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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

A rapid and precise algorithm for maize leaf disease detection based on YOLO MSM.

Yu Meng^1,2, Jiawei Zhan^3,4, Kangshun Li⁵

¹College of Computer Science, Guangdong University of Science and Technology, Dongguan, 510645, China. mydaju@163.com.

Scientific Reports

|February 19, 2025

Summary

This summary is machine-generated.

A new YOLO-MSM algorithm improves maize leaf disease detection using multi-scale variable kernel convolution and attention mechanisms. This lightweight model achieves high accuracy and speed, enabling mobile device deployment for early disease identification.

Keywords:

Convolutional neural network Deep learning Maize leaf disease Smart agriculture

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate and real-time maize leaf disease detection is crucial for reducing agricultural economic losses.
Challenges in current methods include large datasets, low accuracy, and production inefficiencies.

Purpose of the Study:

To introduce YOLO-MSM, an advanced algorithm for maize leaf disease detection.
To enhance detection accuracy, speed, and efficiency in real-world agricultural settings.

Main Methods:

Developed the MKConv (Multi-scale Variable Kernel Convolution) for adaptive feature extraction.
Integrated the C2f-SK module with Selective Kernel (SK) attention for optimized feature representation.
Utilized MPDIoU (Minimum Point Distance Intersection over Union) loss function for improved target localization.

Main Results:

YOLO-MSM achieved a real-time detection rate of 279.56 frames per second (fps).
Demonstrated improvements in precision (0.66%) and recall (1.61%) compared to baseline algorithms.
The algorithm is lightweight (5.4 MB) with significantly reduced parameters and FLOPs.

Conclusions:

YOLO-MSM offers a superior balance between precision and speed for maize leaf disease detection.
The lightweight design facilitates deployment on mobile devices for practical agricultural applications.