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Related Experiment Video

Updated: Jun 11, 2025

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast.

Qiang Cao¹, Dongxue Zhao¹, Jinpeng Li¹

¹College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.

|September 28, 2024

Summary

Pyramid-YOLOv8 offers rapid and accurate detection of rice blast disease, improving upon existing methods. This advancement aids in sustainable agriculture by enhancing rice yield and quality through efficient disease management.

Keywords:

Computer vision Disease detection Pyramid-YOLOv8 Rice leaf blast Small target

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Last Updated: Jun 11, 2025

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

Detecting Virus and Salivary Proteins of a Leafhopper Vector in the Plant Host

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Published on: September 14, 2021

Modification and Application of a Leaf Blower-vac for Field Sampling of Arthropods

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Published on: August 10, 2016

Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Rice blast poses a significant threat to global rice production, impacting yield and quality.
Traditional disease detection methods are often slow and inefficient, hindering timely intervention.
Accurate and rapid disease identification is crucial for sustainable agriculture and food security.

Purpose of the Study:

To develop a fast and accurate method for detecting rice leaf blast disease.
To improve upon existing deep learning models for agricultural disease detection.
To provide a new perspective on disease management strategies in rice cultivation.

Main Methods:

Proposed Pyramid-YOLOv8 algorithm based on the YOLOv8x network framework.
Incorporated a multi-attention feature fusion network and an additional detection head.
Designed a lightweight C2F-Pyramid module to enhance computational efficiency.

Main Results:

Achieved a mean Average Precision (mAP) of 84.3%, outperforming several benchmark models.
Demonstrated a detection speed of 62.5 FPS with a model size of 42.0 M parameters.
Reduced model size by 41.7% and Floating Point Operations (FLOPs) by 23.8%.

Conclusions:

Pyramid-YOLOv8 provides a highly efficient and accurate solution for rice leaf blast detection.
The developed algorithm offers a robust theoretical foundation for agricultural disease detection.
This work introduces novel strategies for disease management and prevention in rice production.