An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

Area of Science:

• Plant biology
• Computational biology
• Image analysis

Background:

• High-throughput phenotyping generates large image-based datasets for plant biology research.
• Automated image analysis pipelines are crucial but susceptible to image quality variations, such as brightness and color shifts.
• These variations can bias data and downstream analyses, compromising experimental accuracy.

Purpose of the Study:

• To develop and implement an automated method for standardizing image quality in high-throughput plant phenotyping datasets.
• To correct for variations in brightness, contrast, and color profiles within image datasets.
• To enhance the accuracy of morphological measurements derived from image analysis.

Main Methods:

• Developed an automated image correction method using a collection of linear models.
• The method adjusts pixel data (R, G, B values) based on a reference color panel.
• Applied the technique to an image dataset from a high-throughput imaging facility.

Main Results:

• Successfully detected and corrected variance in image quality attributed to temperature-dependent light intensity.
• Standardization of images improved the accuracy of quantifying morphological measurements.
• The correction method was implemented in a high-throughput pipeline and integrated into PlantCV.

Conclusions:

• The developed automated image correction method effectively standardizes image quality in high-throughput plant phenotyping.
• This standardization is crucial for mitigating bias and improving the reliability of data analysis.
• The method enhances the precision of morphological trait quantification, advancing plant biology research.