Biology subjects

Camiletti, B. X.

Publications and source records attributed to Camiletti, B. X..

4 recordsLinked to original sources

bioRxiv2026

Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning

Red crown rot (RCR), caused by Calonectria ilicicola, is an emerging soybean disease in the U.S. Midwest for which scalable approaches to characterize within-field disease distribution are lacking. This study evaluated high-resolution PlanetScope satellite imagery for mapping RCR-affected soybean canopies across 15 commercial fields in Illinois surveyed during the 2024 and 2025 growing seasons. A total of 2,921 georeferenced canopy plots were classified as asymptomatic or RCR-affected and paired with six multispectral bands and seven vegetation indices. Spectral differences between classes were evaluated using linear mixed-effects models, and seven machine-learning classifiers representing linear, tree-based, neural-network, kernel, and probabilistic approaches were compared using spatially independent leave-one-field-out cross-validation. RCR-affected canopies exhibited increased reflectance in the visible and red-edge regions, reduced near-infrared reflectance, and lower vegetation-index values relative to asymptomatic canopies. All classifiers showed strong discrimination, with ROC-AUC values ranging from 0.963 to 0.982. Regularized logistic regression achieved the highest overall performance, with an accuracy of 0.945, balanced accuracy of 0.945, F1-score of 0.948, and ROC-AUC of 0.982 at the optimized decision threshold. Permutation analysis identified EVI, NDVI, and red reflectance as the most influential predictors across representative model architectures. Satellite-derived probability and classification maps generally corresponded with symptomatic canopy patterns observed in high-resolution UAV imagery, although mixed pixels reduced precision near disease-patch boundaries. These results demonstrate the potential of high-resolution satellite imagery for within-field mapping of RCR-associated canopy symptoms across independent commercial soybean fields.

plant biology↗

bioRxiv2026

High-Resolution Melting (HRM) Assay for Molecular Detection of Calonectria ilicicola in Soybean

Calonectria ilicicola, an emerging and economically important soilborne pathogen in the U.S. Midwest, causes red crown rot (RCR) disease of soybean. It is essential to have an early and accurate detection method for effective management of RCR, since disease symptoms are nearly identical to those of other soybean diseases. In this study, we developed and validated a high resolution melting (HRM) assay targeting the translation elongation factor 1&alpha (TEF-1&alpha) gene for the specific detection of C. ilicicola. The assay was evaluated using a specificity panel of 78 DNA samples, including 67 fungal and oomycete DNA samples and 11 soybean host-DNA samples, as well as DNA from 56 field-collected symptomatic and asymptomatic soybean tissues. The HRM assay generated distinct melting profiles and, where applicable, absence or delayed amplification that reliably differentiated C. ilicicola from non target fungi and oomycetes. Additionally, the marker distinguished three haplotypes among C. ilicicola isolates, consistent with nucleotide polymorphisms in the TEF-1&alpha target region. The assay demonstrated strong quantitative performance, exhibiting high linearity (R^2= 0.9992) and acceptable amplification efficiency (94.05%) across a broad DNA concentration range. In field samples, HRM based detection aligned with disease status, accurately identifying C. ilicicola in symptomatic plants and no detection in asymptomatic tissues. Overall, this HRM assay provides a rapid, sensitive, and reliable diagnostic tool for routine molecular detection of C. ilicicola.

pathology↗

bioRxiv2026

Development and validation of methods to assess red crown rot (Calonectria ilicicola) severity in soybean: standard area diagram set for roots and diagrammatic scale for canopy

Red crown rot of soybean (RCR), caused by Calonectria ilicicola, is an emerging soilborne disease whose quantification is challenging due to its complex symptom development across root and foliage levels. This study developed and evaluated a multi-scale framework to improve the assessment of RCR severity from controlled environments to field conditions using root imaging and standardized visual scales. Under controlled conditions, a standard area diagram (SAD) for root necrosis was developed and validated, and SAD-assisted evaluations significantly improved accuracy, precision, and inter-rater agreement compared with unaided assessments. In field conditions, a diagrammatic symptom scale (DSS) was developed using consensus-rated images from experts and showed high reliability, repeatability, and reproducibility across 18 raters, with strong intra- and inter-rater agreement. This study developed and evaluated complementary methods to improve the assessment of RCR severity from controlled environments to field conditions using root imaging and standardized visual scales.

plant biology↗

bioRxiv2026

Yield losses associated with peanut smut incidence in Argentina: a quantitative synthesis across field studies

Peanut smut, caused by Thecaphora frezzii, is an important constraint to peanut production in Argentina, but quantitative estimates of yield losses across environments remain limited. We quantified the relationship between disease incidence and kernel yield using 922 observations from 26 field studies conducted in Cordoba, Argentina, between 2021 and 2025. Study-specific incidence-yield relationships were analyzed using linear regression, random-effects meta-analysis, and linear mixed-effects models. Peanut smut incidence was consistently associated with yield reduction across studies. The estimated damage coefficient ranged from 24.2 to 28.7 kg ha-{superscript 1} per 1% increase in disease incidence, corresponding to a relative yield reduction of 0.74-0.87% of attainable yield. In contrast, attainable yield varied markedly among studies, ranging from 1,370 to 5,409 kg ha-{superscript 1}. Although an exploratory segmented analysis suggested a breakpoint near 12% incidence, subsequent moderator analyses, study- specific regressions, and normalized response curves provided no evidence of a biologically meaningful change in the damage coefficient across incidence or yield classes. These results indicate that differences among environments were primarily associated with attainable yield rather than with changes in the magnitude of disease-associated yield loss. The resulting damage function provides a quantitative basis for yield-loss assessment and disease management in peanut.

plant biology↗