Development and validation of a standard area diagram (SAD) set for assessing Alternaria black spot severity in pecan leaves

Development and validation of a standard area diagram (SAD) set for assessing Alternaria black spot severity in pecan leaves | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a standard area diagram (SAD) set for assessing Alternaria black spot severity in pecan leaves María Victoria Coronel, Franca Denise Carrasco, Ruth Mariela Kaen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6655217/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Pecan ( Carya illinoinensis ) cultivation is expanding in Argentina, with Catamarca Province emerging as a significant production region. However, fungal diseases such as Alternaria black spot (ABS), caused by Alternaria spp., pose an increasing threat to crop yield and health. Considering that disease quantification is crucial for epidemiological studies and management, this study aimed to design and validate a standard area diagram (SAD) set to improve the visual estimation of ABS severity on pecan leaves. Using 255 diseased leaves, an eight-image SAD set with severity levels that linearly ranged from 2.2–88.9% was designed. Thirty-four raters participated in the validation process using the online platform TraineR2 in two phases: unaided and aided assessments. The use of the SAD set significantly improved accuracy metrics. Lin’s concordance correlation coefficient (CCC) increased from 0.93 to 0.97, while precision (r) rose from 0.92 to 0.97. Additionally, inter-rater reliability, measured using the intraclass correlation coefficient (ICC), improved from 0.86 to 0.93. This study demonstrates the effectiveness of the SAD set tool in enhancing the accuracy and consistency of ABS severity estimations, highlighting its potential as a practical resource for pecan producers and researchers. Carya Illinoinensis foliar disease phytopathometry epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Pecan ( Carya illinoinensis ), a member of the family Juglandaceae , is a tree native to the south-central and southeastern United States and northeast Mexico (Sparks 2005 ; Nadler et al. 2017 ). It is the most commercially cultivated species within the genus Carya , recognized as an important nut and woody oil tree worldwide (Sun y He 1982; Grauke et al. 2016 ; Huang et al. 2019 ). In Argentina, pecan cultivation is an emerging and expanding fruit production system. According to data from the Argentine Pecan Cluster (CAPPECAN 2024), there are approximately 10,000 hectares dedicated to pecans in the country, yielding over 2,000 tons of production. High international market demand and its excellent nutritional value compared to other nuts make pecan a highly profitable crop (Atanasov et al. 2018 ; Castillo et al. 2018 ). In Catamarca Province, commercial pecan cultivation began in 2016, with the current planted area reaching approximately 458.4 hectares and plans for further expansion. Catamarca ranks as the second-largest province in planted area, following Entre Ríos, the leading producer province (National Institute of Statistics and Census 2018). From a plant health perspective, pecan production faces significant challenges due to fungal diseases, which are among the most damaging threats. Key diseases affecting pecan health include pecan scab ( Venturia effusa , syn. Cladosporium caryigenum ), vein spot ( Gnomia nerviseda ), downy spot ( Mycosphaerella caryigena ), brown leaf spot ( Ragnhildiana diffusa ), shuck and kernel rot ( Phytophthora cactorum ), pink mold ( Trichothecium roseum ), cankers ( Phomopsis sp.), anthracnose ( Colletotrichum gloeosporioides ), wilt ( Fusarium spp.), and black spot disease ( Alternaria spp.) (Sanderlin 1983 ; Reilly et al. 1998 ; Mantz et al. 2009 ; Mantz et al. 2009 ; Mantz et al. 2015 ; Samaniego-Gaxiola et al. 2015 ; Noelting et al. 2016 ; Madero et al. 2016 ; Poletto et al. 2021 ). These diseases can significantly impact on pecan production, reducing nut quality. Such is the case of Alternaria black spot (ABS), which has recently become an emerging foliar fungal disease of pecan in producing countries such as South Africa and China. This disease can induce premature defoliation and nut abortion. It can also retard growth and decrease tree vigor over time (Yan et al. 2022 ; Achilonu 2023; Fan et al. 2024 ). In 2021 in pecan crops in the Central Valley of Catamarca, symptoms associated with ABS were observed with a high incidence rate. Symptoms included irregular necrotic lesions and dark brown to black spots on the leaf blades and fruit surfaces. Leaves and fruit with symptoms associated with the disease were collected to confirm the etiological agent. The samples were analyzed at the Plant Pathology Laboratory of the National University of Catamarca (FCA, UNCA). Morphological characterization of the pathogen on PDA medium and the completion of Koch's postulates identified Alternaria sp. as the causal agent. In Argentina, limited studies have been conducted to assess or manage the impact of ABS on pecan production. Accurate quantification of disease severity is critical for conducting epidemiological studies, evaluating cultivar resistance, and developing effective management strategies, including fungicide applications (Bock et al. 2010 ; Pereira et al. 2020 ). Although advanced methods like remote sensing and image analysis are available (Nomura and Oki 2021 ), visual estimation remains widely utilized. This traditional approach involves assigning a value (e.g., a score or percentage) to the severity of symptoms based on the rater's visual perception (Bock et al. 2021 ). Visual estimation is both time- and cost-efficient, requiring no specialized infrastructure or expensive equipment. However, this method has limitations, including errors that arise from the subjective nature of the rater's assessment abilities (Bock et al. 2020 ; Del Ponte et al. 2021 ). Currently, two primary tools are used to enhance the accuracy of visual disease severity estimation. The first is training systems, which have proven effective in improving visual assessment skills. Sanabria-Velazquez et al. ( 2023 ) emphasized the value of training systems in enhancing the ability to estimate the severity of septoria leaf spot in stevia. These findings align with recent results reported by Cazón et al. ( 2025 ), highlighting the significant improvements in accuracy and precision for peanut late leaf spot assessments after just a few training sessions. Currently, various computer-based tools are available in online training interfaces. Notable examples include the TraineR2 system (Del Ponte 2023 ), SteviaScout (Sanabria-Velazquez et al. 2023 ), and the Field Crop Disease and Insect Defoliation Severity Training Tool for field crops (Sisson and Mueller 2023 ). The second tool, and likely the most commonly used in phytopathometry to enhance the rater's ability, is the standard area diagrams (SAD) set (Mazaro et al. 2006 ; Del Ponte et al. 2021 ; Bock et al. 2022 ; Lavilla et al. 2022 ). These consist of sets of illustrations representing incremental percentage severity values, designed to assist raters in accurately interpolating disease severity percentages (Nutter et al. 1993 ; Book et al. 2016; Del Ponte et al. 2017 ). Numerous studies have demonstrated the benefits of SAD set in reducing bias and improving accuracy (Nutter et al. 1998; Del Ponte et al. 2021 ; Bock et al. 2022 ). Despite being over a century old, SADs continue to be widely used. According to the SADBank, a curated repository of SAD studies ( https://edelponte.shinyapps.io/sadbank/ ) , more than 187 SADs for fungal, viral, bacterial, and oomycete diseases were published and validated between 1993 and 2025. This consistent frequency of publication highlights the enduring relevance and utility of these tools, even amidst advancements in modern technologies. Given the lack of standardized tools for quantifying the impact of ABS on pecan leaves in Argentina, this study aims to design and validate a standard area diagram (SAD) to improve the accuracy and reliability of visual disease severity estimation. This tool will contribute to a better understanding and management of this emerging pathology within Argentina's pecan production system. Materials and Methods SAD development and validation. A total of 255 leaves displaying various levels of ABS severity were collected in May 2024 from 15-year-old pecan trees in Capayán, Catamarca, Argentina (28° 41’ 43” S, 65° 56’ 09” W). These leaves were taken to the laboratory of Plant Pathology of the Catamarca National University and photographed using a 50mp smartphone on a blue background. The images were then processed using Adobe Photoshop software (2011) to eliminate shadows cast by leaf edges on the background which could affect severity measurement by the software. Each image was saved in JPG format at a resolution of 300 dpi. The percentage of leaf area affected by Alternaria sp. was quantified using the "analyze objects" function of the Pliman package (Olivoto 2022 ) in R (R Core Team, 2022 ). This function automates image analysis by applying segmentation and quantification techniques to distinguish diseases from healthy tissue. The SAD set was designed in Microsoft PowerPoint (Microsoft 2024 ) considering the minimum and maximum severities observed in the samples collected. The diagrams were distributed at linear intervals, and an additional diagram was included at low severity levels to reduce overestimation (Del Ponte et al. 2017 ; Liu et al. 2019 ; Cazón et al. 2025 ). SAD set validation was performed using the online training platform TraineR2 ( https://delponte.shinyapps.io/traineR2/ ) (Del Ponte 2023 ). This application, developed in the R environment (R Core Team 2022 ) with the Shiny package (Chang et al. 2023 ), incorporates real photographs of diseased leaves, each annotated with their corresponding severity percentage. On its homepage, the right-hand panel contains three tabs. The first tab ( Instruction ) provides usage guidelines; the second tab ( Estimates ) displays the actual severity values and those estimated during the training; and the third tab ( Plot and Accuracy ) presents key statistical parameters during training, such as Lin CCC, Bias Coefficient, Precision, and training time. This tab also provides a real-time graph plotting actual versus estimated severity. To ensure unbiased results during the validation process, the 'Estimate' and 'Plot and Accuracy' tabs remained hidden at all times, preventing evaluators from