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Related Experiment Video

Updated: Jul 28, 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 mobile-based system for maize plant leaf disease detection and classification using deep learning.

Faiza Khan^1,2, Noureen Zafar^1,2, Muhammad Naveed Tahir^2,3

¹University Institute of Information Technology, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan.

Frontiers in Plant Science

|May 31, 2023

Summary

This summary is machine-generated.

This study introduces an AI application using deep learning for maize disease detection, classifying Blight, Sugarcane Mosaic virus, and Leaf Spot. The YOLOv8n model achieved 99.04% accuracy, enabling real-time mobile detection.

Keywords:

YOLO deep learning disease classification object detection transfer learning

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Area of Science:

Agricultural Science
Computer Science
Plant Pathology

Background:

Maize is a vital global crop susceptible to diseases impacting yield and quality.
Accurate and timely disease detection is crucial for effective crop management.

Purpose of the Study:

To develop and evaluate a deep learning-based application for detecting and classifying maize crop diseases.
To segment affected leaf areas for disease tracking.
To provide a real-time disease detection tool for farmers.

Main Methods:

A dataset of three maize diseases (Blight, Sugarcane Mosaic virus, Leaf Spot) was collected under diverse conditions.
Deep learning models (YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, YOLOv8n) were trained and compared.
The best-performing model was embedded into a mobile application.

Main Results:

YOLOv8n demonstrated the highest prediction accuracy at 99.04%, outperforming other models.
The YOLOv8n model accurately localized disease spots with high confidence.
This research reports the first deep learning application for Sugarcane Mosaic virus detection in maize.

Conclusions:

Deep learning, particularly the YOLOv8n model, offers a highly accurate solution for maize disease detection and classification.
The developed mobile application provides a practical, real-time tool for agricultural disease management.
This technology has the potential to significantly improve maize yield and quality by enabling early disease intervention.