ArticlePlant phenomics (Washington, D.C.)2025
Pre-symptomatic diagnosis of rice blast and brown spot diseases using chlorophyll fluorescence imaging.
Article in Plant phenomics (Washington, D.C.), 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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Who cites it
2 citing papers in PubMed.
- Mm-VitnNet: a gated image-text interaction network for soybean salt tolerance recognition using chlorophyll fluorescence phenotypes.Frontiers in plant science · 2026Article
- A lightweight and generalizable deep learning framework for early detection of rice leaf diseases in complex field environments.Scientific reports · 2025Article
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Authors and funding
4 authors.
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Abstract
Rice blast and brown spot are two of the most significant fungal diseases affecting rice production. Although the leaf symptoms of both diseases are distinct, they manifest as similar brown-black lesions, complicating differentiation and effective management. Traditional diagnostic methods predominantly rely on DNA-based molecular techniques, which are not well-suited for rapid, large-scale applications. This study aimed to identify reliable chlorophyll fluorescence (ChlF) indicators for diagnosing these diseases at the pre-symptomatic stage using a pulse-amplitude modulation fluorometer. Changes in ChlF parameters were measured following fungal infection in 120 leaves and 750 spots across five time points in detached leaf assays. Diagnostic indicators were selected through machine learning and fold-change value comparisons, then validated using 374 pre-symptomatic spots induced under different infection conditions in whole plants. Fifteen ChlF diagnostic parameters were identified, nine of which were specifically associated with rice blast. Pre-symptomatic lesions in both diseases caused significant decreases in non-photochemical quenching parameters (
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