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Updated: Aug 5, 2025

Real-Time Detection of Reactive Oxygen Species Production in Immune Response in Rice with a Chemiluminescence Assay

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Published on: November 25, 2022

Detection of Rice Pests Based on Self-Attention Mechanism and Multi-Scale Feature Fusion.

Yuqi Hu^1,2,3,4, Xiaoling Deng^1,2,3,4, Yubin Lan^1,2,3,4

¹College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China.

|March 28, 2023

Summary

This summary is machine-generated.

A new deep learning model, YOLO-GBS, accurately detects and classifies rice pests. This advanced system improves pest identification in complex environments, aiding in crop protection and yield enhancement.

Keywords:

BiFPN Swin Transformer YOLOv5 rice pest detection self-attention

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Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Increasing rice pest occurrences negatively impact global yields.
Existing pest detection methods struggle with subtle appearance differences and size variations.

Purpose of the Study:

To develop an advanced deep learning model for accurate and efficient detection and classification of rice pests.
To address the challenges of small visual differences and large size variations among pests.

Main Methods:

Proposed a novel deep neural network, YOLO-GBS, based on YOLOv5s.
Integrated a global context (GC) attention mechanism and BiFPN for enhanced feature fusion.
Incorporated Swin Transformer to leverage self-attention for global contextual information.

Main Results:

Achieved an average mean Average Precision (mAP) of 79.8% on an insect dataset.
Demonstrated a 5.4% improvement in mAP compared to YOLOv5s.
Significantly enhanced detection performance in complex scenes and showed good generalization ability.

Conclusions:

YOLO-GBS offers a more accurate and efficient intelligent detection method for rice pests.
The model shows promise for broader applications in crop pest management.
This research contributes to intelligent agricultural solutions for pest control.