Oxalate-Linked Electrochemistry Enables Early Detection of Sclerotinia Blight in Peanut Crops Using 3D-Printed Nanostructured Pt Sensors

Abstract Sclerotinia minor (causing Sclerotinia blight) is a devastating pathogen in peanut production, with severe outbreaks causing up to 50% yield loss due to rapid oxalic acid (OA) accumulation. Early diagnosis is challenging because canopy-level symptoms typically emerge only after infection is well established, while early cues are subtle, stem-localized, and nonspecific; in contrast, molecular assays are time- and resource-intensive. Despite established mechanistic links between oxalate accumulation and disease progression, so far, there are no sensors developed or tested for detecting Sclerotinia blight in peanut plants. This paper reports a low-cost, lithography-free, and label-free electrochemical sensor for metabolite-targeted, presymptomatic monitoring of S. minor in peanut plants based on clear mechanistic links between oxalate accumulation and disease progression. The sensor platform comprises 3D-printed resin substrates with platinum (Pt) electrodes and a nanostructured reduced graphene oxide (rGO)−chitosan interface functionalized with an oxaloacetic acid (OAA) interfacial layer. Using ferri/ferrocyanide as a redox probe, the sensor exhibited a linear calibration to oxalate (prepared from OA) from 0.05 µM to 1 mM (R2 = 0.99), with a sensitivity of 6.37 µA/decade, limit of detection of 17.6 nM, and excellent coefficient of variation of 0.93−3.32% across standards (n = 4). In real plant trials, stem sap from S. minor-inoculated peanut plants produced significantly elevated voltammetric responses relative to healthy and Nothopassalora personata controls as early as five days post-inoculation (dpi), enabling longitudinal monitoring through 20 dpi (p < 0.001). Oxalate-equivalent mapping showed progressive increases in infected plants, reaching mM levels by 20 dpi, while the validated sensor readings agreed with those of a commercial assay during oxalate-linked S. minor detection in peanut plants. To our knowledge, this is the first demonstration of a 3D-printed, Pt-based electrochemical platform validated with peanut plants for early, metabolite-linked detection of Sclerotinia blight, providing a practical foundation for point-of-need surveillance and precision disease management in peanut production systems.