Beech Leaf Disease: Foliar NIR Spectra and Microbiome Data for Pathogen Detection

Eastern North American beech leaf disease data integrates near-infrared spectroscopy and microbiome sequencing. The dataset, authored by Andrew Miles and last updated in 2026, likely contains spectral profiles and bacterial community data used to predict nematode abundance. It demonstrates the potential of NIR spectroscopy as a non-destructive tool for detecting forest disease.

Use Cases

• Predict foliar nematode abundance based on near-infrared spectral signatures described in the study.
• Model relationships between host spectral profiles and bacterial community composition derived from 16S rRNA sequencing.
• Benchmark machine learning algorithms like random forest and support vector machines for spectral data analysis in plant health.
• Develop rapid, non-destructive detection tools for beech leaf disease in forest ecosystems as suggested by the research findings.

Strengths

• Data integrates two modalities: near-infrared spectroscopy and 16S rRNA gene microbiome sequencing.
• Predictive models described include random forest, support vector machines, elastic net, and partial least squares regression.
• License is CC0-1.0, allowing for maximum reuse and redistribution.

Limitations

• Row count and column-level documentation are unknown, which may limit suitability assessment.
• The listed file size is 0.0 B, indicating the actual data files may need to be located separately or the record is a placeholder.
• Description metadata is limited; actual data quality and structure require manual inspection after download.

Provenance

Source

figshare, author Andrew Miles.

Collection Method

Data likely collected from field studies of American beech (Fagus grandifolia) involving spectral measurement and sample sequencing.

Freshness

Last updated 2026-04-22 06:37:56; freshness should be verified.

Geography

Eastern North America, based on the description of beech leaf disease prevalence.