accessing statistical parameters or results during their assessments. Thirty-five representative images of ABS-affected leaves were included in November 2024 in the TraineR2 database. The validation process involved 34 raters with no prior experience using SADs. Among the raters, 19 were affiliated with the Centro de Investigaciones Agropecuarias (CIAP-INTA), located in Córdoba province, while the remaining 15 were from the Facultad de Ciencias Agropecuarias at Universidad Nacional de Catamarca (FCA-UNCA). The methodology followed the protocol described by Cazón et al. ( 2025 ) and to maintain impartiality in the evaluations, all participants in the study had no prior exposure to this online database. Raters performed the task on their personal computers, without time restrictions, and received a brief tutorial on system usage before beginning the assessment. The validation process was conducted in two phases: first, the raters assessed disease severity without SAD assistance. Upon completion, one of the study's authors downloaded the spreadsheets containing accuracy metrics from TraineR2 to prevent raters from accessing their own results. After a ten-minute interval, the second phase started, during which raters performed the same task, but with SAD assistance this time. For raters affiliated with CIAP-INTA, the SAD was provided as a full-color printed A4 sheet, while for the FCA-UNCA raters, the SAD was projected onto a screen. Accuracy and reliability. The absolute error (the difference between the estimated and actual severity) and its standard deviation (indicating error variability) were calculated for each rater across different assessment rounds (Del Ponte 2023 ). The agreement level between the actual and estimated severity by raters was determined by calculating Lin’s concordance correlation coefficient (Lin’s CCC) (Lin 1989 ; Madden et al. 2007 ; Del Ponte et al. 2017 ). Key accuracy metrics, including overall accuracy (ρc), generalized bias coefficient (Cb), and precision (Pearson's r), were calculated and displayed in TraineR2 after each evaluation round. The inter-rater reliability was evaluated using the intraclass correlation coefficient (ICC) (Shoukri and Pause 1999 ) based on the 95% confidence interval, and the overall concordance correlation coefficient (OCCC), an improved Lin’s CCC method for multiple raters (Barnhart et al. 2002 ). The ICC was computed using the "ICC" function from the psych R package (Revelle 2020 ), with the “lmer” parameter set to “TRUE” to estimate confidence intervals and reliability scores (Cazón et al. 2025 ; Pereira et al. 2020 ). The OCCC was calculated with the "epi.occc" function from the epiR package in R (Stevenson et al. 2019 ). SAD effect on accuracy. Accuracy (ρc), precision (Pearson's r), and bias coefficient (Cb) during the SAD validation were modeled using generalized linear mixed-effects models, where the interaction between aid (Unaided, Aided) was included as a fixed effect, and the raters as random intercepts. The quality of the model fit was checked via simulation of the residuals provided by the DHARMa R package, which provides p-values for assessing the normality of residuals, overdispersion, and outliers (Hartig 2021 ). Significant differences between the means resulting from the different stages of the validation and training processes were compared using Tukey's mean comparison test. Results Following the recommendations of Del Ponte et al. ( 2017 ), the developed SAD set consists of eight true-color representative images, ranging from 2.2–88.9% severity (Fig. 1 ). As previously described, approximately 15% intervals were defined between diagrams, with an additional diagram at 9.1% severity inserted between the minimum (2.2%) and 15.2% severity, resulting in the "amended-linear" percent scale as suggested by Liu et al. ( 2019 ). No statistically significant interaction was observed between the laboratory affiliation and the aided results during the second phase of the SAD validation process. Thus, all raters were treated as a single group. The use of the SAD tool significantly reduced the absolute error (P < 0.05) and minimized the dispersion of estimates compared to unaided evaluations (Fig. 2 ). Specifically, the standard deviation decreased from 9.66 (unaided) to 6.25 (aided) (Table 1 ), improving consistency in severity estimation across raters. Table 1 Effect of the standard area diagram (SAD) to improve the visual severity estimation of ABS on the LCCC (c), precision (r), bias coefficient (Cb), error, standard deviation (Sd), agreement between raters described by the intraclass correlation coefficient (ICC) at 95% of the confidence interval (95% CI) and meantime of severity estimation per image. ρ Unaided 1 Aided 1 ρ c 0.93a 0.97b r 0.92a 0.97b C b 0.97a 0.99b Error 0.09 -0.22 Sd 9.66 6.25 ICC (95% CI) 0.86 (0.80–0.92) 0.93 (0.90–0.96) 1 Means followed by a common letter are not significantly different by the Tukey-test at the 5% significance level. In terms of accuracy, Lins´CCC increased from 0.93 (unaided) to 0.97 (aided), representing a notable gain of 0.04 points. Precision, measured by Pearson’s r also improved by 0.05, rising from 0.92 (unaided) to 0.97 (aided). These results underscore the effectiveness of SAD in enhancing the alignment