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Related Experiment Video

Updated: Jul 5, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Computer vision-based plants phenotyping: A comprehensive survey.

Talha Meraj1, Muhammad Imran Sharif1, Mudassar Raza1

  • 1Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan.

Iscience
|January 25, 2024
PubMed
Summary

Computer vision systems are essential for plant phenotyping to monitor plant traits and productivity in diverse environments. This review discusses current challenges and solutions in data collection, segmentation, and classification for automated plant analysis.

Keywords:
Machine learningPhenotypingPlant Biology

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Biology

Background:

  • Growing global population necessitates enhanced food production and understanding plant trait variability across environments.
  • Manual monitoring of individual plant traits is labor-intensive and impractical for large-scale breeding programs.
  • Computer vision offers a solution for objective, scalable plant phenotyping and analysis.

Purpose of the Study:

  • To review current computer vision-based methods for plant phenotyping.
  • To identify challenges and limitations in data collection, segmentation, and classification for plant analysis.
  • To highlight existing solutions and their shortcomings in addressing data limitations.

Main Methods:

  • Review of existing literature on computer vision applications in plant phenotyping.
  • Discussion of traditional and modern segmentation and classification techniques.
  • Analysis of data collection strategies and their associated challenges.

Main Results:

  • Various data collection methods are employed, each with limitations.
  • Traditional segmentation and classification approaches are discussed.
  • Current computer vision solutions for data limitations are not fully adequate.

Conclusions:

  • Automated plant phenotyping using computer vision is crucial for efficient crop improvement.
  • Significant challenges remain in data acquisition, quality, and model generalizability.
  • Further research is needed to develop robust and genuine solutions for plant phenotyping challenges.