Biology subjects

Paredes, J. A.

Publications and source records attributed to Paredes, J. A..

6 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

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↗

bioRxiv2026

A standard area diagram for potato common scab: comparable performance of image- and object-based validation

Potato common scab (Streptomyces sp.) is an economically important disease that reduces the quality and market value of tubers. A key aspect in developing management strategies involves accurately quantifying the disease. Due to the three-dimensional nature of the tuber and the heterogeneous distribution of lesions across its surface, visual estimates of severity can be challenging. Therefore, the objectives of this study were to develop and validate a standard area diagram (SAD) for estimating common scab severity on potato tubers and to compare validation outcomes obtained using real tubers and digital images. A SAD comprising six severity levels (from 1.3 to 66.8%) was developed based on image analysis of naturally infected tubers. Validation was conducted using two complementary approaches in which inexperienced raters evaluated either real potato tubers or digital images of the same tubers under unaided and aided conditions. Accuracy, bias components, and inter-rater reliability were quantified using absolute error metrics, Lins concordance correlation coefficient, intraclass correlation coefficients, and overall concordance correlation coefficients. Use of the SAD significantly improved accuracy, reduced systematic bias, and increased inter-rater reliability across both validation approaches. No significant differences were detected between assessments conducted on real tubers and images, although image-based evaluations showed a slight, non-significant tendency toward reduced scale and location bias under aided conditions. These results demonstrate that a dimension-aware SAD integrating information across the full tuber surface enhances the reliability and reproducibility of visual severity assessments and supports the use of image-based evaluations for training, large-scale surveys, and remote or collaborative applications involving three-dimensional plant organs.

plant biology↗

bioRxiv2025

LLS-SevEst - Late leaf spot severity estimator. A machine learning approach to assessing Nothopassalora personata in peanut.

Late leaf spot (LLS), caused by Nothopassalora personata, is the most damaging foliar disease in peanut production worldwide, leading to significant yield losses if not properly managed. Accurate disease severity assessment is crucial for evaluating fungicide efficacy and implementing effective management strategies. This study aimed to develop and validate an automated image analysis model, LLS-SevEst, for quantifying LLS severity in peanut leaves. A dataset of 190 scanned leaf images was analyzed using three approaches: a fixed threshold-based segmentation, morphological preprocessing, and K-means clustering. Exploratory analyses revealed distinct brightness patterns between healthy and diseased tissues, guiding the development of classification functions. The threshold-based model yielded high false positive rates due to its inability to account for natural leaf variation, while the morphological preprocessing method improved segmentation marginally but still required manual adjustments. The K-means clustering approach achieved superior segmentation by objectively differentiating healthy tissue, lesions, and background, and showed high potential for automated, reproducible disease severity estimation. Future work should focus on integrating deep learning and expanding the dataset to improve model robustness and adaptability to other foliar pathosystems.

plant biology↗

bioRxiv2023

Peanut Smut: A scientometric analysis for a pathosystem that concerns the Argentine peanut industry.

Since its first report in commercial batches in 1995, the prevalence and yield impact caused by smut disease have increased rapidly in peanut fields. At the same time, various working groups have studied this pathosystem using different approaches, contributing to the scientific knowledge of the disease. By recognizing the importance of a thorough bibliographic review and meticulous organization of information, the process of initiating new research projects becomes more effective. In light of this, the aim of this work was to provide a comprehensive scientometric analysis of the evolution of peanut smut research, spanning from its inception to the current day. For this purpose, we compiled bibliographic data about the disease and extracted information to calculate metrics. We observed that a smaller proportion of the scientific production was presented in peer-reviewed journals, the prevalent topics were epidemiology and breeding, and the collaborative endeavors were crucial for the scientific advancement in the study of this pathosystem. Additionally, the researchers with the most significant presence in the publications, the involved institutions, and the impact of the produced papers, among other trends were identified. Although there have been many scientific-technological advances in peanut smut over the years, this information is not reflected in scientific papers in peer-reviewed journals, which represents a great challenge for researchers involved in this topic. It is crucial to continue generating knowledge that contributes to the integrated management of this complex pathosystem. This will prevent further yield losses and the spread of the pathogen to new production areas.

plant biology↗