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  1. Home
  3. Maize Detection And Row Extraction Using Maize-yolo And Ipm-clustering Method For Autonomous Agricultural Navigation.
  1. Home
  3. Maize Detection And Row Extraction Using Maize-yolo And Ipm-clustering Method For Autonomous Agricultural Navigation.

Related Experiment Video

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

Maize Detection and Row Extraction Using Maize-YOLO and IPM-Clustering Method for Autonomous Agricultural Navigation.

Tao Sun1, Junzhe Qu1, Chen Cai1

  • 1Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China.

Sensors (Basel, Switzerland)
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Maize-YOLO, a lightweight model for accurate maize seedling detection, improving autonomous navigation. The plant-oriented framework achieves high accuracy in crop row extraction, even in challenging field conditions.

Keywords:
Inverse Perspective Mapping (IPM)autonomous agricultural navigationcrop row extractionmaize detection

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Robotics

Background:

  • Accurate crop row extraction is crucial for autonomous agricultural machinery navigation.
  • Challenges in maize fields include variable illumination, occlusion, and complex backgrounds, hindering traditional navigation methods.

Purpose of the Study:

  • To develop a robust and efficient plant-oriented framework for maize crop row perception.
  • To enhance the reliability and accuracy of vision-based navigation for autonomous agricultural robots.

Main Methods:

  • A lightweight object detection model, Maize-YOLO (based on YOLOv11n), was developed with optimized modules (C3k2_Faster_CGLU, Detect_LSH) and pruning strategies.
  • A pipeline (IPM-DBSCAN-LSM) was used to estimate row centerlines from detected plant positions, employing inverse perspective mapping, DBSCAN clustering, and least squares fitting.

Main Results:

  • Maize-YOLO achieved 97.6% mAP@0.5, reduced GFLOPs by 61.9%, and reached 84.4 FPS on CPU.
  • The crop row extraction achieved 98.6% positional accuracy and a mean angular deviation of 0.44°.

Conclusions:

  • The proposed framework offers accurate, lightweight, and real-time crop row perception for autonomous agricultural navigation.
  • This method enhances precision field operations by overcoming common challenges in maize cultivation environments.