RGB image-based method for phenotyping rust disease progress in pea leaves using R.

Area of Science:

• Plant Pathology
• Agricultural Science
• Computational Biology

Background:

• Pea rust disease poses a significant threat to crop yield.
• Identifying rust-resistant pea genotypes is crucial for crop improvement.
• Current disease assessment methods are time-consuming and prone to errors.

Purpose of the Study:

• To develop an automated image analysis pipeline for precise rust disease quantification in pea.
• To establish a reliable method for assessing disease progression parameters.
• To facilitate the screening of large pea germplasm collections for rust resistance.

Main Methods:

• Developed an automated image analysis pipeline using R for rust assessment in pea leaves.
• Utilized segmentation indices, including the Normalized Green Red Difference Index (NGRDI), and varying image resolutions.
• Validated the method by analyzing 600 pea leaflets and comparing results with visual assessments.

Main Results:

• The image-based method accurately estimated pustule number, size, leaf area, and coverage.
• NGRDI provided the fastest analysis, with high accuracy even at reduced resolutions.
• Disease progression parameters, such as latency period and area under the disease curve, were reliably reconstructed.
• Image-based analysis showed significant variation in disease response among pea genotypes, comparable to visual ratings.

Conclusions:

• The developed image-based method offers improved resolution and precision for monitoring pea rust.
• This approach eliminates rater-induced errors, providing more accurate disease evaluation than traditional methods.
• Implementation in germplasm collections will enhance understanding of plant-pathogen interactions and accelerate breeding for rust-resistant pea cultivars.