between estimated and actual severity values. Among the 34 evaluators who participated in the validation process, 29 exhibited improvements in precision after using the SAD tool. Four evaluators maintained their initial levels of precision, while only one evaluator experienced a slight decrease in performance, indicating the robustness of the SAD tool implementation. After the validation process, both the ICC and OCCC parameters showed similar values, increasing from 0.86 (unaided) to 0.93 (aided). For that reason, Table 1 only provides information on the ICC value. These results highlight the tool's ability to reduce variability among evaluators and improve agreement in severity assessments. Discussion The nut sector is one of the most promising economies in Argentina. Pecan production demonstrates capabilities to boost the sector by the availability of cultivars adapted to different soil and climatic conditions in the country, which drives the production of this crop in several regions, such as Northeast, Central, Patagonia, and Northwest (Bischoff et al. 2020). In Catamarca province, pecan farming began less than a decade ago, leaving many production-related challenges yet to be addressed, including plant health issues and their impact on yield. Among these issues, ABS has recently been identified as a concern. As previously mentioned, identifying and quantifying diseases are crucial for understanding their impact on crops and determining the appropriate management strategies (Del Ponte et al. 2024 ). In the case of ABS, its presence was initially overlooked following the crop's introduction due to the striking similarity of its symptoms to those caused by zinc deficiency. Because the pecan crop has a greater need for Zn than most other fruit and nut trees (Ojeda-Barrios et al. 2012 ; Heerema 2013 ), and the availability of this microelement is low in soils such as those of the central valley of Catamarca (ph > 7.0). However, clear distinguishing features exist between these two conditions. Lesions caused by zinc deficiency are typically small, necrotic, and uniformly distributed across the entire leaf, reflecting a generalized physiological imbalance in the plant. In contrast, lesions caused by Alternaria sp. are generally larger, irregular, and exhibit an aggregated pattern, with spots concentrated in specific areas of the leaf due to the localized activity of the pathogen (Fig. 5 ). These symptomatic patterns are well defined in our SAD, so in addition to providing support to quantify the disease, it allows us to identify it more clearly. While there is a SAD for estimating the severity of brown leaf spots (Poletto et al., 2020), we identified several reasons why the development of a new one was necessary for this pathosystem. First, the SAD designed by Poletto et al. (2020) was specifically developed to assist in severity assessments of brown leaf spot caused by Ragnhildiana diffusa in pecan. In this case, the diagrams do not allow for precise visualization of symptoms, as brown-colored markings represent them. Additionally, the highest severity levels include chlorotic areas around the lesions, which may introduce difficulties in symptom recognition. In contrast, our SAD incorporates images of actual lesions caused by a different pathogen ( Alternaria sp.), with clearly defined boundaries between diseased and healthy leaf tissue. Other considerations are related to the severity intervals between diagrams representing each SAD. Although both SADs contain eight diagrams, our SAD employs linear severity intervals and includes an additional diagram for lower severity levels to minimize overestimation at these early disease stages Del Ponte et al. 2017 ; Liu et al. 2019 ; Cazón et al. 2025 . This feature aligns with the best practices for developing diagrammatic scales, as recommended by Del Ponte et al. ( 2017 ). Overall, we believe the new SAD offers a more accurate and standardized approach for assessing the severity of Alternaria leaf spot in pecan. The inclusion of real lesion images, well-defined linear severity intervals between diagrams, and its specific design for evaluating a disease caused by a different pathogen are key features that justify the development and adoption of this tool for disease assessments. Regarding the methodology used in this work, the validation process of our SAD was conducted using the TraineR2 system, which proved to be highly effective for this purpose (Cazón et al. 2025 ). Initially, 35 images were included in the system for training disease severity estimation, and these same images were consistently employed throughout the validation process. Although Del Ponte et al. ( 2017 ) recommend using 40 to 50 images for this purpose, we opted to work with the 35 images available. This limitation was mitigated by including a substantial cohort of 34 evaluators from two different locations, ensuring robust validation of the SAD. Despite the differences in the second stage of the validation process between laboratories, no statistically significant variations were detected in the parameters related to accuracy. For this pathosystem, the size of the SAD set and its presentation format to raters (whether as a printed scale on A4 sheets or projected on a screen) did not impact the