Article Details

Title · Authors · Abstract · Keywords · DOI · References

A Review of for Agricultural Pest Counting Methods Based on Object Detection

XIONG Shun, ZHOU Rui, SHI Lian, DENG Tao, LI Donghui

(College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China)

Section：Artificial Intelligence · Vol. 2026, Issue 4 · Pages：60-66,72 · 2096-4706（2026）04-0060-08 · CLC number：TP391.4 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2026.04.011

Abstract

With the development of smart agriculture, Computer Vision methods powered by Deep Learning have been widely applied to agricultural pest detection and counting. This paper reviews recent advances in Object Detection for this field, and compares the differences between single-stage detectors (YOLO series, SSD) and two-stage detectors (Faster R-CNN) in terms of precision, speed, and deployment. Representative improvement strategies and their performance on datasets such as AgriPest, IP102, PestImgData, and Pest24 are discussed. In addition, commonly used evaluation metrics (mAP, Precision, Recall, MAE, RMSE, R2) and their applicability to detection and counting tasks are summarized. The current challenges, including small object density, complex backgrounds, data imbalance, and edge deployment, are also concluded. Finally, future directions such as multimodal and spatiotemporal modeling, generative model-based data augmentation, unified evaluation frameworks, and lightweight optimization are proposed, providing a reference for intelligent agricultural monitoring research and applications.

Keywords

agricultural pest; Object Detection; counting; Deep Learning; dataset; evaluation metrics

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Funding

Youth Project of National Social Science Fund of China (CIA240281); Research Project of Hunan Provincial Department of Education (24B0226)

About the authors

Xiong Shun (2000-), male, Han ethnicity, from Yiyang, Hunan, currently pursuing a master's degree in computer vision and agricultural information technology; Zhou Rui (2003-), male, Han ethnicity, from Shaoyang, Hunan, is currently pursuing a master's degree in computer vision and image processing; Deng Tao (2001-), male, Han ethnicity, from Yongzhou, Hunan, currently pursuing a master's degree in computer science and technology, bioinformatics; Corresponding author: Li Donghui (1973-), male, Han ethnicity, born in Shaoyang, Hunan, associate professor, PhD, research direction: software engineering, artificial intelligence, agricultural information technology, and bioinformatics technology.