Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics). The excel file includes Aphanomyces root rot disease visual scores (Root_Rating), unique identifier for each lentil accession/line (Lentil_ID), unique identifier for each experiment (Experiment), and unique identifier for each image (Lab_ID).

This project was funded in part by US Department of Agriculture (USDA) – National Institute for Food and Agriculture (NIFA) Agriculture and Food Research Initiative Competitive Project WNP06825 (accession number 1011741), Hatch Project WNP00011 (accession number 1014919), and the Washington State Department of Agriculture, Specialty Crop Block Grant program (project K1983).

{"references": ["Marzougui, A., Ma, Y., McGee, R. J., Khot, L. R., & Sankaran, S. (2020). Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil. Plant Phenomics, 2020."]}