State-of-the-art technology and applications in crop phenomics, volume II

Wanneng Yang*, John H. Doonan, Xinyu Guo, Xiaohui Yuan, Feng Ling

*Corresponding author for this work

Research output: Contribution to journalEditorial

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Abstract

High-throughput acquisition and analysis of phenotypic data is crucial for plant breeding, as phenotypes are the language of plants and a way for them to express their growth status. We can understand plants and discover their secrets of life through the phenotypes. The crop phenotyping community has put a lot of effort into collecting, processing, and analyzing phenotypic data, which is increasingly considered an important tool for rapidly advancing genetic gain in breeding programs (Zhao et al., 2019). Various types of data have emerged from diverse phenotyping platforms ranging from lab-scale to field-scale, and as a result, various phenotypic data processing approaches have emerged at this historic moment. By integrating knowledge from life science, optics, artificial intelligence, computer science, and engineering, plant phenomics has developed into a cutting-edge discipline, and considerable progress has been made in both phenotyping facilities and methodologies.

To promote the most advanced research progresses in crop phenomics, this Research Topic is prepared and released, which covers new advances in crop phenomics, including phenotyping platforms, methods, and applications. Although various phenotyping platforms have been developed, there is still a great demand for cost-effective phenotyping platform. Deep learning, as a highly effective technology, is applied to various types of images such as RGB, hyperspectral, and CT images to produce highly accurate phenotypic parameters. Meanwhile, the potential of machine learning approaches has also been demonstrated in phenotypic analysis, such as post-harvest quality control, breeding, plant research, and plant response to environmental stress.
Original languageEnglish
Article number1195377
Number of pages4
JournalFrontiers in Plant Science
Volume14
DOIs
Publication statusPublished - 10 May 2023

Keywords

  • AI-driven phenotypic analysis
  • hyperspectral
  • X-ray micro-CT
  • UAV
  • high-throughput phenotyping

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