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Related Experiment Video

Updated: Jul 2, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

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

Published on: March 28, 2025

LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.

Chengwen Yang1, Qisheng Feng1,2, Jingjing Mai1

  • 1State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.

Frontiers in Plant Science
|July 1, 2026
PubMed
Summary

Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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A new model, LSL-YOLO11n, enhances maize leaf disease detection in complex field conditions by improving feature representation and localization. This advanced model shows superior performance in identifying small lesions and irregular disease patterns, aiding intelligent field monitoring.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Maize leaf diseases present significant detection challenges due to variable lesion scales, irregular shapes, blurred boundaries, and complex backgrounds in field environments.
  • Existing detection models struggle with precise localization, especially for small lesions, hindering accurate disease identification and management.

Purpose of the Study:

  • To develop an advanced maize leaf disease detection model, LSL-YOLO11n, based on the YOLO11n framework.
  • To improve feature representation, localization quality modeling, and bounding-box regression for enhanced disease detection accuracy under challenging field conditions.

Main Methods:

  • Proposed LSL-YOLO11n model integrating improvements into the YOLO11n architecture.
  • Conducted experiments on a dataset of 15,119 images with 29,366 annotated instances across eight categories (seven diseases and healthy leaves).
Keywords:
LSL-YOLO11nYOLO11ncomplex field environmentslocalization quality estimationmaize leaf disease detectionobject detection

Related Experiment Videos

Last Updated: Jul 2, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

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

Published on: March 28, 2025

  • Performed ablation studies, comparative analyses with mainstream object detection models (YOLOv8n, YOLOv9t, YOLOv10n, YOLOv12n), and visual detection assessments.

    • Main Results:

    • LSL-YOLO11n achieved a Precision of 84.4%, Recall of 73.9%, and mean Average Precision (mAP) of 83.3%.
    • The model demonstrated a 3.1 percentage point mAP improvement over the baseline YOLO11n.
    • Significant mAP enhancements were observed compared to YOLOv8n (4.7 pp), YOLOv9t (3.3 pp), YOLOv10n (5.3 pp), and YOLOv12n (10.9 pp).

    Conclusions:

    • LSL-YOLO11n effectively addresses the challenges of maize leaf disease detection in complex field environments, particularly for small and irregular lesions.
    • The model's improved performance provides robust technical support for rapid maize disease recognition and intelligent agricultural field monitoring.
    • The enhanced feature representation and localization capabilities offer a more stable and accurate solution for precision agriculture.