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Deep learning has revolutionized plant science and agriculture over the past decade, establishing new research communities and connecting computer vision with biology for plant phenotyping.

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Area of Science:

  • Plant science and agriculture
  • Computer vision
  • Machine learning

Background:

  • Deep learning adoption in scientific fields is rapidly increasing.
  • Plant phenotyping research has seen significant advancements.
  • The intersection of computer vision and biology is a growing area.

Purpose of the Study:

  • To reflect on the past decade of deep learning in plant science and agriculture.
  • To highlight the establishment of a new research community.
  • To discuss the connections forged between computer vision and biology.

Main Methods:

  • Literature review of deep learning applications in plant phenotyping.
  • Analysis of the growth and impact of the research community.
  • Synthesis of the interdisciplinary connections formed.

Main Results:

  • Deep learning applications in plant phenotyping have grown substantially over the last 10 years.
  • A distinct research community has emerged.
  • Strong links between computer vision techniques and biological applications have been established.

Conclusions:

  • The past decade has been pivotal for deep learning in plant science.
  • Future research will likely build on these established interdisciplinary foundations.
  • The field is poised for continued innovation and integration.