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Related Experiment Video

Updated: May 27, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Efficient and accurate identification of maize rust disease using deep learning model.

Pei Wang^1,2, Jiajia Tan¹, Yuheng Yang^2,3

¹Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, China.

Frontiers in Plant Science

|February 21, 2025

Summary

A new Maize-Rust model accurately differentiates common and southern corn rust. This AI tool, deployable on mobile phones, aids in real-time disease detection and management for large-scale maize cultivation.

Keywords:

SimAM common rust maize small target detection southern rust

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Area of Science:

Agricultural Science
Plant Pathology
Computer Vision
Artificial Intelligence

Background:

Accurate differentiation of common corn rust and southern corn rust is crucial for understanding disease patterns and risks in maize cultivation.
Existing methods may lack the precision or real-time capabilities needed for effective field management of these prevalent maize diseases.

Purpose of the Study:

To develop a specialized AI model for the accurate identification and differentiation of common and southern corn rust in maize.
To enable real-time disease detection and data analysis directly in the field using mobile device deployment.

Main Methods:

Development of a novel Maize-Rust detection model based on the YOLOv8s architecture.
Integration of a SimAM module for enhanced feature extraction and a BiFPN for multi-scale feature fusion.
Utilization of Depthwise Separable Convolution (DWConv) for streamlined and efficient detection.

Main Results:

The developed Maize-Rust model achieved a high accuracy of 94.6%, average accuracy of 91.6%, recall of 85.4%, and an F1 score of 0.823.
Demonstrated superior classification accuracy compared to Faster-RCNN (16.35% higher) and SSD (12.49% higher) models.
Achieved a detection speed of 16.18 frames per second, suitable for real-time applications.

Conclusions:

The specialized Maize-Rust model offers a highly accurate and efficient solution for differentiating common and southern corn rust.
Mobile deployment facilitates real-time data collection and analysis, supporting timely and effective management of rust outbreaks in agricultural settings.