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Resources for image-based high-throughput phenotyping in crops and data sharing challenges.

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Summary
This summary is machine-generated.

High-throughput phenotyping (HTP) platforms collect extensive plant data for crop improvement. This review highlights HTP applications and available datasets to accelerate breeding efforts.

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Area of Science:

  • Plant science
  • Agricultural technology
  • Genomics

Background:

  • High-throughput phenotyping (HTP) platforms utilize diverse sensors (RGB, hyperspectral, CT) to monitor plant phenotypic variation.
  • These platforms integrate phenotypic data with environmental and genotypic information, creating valuable datasets for crop characterization.

Purpose of the Study:

  • To review the applications of image-based HTP in accelerating crop improvement.
  • To inform researchers about available HTP resources and their potential for crop breeding.

Main Methods:

  • Review of existing literature and available HTP datasets.
  • Description of HTP applications including yield prediction, root phenotyping, climate resilience, pest/pathogen detection, and quantitative trait measurement.

Main Results:

  • HTP datasets are crucial for characterizing crop phenotypes and enabling comparative studies.
  • Image-based HTP supports yield prediction, root analysis, climate-resilient crop development, and pest/pathogen detection.

Conclusions:

  • Sharing phenotypic data is essential for advancing crop breeding.
  • Available HTP datasets and tools can significantly accelerate crop improvement for breeders and tool developers.