Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

18.8K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
18.8K
Key Elements for Plant Nutrition02:35

Key Elements for Plant Nutrition

18.7K
Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
18.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluation of Autoregressive Models for Predicting Two-Dimensional Mandibular Landmark Displacement During Pubertal Growth.

Orthodontics & craniofacial research·2026
Same author

Cardiovascular Disease Risk in the Obese Population in Kuwait: A Systematic Review and Meta-Analysis.

Cureus·2024
Same author

Binding and selectivity studies of phosphatidylinositol 3-kinase (PI3K) inhibitors.

Journal of molecular graphics & modelling·2023
Same author

A Handheld Quantifiable Soft Tissue Manipulation Device for Tracking Real-Time Dispersive Force-Motion Patterns to Characterize Manual Therapy Treatment.

IEEE transactions on bio-medical engineering·2022
Same author

Profiling Immune Cells in the Kidney Using Tissue Cytometry and Machine Learning.

Kidney360·2022
Same author

Informative Causality Extraction from Medical Literature via Dependency-Tree-Based Patterns.

Journal of healthcare informatics research·2022
Same journal

Cross-linguistic patterns of cognitive biases in large language models: a comparative study in English, Hebrew, and Russian.

Frontiers in artificial intelligence·2026
Same journal

From human-like AI to user adoption: the role of trust, attitude, and social influence in shaping behavioral intention.

Frontiers in artificial intelligence·2026
Same journal

Building large-scale English-Romanian literary translation resources with open models.

Frontiers in artificial intelligence·2026
Same journal

Editorial: GenAI in healthcare: technologies, applications and evaluation.

Frontiers in artificial intelligence·2026
Same journal

Logic, inference, understanding: cross-domain generalization for generative language models.

Frontiers in artificial intelligence·2026
Same journal

Label tree semantic losses for rich multi-class medical image segmentation.

Frontiers in artificial intelligence·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.2K

Real-time crop row detection using computer vision- application in agricultural robots.

Md Nazmuzzaman Khan1, Adibuzzaman Rahi2, Veera P Rajendran3

  • 1Lead Research Scientist (Kroger), 84.51°, Cincinnati, OH, United States.

Frontiers in Artificial Intelligence
|November 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new crop row detection algorithm for agricultural robots, achieving over 90% accuracy in real-time. The method enhances autonomous navigation by reliably distinguishing crops from weeds even in challenging conditions.

Keywords:
agricultural robotcrop row detectionprecision farmingreal-time applicationunsupervised learning

More Related Videos

Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

7.9K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K

Related Experiment Videos

Last Updated: Jun 7, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.2K
Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

7.9K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K

Area of Science:

  • Agricultural Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Autonomous navigation in agriculture faces challenges due to natural variations in crop images (weather, growth stages).
  • Real-time processing is crucial for agricultural robot applications.

Purpose of the Study:

  • To develop a robust and efficient crop row detection algorithm for autonomous agricultural robot navigation.
  • To address the need for low-inference-time detection in variable field conditions.

Main Methods:

  • Projective transformation and color-based segmentation to isolate crops from the background.
  • Clustering algorithms to differentiate crop and weed pixels.
  • Robust line-fitting for precise crop row detection.

Main Results:

  • Achieved an overall Intersection over Union (IOU) of 0.73.
  • Demonstrated high robustness in scenarios with significant weed growth.
  • Exhibited over 90% detection accuracy in real-time video experiments.

Conclusions:

  • The proposed algorithm is a viable solution for real-time autonomous navigation of agricultural robots.
  • Offers high accuracy and low inference time, minimizing crop damage.
  • Provides a foundation for future research in precision agriculture.