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 (Arachis hypogaea). 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.

This data set was collected using a custom-fitted drone with a Sony IMX250MZR polarization-sensitive sensor. Field trials were held where the peanut was infected with leaf spot and scored in accordance with the traditional Modified 9-Point Scale. This protocol allowed us to develop this data set, which associates polarization intensity with infection of the peanut.