At the heart of the new methodology is RONUM, an ImageJ-based disease quantification workflow that converts ordinary leaf photographs and scans into precise numerical disease metrics. Rather than relying on fully automated machine learning models that demand large training datasets and specialized computing infrastructure, RONUM uses a user-guided calibration approach. A researcher first teaches the system what infected and healthy tissue looks like within their specific experimental context, and the workflow then applies that calibration consistently across entire replicate datasets. This design choice matters enormously for practical plant pathology, where lighting conditions, leaf health, and disease presentation can vary dramatically between experiments, growth facilities, and pathogen isolates.

The team validated RONUM against manually corrected reference image sets derived from both whole-plant leaf scans and detached-leaf samples. The results were striking: disease estimates showed strong agreement with expert-verified measurements, and critically, the workflow proved reproducible when independent users performed their own calibrations. The agreement was further confirmed by pixel-level comparison against reference masks, demonstrating that different researchers could arrive at essentially the same quantitative answer from the same images. This reproducibility addresses one of the most persistent criticisms of visual disease scoring, where inter-rater variability can obscure genuine biological differences between wheat lines or treatments.

Benchmarking revealed another important advantage. When compared against another published ImageJ workflow, RONUM showed closer agreement with manually corrected measurements, particularly for images containing chlorosis, necrosis, or leaves in generally poor health. This distinction is far from trivial. Rust infections frequently trigger yellowing and tissue death that confounds simple color-based segmentation, causing automated pipelines to either overestimate disease by counting discolored tissue or underestimate it by missing subtle early symptoms. RONUM’s flexible calibration allows researchers to adapt the analysis to these challenging visual conditions rather than forcing their experiments to fit rigid algorithmic assumptions.

The researchers also took the unusual step of systematically evaluating the post-processing options available within the workflow. Their quantitative assessment showed that these choices had little influence on total pustule-area estimates, but substantially affected pustule count measurements. For researchers studying pathogen reproduction dynamics or the early stages of infection, where the number of individual lesions can be as informative as their total area, this finding provides practical guidance on how to standardize their analysis pipelines and avoid inadvertently introducing bias through arbitrary parameter selection.

To demonstrate real-world applications, the team paired RONUM with two complementary infection assay systems. The first is a soil-free whole-plant assay that enabled non-destructive tracking of wheat leaf rust development across an eighteen-day infection time course. By imaging the same plants repeatedly as the disease progressed, researchers could construct quantitative disease curves rather than relying on endpoint snapshots. This longitudinal approach captures the full trajectory of infection, revealing differences in disease onset, progression rate, and final severity that a single time-point measurement would miss entirely. The soil-free growth conditions, established with assistance from Dr Chris Brosnan, additionally reduce the space, cost, and waste associated with conventional soil-based plant culture.

The second assay is a benchtop-scale detached-leaf system that proved capable of delivering reproducible disease quantification across thirteen wheat lines with contrasting disease phenotypes. Detached-leaf assays dramatically increase screening throughput, allowing many genotypes to be evaluated simultaneously on a laboratory bench without the overhead of maintaining mature plants. In an exploratory analysis that showcases the biological depth of the approach, the researchers quantified the ratio of chlorosis to pustule area across these lines. This single derived metric substantially separated susceptible lines from those carrying pathotype-specific Lr resistance genes, suggesting that image-based phenotyping can capture biologically meaningful host responses, such as resistance-associated yellowing, that are typically invisible to conventional image quantification.

Perhaps the most immediately practical demonstration came in the form of a modified detached-leaf assay equipped with a nylon-mesh treatment reservoir, designed specifically for chemical screening. The team used this system to establish a dose-response curve for a commercial propiconazole fungicide formulation, deriving an estimated IC₅₀ of 20.49 ppm, with a 95 percent confidence interval spanning 14.64 to 28.11 ppm. This means the workflow can quantify the efficacy of disease control agents with proper statistical confidence using standard laboratory equipment. For agrochemical companies, academic screening programs, and breeding pipelines searching for novel control strategies, the ability to generate rigorous dose-response data at benchtop scale represents a significant democratization of what has traditionally required substantial infrastructure.

The principal value of RONUM, as the authors emphasize, lies not in any single technical breakthrough but in its flexible, accessible design philosophy. The workflow calibrates to individual experimental systems and then quantifies rapidly and consistently across replicate datasets, generating the annotations and output summaries needed for streamlined record keeping. Combined with the whole-plant and detached-leaf assays, it enables quantitative assessment of disease progression and screening of candidate treatments or putative resistance traits using freely available software and standard laboratory equipment. The research was supported by the Grains Research and Development Corporation and the Australian Research Council Research Hub for Sustainable Crop Protection, and Anne Sawyer was supported by an Advance Queensland Industry Research Fellowship.

The authors note that further validation will be required before these tools reach their full potential, and the preliminary nature of some analyses, particularly the chlorosis-based resistance discrimination, is honestly acknowledged. Yet the significance of the work is difficult to overstate in a field where wheat rust pathogens continuously evolve to overcome resistance genes, making rapid phenotyping of breeding material an ongoing arms race. By lowering the technical and financial barriers to quantitative disease assessment, this suite of methods could accelerate the identification of durable resistance and effective control agents. With rust outbreaks capable of destroying entire harvests in a single season, tools that transform subjective visual scores into reproducible numbers may prove as important to food security as any single resistance gene.

Subject of Research: A semi-automated image-based method for quantifying wheat leaf rust disease progression, resistance, and fungicide response

Article Title: A rapid, semi-automated image-based method for quantitative assessment of rust disease progression in wheat

Article References: Mann, C. W. G., Periyannan, S., Carroll, B. J., Gardiner, D. M., & Sawyer, A. (2026). A rapid, semi-automated image-based method for quantitative assessment of rust disease progression in wheat. Plant Methods. https://doi.org/10.1186/s13007-026-01590-x

Image Credits: AI Generated

DOI: 10.1186/s13007-026-01590-x

Keywords: wheat, leaf rust, RONUM, ImageJ, image analysis, plant pathology, disease quantification, phenotyping, fungicide screening, detached leaf assay, Puccinia triticina, crop protection