Published in the journal SoftwareX, the new tool represents a significant step forward in digital plant pathology. Developed by Stefanie Lück, Moozhan Serpoush, and Dimitar Douchkov, BluVision Root utilizes deep learning-based instance segmentation models to detect and quantify disease symptoms from standardized seedling images. Unlike previous image-analysis tools such as SmartRoot or RootNav, which primarily focus on root architecture traits like length and branching, BluVision Root is specifically engineered to identify Fusarium-infected regions alongside total root tissue. This dual detection capability allows for the extraction of disease-specific quantitative traits, providing a continuous and objective measure of infection severity that can be directly applied to statistical and genetic analyses.

The technical architecture of BluVision Root is built upon the Ultralytics YOLO framework, a popular deep learning library for object detection and segmentation. The software processes composite RGB images, which are often generated from monochrome images acquired at defined wavelengths by the Macrobot high-throughput phenotyping platform. A central technical challenge in developing this tool was the reliable segmentation of thin root structures and relatively small infected tissue regions against a large image background. Because infected regions often occupy only a small fraction of the total image area, the model faced a significant small-object segmentation problem with substantial foreground-background imbalance. To overcome this, the developers designed the architecture to support high-resolution image processing and polygon-based annotation, ensuring that the model could accurately distinguish between healthy and diseased tissue even when the symptoms were subtle or localized.

Model development involved a rigorous process of manual annotation and systematic optimization. Researchers manually annotated 128 root images using the Roboflow platform, defining two biologically relevant object classes: total root tissue and Fusarium-infected regions. The training dataset contained 597 root annotations and 392 Fusarium-infected region annotations. During the development phase, the team evaluated 39 different model configurations, including variants of YOLOv8, YOLO11, and YOLO26, with various model sizes, image resolutions, and training durations. The models were compared using standard metrics such as mask mAP50-95, precision, and recall. Ultimately, the YOLOv8s-seg model, trained at an image resolution of 1024 pixels for 150 epochs using the AdamW optimizer, provided the best overall performance and was selected as the production model for the software release.

The validation of BluVision Root was conducted using an independent dataset of 148 samples from winter wheat seedlings inoculated with Fusarium culmorum strain UK99. These seedlings were imaged daily from one to four days post-infection using the Macrobot 2.0 platform under controlled environmental conditions. The automated infection-area measurements generated by the software were compared against expert-based manual scoring, where an expert visually estimated the percentage of total root tissue showing Fusarium-associated symptoms on a continuous scale from 0 to 100%. The results demonstrated strong agreement between the automated and manual assessments, with a Pearson correlation coefficient of 0.906. The mean absolute error was 9.51 percentage points, and the root mean square error was 13.35 percentage points, indicating that the software provides a reliable and accurate alternative to human scoring for quantitative disease assessment.

From a user perspective, BluVision Root offers a modular and flexible workflow that supports batch processing of experimental image sets, object-level detection, and quantitative trait extraction. The software calculates several disease-related traits, including infected root tissue area, total root tissue area, relative infection coverage, and symptom distribution. It also supports lane-position analysis, which is particularly useful for plate-based high-throughput experiments where seedlings are arranged in fixed positions. The output includes segmentation masks, polygon coordinates, class labels, and confidence values, allowing users to inspect the detected structures and export standardized CSV tables for downstream analysis. These tables can be used directly for statistical modeling, genotype comparison, treatment evaluation, or machine learning workflows, facilitating a seamless transition from image data to biological insight.

Performance benchmarks indicate that BluVision Root is capable of handling large datasets efficiently, even without GPU acceleration. In a CPU-only benchmark using 2,268 images on a Windows workstation equipped with two Intel Xeon E5-2643 v3 processors and 128 GB of RAM, the complete processing workflow required approximately 10,168 seconds. This corresponds to an average processing time of 4.48 seconds per image, or roughly 13.4 images per minute. While GPU acceleration can substantially speed up model inference, the CPU-only performance demonstrates that the software is accessible to laboratories with standard computing infrastructure. The modular architecture of the software also allows individual components, such as preprocessing steps, model weights, and reporting settings, to be updated or adapted without altering the complete software structure, ensuring long-term maintainability and adaptability to new research needs.

The impact of BluVision Root extends beyond simple disease quantification to enable research questions that are difficult to address with manual scoring alone. By providing continuous, reproducible phenotypic data, the software facilitates the analysis of early infection dynamics, genotype-specific differences in symptom development, and spatial patterns of infection along seedling tissues. This is particularly relevant for resistance research and breeding programs, where large genotype panels require rapid and objective assessment of disease responses. The extracted traits provide a quantitative basis for statistical modeling and biological interpretation, supporting applications such as QTL mapping, genome-wide association studies, and machine learning-based prediction of disease resistance. By reducing operator-dependent variation, the software improves the comparability of disease measurements across different experiments, time points, and research groups.

Despite its advantages, the current implementation of BluVision Root has defined limitations that researchers must consider. The model was validated specifically for young wheat seedlings inoculated with F. culmorum UK99 under standardized Macrobot imaging conditions. It does not establish a direct correspondence between seedling responses and resistance in soil-grown or adult plants. Additionally, non-pathogenic discoloration that resembles Fusarium-associated symptoms may lead to misclassification, making the use of non-inoculated controls advisable for accurate biological interpretation. Application to other crops, diseases, or substantially different imaging conditions may require representative training data, model retraining, and independent validation. The current release also does not automatically identify or exclude blurred, overexposed, or otherwise unsuitable images, necessitating a quality control step in the workflow.

BluVision Root is available as open-source software under the GNU General Public License v3.0, with a public repository and archived software release to support reuse and further development. By linking controlled infection assays with automated quantitative image analysis, the tool provides a practical and reusable resource for digital plant pathology, resistance genetics, and cereal breeding research. As the field of plant phenomics continues to evolve, tools like BluVision Root are essential for overcoming the phenotyping bottleneck and accelerating the discovery of disease-resistant crop varieties. The software’s ability to transform complex image data into actionable quantitative traits marks a significant advancement in the precision and efficiency of plant disease research.

Subject of Research: Automated image analysis software for high-throughput phenotyping of Fusarium root rot in cereal seedlings

Article Title: BluVision Root: automated image analysis software for high-throughput phenotyping of Fusarium root rot in cereal seedlings

Article References: Lück, S., Serpoush, M., & Douchkov, D. (2026). BluVision Root: automated image analysis software for high-throughput phenotyping of Fusarium root rot in cereal seedlings. SoftwareX, 36, Article 103132. https://doi.org/10.1016/j.softx.2026.103132

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103132

Keywords: Fusarium root rot, Cereal phenotyping, Deep learning, YOLOv8, Plant pathology, Image analysis, Open-source software, High-throughput screening, Wheat disease, Digital agriculture, BluVision, Root