Improving the efficiency of plant root system phenotyping through digitization and automation

Area of Science:

• Plant science
• Agricultural science
• Genetics

Background:

• Root system architecture (RSA) significantly impacts crop yield by influencing water and nutrient uptake from soil.
• Traditional root phenotyping is labor-intensive and difficult due to the subterranean nature of roots, limiting genetic improvement efforts.
• Digital phenotyping offers advanced solutions for plant trait measurements, including RSA.

Purpose of the Study:

• To review recent advancements in digital phenotyping techniques for root system architecture (RSA) traits.
• To categorize different sample types used in digital RSA phenotyping.
• To identify areas for future development in digital root phenotyping.

Main Methods:

• Categorization of digital phenotyping approaches based on sample types: intact soil blocks, soil sections, and isolated root samples.
• Review of current digital phenotyping technologies and their application to RSA trait analysis within these categories.
• Discussion of the challenges and limitations associated with each sample type in digital phenotyping.

Main Results:

• Digital phenotyping methods for RSA have been successfully applied across various sample types.
• Each sample type presents unique advantages and disadvantages for digital phenotyping of root systems.
• Significant potential exists for refining digital phenotyping techniques to overcome current limitations.

Conclusions:

• Digital phenotyping is a rapidly evolving field with great promise for accelerating the genetic improvement of root system architecture.
• Further innovation is needed to enhance the efficiency and accuracy of digital root phenotyping across all sample categories.
• Improved RSA phenotyping through digital tools will be critical for enhancing crop productivity and sustainability.