ArticleScientific reports2025
Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- An explainable vision transformer model with transfer learning for accurate bean leaf disease classification.Scientific reports · 2026Article
- Machine learning and time-series approaches for forecasting bacterial blight of pomegranate.Frontiers in plant science · 2026Article
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Authors and funding
14 authors.
Funding
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Abstract
Accurate estimation of plant disease severity is pivotal for effective management and decision-making. Field experiments were conducted to understand the correlation and predict the yellow mosaic disease severity in yard-long beans using visible image indices. A total of 45 visible / Red Green Blue (RGB) indices were derived from the RGB images and correlated with disease severity, and also used as inputs for predicting disease severity using nine machine learning (ML) models. Out of 143 genotypes screened based on final disease severity 3, 18, 18, 17, 34 and 53 genotypes were grouped in immune, resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible categories, respectively. Model performances was evaluated using R
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