/paper/2206.13383/) (2022)
- [MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest Detection](https://mlsanity.com/paper/2505.02441/) (2025)
- [Crop Pest Classification Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2507.01494/) (2025)

## Abstract (arXiv)

Accurate insect pest recognition is significant to protect the crop or take the early treatment on the infected yield, and it helps reduce the loss for the agriculture economy. Design an automatic pest recognition system is necessary because manual recognition is slow, time-consuming, and expensive. The Image-based pest classifier using the traditional computer vision method is not efficient due to the complexity. Insect pest classification is a difficult task because of various kinds, scales, shapes, complex backgrounds in the field, and high appearance similarity among insect species. With the rapid development of deep learning technology, the CNN-based method is the best way to develop a fast and accurate insect pest classifier. We present different convolutional neural network-based models in this work, including attention, feature pyramid, and fine-grained models. We evaluate our methods on two public datasets: the large-scale insect pest dataset, the IP102 benchmark dataset, and a smaller dataset, namely D0 in terms of the macro-average precision (MPre), the macro-average recall (MRec), the macro-average F1- score (MF1), the accuracy (Acc), and the geometric mean (GM). The experimental results show that combining these convolutional neural network-based models can better perform than the state-of-the-art methods on these two datasets. For instance, the highest accuracy we obtained on IP102 and D0 is 74.13% and 99.78%, respectively, bypassing the corresponding state-of-the-art accuracy: 67.1% (IP102) and 98.8% (D0). We also publish our codes for contributing to the current research related to the insect pest classification problem.