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Polarimetric peanut drone images of peanut crop infected with leaf spot

dataset
posted on 2025-11-23, 02:58 authored by Joshua Larsen, Jeffrey Dunne, Robert Austin, Cassondra Newman, Michael Kudenov
<p>This dataset contains polarization-based aerial imagery and plot-level phenotypic measurements collected to evaluate the relationship between leaf spot severity and polarization signatures in peanut (<em>Arachis hypogaea</em>). The study was conducted at two North Carolina research stations—the Upper Coastal Plain Research Station (UCPRS) and the Peanut Belt Research Station (PBRS)—using an automated drone imaging system developed by North Carolina State University. A Lucid Triton camera equipped with a Sony IMX250MZR polarization-sensitive CMOS sensor was mounted on a UAV to acquire Stokes vector–derived Degree of Linear Polarization (DOLP) and S0 intensity images over peanut breeding trials. The research goal was to determine whether DOLP values increase with the severity of leaf spot, a major foliar disease affecting peanut production. Raw DOLP and S0 imagery from each location is provided alongside an Excel file containing expert-assigned leaf spot visual scores, plot-level mean DOLP values, and RGB-based defoliation metrics. Together, these data enable assessment of polarization-based disease detection, validation against breeder scoring, and integration with conventional RGB phenotyping approaches. This dataset supports further investigation into remote sensing of foliar disease, development of machine learning methods for automated severity estimation, and refinement of polarization imaging workflows for plant health diagnostics.</p>

Funding

USDA-NIFA: 007052

History

Related Materials

  1. 1.
    DOI - Is supplement to https://doi.org/10.1002/ppj2.70018
  2. 2.

Data contact name

Larsen, Joshua

Data contact email

larsenjoshua2@gmail.com

Publisher

Dryad

Theme

  • Not specified

ISO Topic Category

  • biota

National Agricultural Library Thesaurus terms

data collection; plant health; foliar diseases; disease detection; defoliation; leaf spot; North Carolina; peanuts; polarimetry; remote sensing; coastal plains; phenotype; cameras; automation

Pending citation

  • No

Public Access Level

  • Public

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