results obtained. However, in a previous study, Pethibridge and Nelson (2018), suggest that the size and type of device used can influence the accuracy of evaluators, particularly when employing mobile devices. For this reason, the use of mobile phones was not considered during our validation process to ensure consistency and minimize potential biases. It is worth noting that the way a SAD set is presented may influence visual perception, depending on factors such as screen resolution, lighting conditions, time of assessment, and viewing distance (Sanabria-Velazquez et al. 2023 ). Although our findings did not show an impact on this specific system, future studies should explore these variables further by using both digital or printed images of the SAD under various environmental conditions. This approach could help us gain insights into the reliability and reproducibility of this tool. As shown in Fig. 2 , the dispersion of absolute errors is significantly reduced with the use of SAD, as evidenced by the decrease in the standard deviation from 9.66 (unaided) to 6.25 (aided) (Table 1 ). Regarding the absolute error values, the mean reveals negative values in aided evaluations (-0.22), indicating a slight systematic bias introduced by the scale. This bias tends to underestimate severity values, particularly within the mid-range (40–70% severity), as illustrated by the red smooth line in Figure XX. Given the characteristics of this pathosystem, along with recommendations from Liu et al. ( 2019 ) to include additional diagrams for lower severity levels, it may also be advisable to incorporate another diagram for mid-range severity values to address this slight bias. Nevertheless, considering the significant improvements observed in other parameters (LCCC, r, standard deviation, ICC), this bias can be considered negligible. The SAD developed in this study demonstrated a significant improvement in raters' abilities. Among the 34 raters who participated in the assessment process, only one experienced a slight decrease in performance. This outcome is consistent with findings in the literature, which suggest that raters with high baseline accuracy levels may exhibit minimal or negative changes when using SADs (Del Ponte et al. 2021 ; Cazón et al. 2025 ). In this study, the rater in question initially achieved an LCCC of 0.96 without assistance, which slightly declined to 0.93 when using the SAD (Fig. XX). Regarding precision (Pearson’s r), we observed a statistically significant increase from 0.92 (unaided) to 0.97 (aided). While this improvement of 0.05 may seem modest, it aligns with data reported by Del Ponte et al. ( 2021 ). Their meta-analytical approach revealed that symptom patterns influence disease severity assessments. According to their findings, pathosystems characterized by a low number of large, coalescent lesions are easier to evaluate than those exhibiting more complex symptom patterns. As a result, severity assessments of pathosystems with simpler lesion patterns tend to have a higher baseline precision than those with numerous small lesions. This explains the benefits of using our SAD for diseases similar to Alternaria black spot. Conclusion This study highlights the effectiveness of the SAD developed for assessing Alternaria black spot in pecan leaves. The tool significantly improved evaluator accuracy and precision, underscoring its potential to enhance disease severity assessments in epidemiological studies and management strategies. Evaluating the adaptation of this tool under different environmental conditions would further improve its applicability and robustness, contributing to more effective disease management in pecan cultivation and beyond. Declarations Author's contribution MVC conceptualized the study, collected leaf samples, contributed to the SAD validation process, conducted the bibliographic research, and wrote the manuscript. FDC, RMK, and JAP contributed to the SAD validation process, participated in data analysis, and co-wrote the manuscript. SM and CC provided critical feedback and contributed to manuscript writing. NBL and LIC conceptualized the study, coordinated the SAD validation process and data analysis, and co-wrote the manuscript. Data availability The datasets generated during and/or analyzed during the current study are available at the following repository link: https://repositorio.inta.gob.ar/xmlui/handle/20.500.12123/22231 Conflict of interest All authors declare that they have no conflicts of interest. Acknowledgments We wish to thank INTA and Universidad Nacional de Catamarca for providing resources for compiling this project. 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New Disease Reports 33 (3400): 9. https://doi.org/10.5197/j.2044-0588.2016.033.009 Nomura R and Oki K (2021) Development of health monitoring method for pecan nut trees using side video data and computer vision. Optical Revie , 28:730–737 . https://doi.org/10.1007/s10043-021-00694-0 Nutter FW Jr, Gleason ML, Jenco JH, Christians NL (1993) Accuracy, intrarater repeatability, and interrater reliability of disease assessment systems. Phytopathology 83: 806–812. Olivoto T (2022) Lights, camera, pliman! An R package for plant image analysis.” Methods in Ecology and Evolution 13(4): 789-798. https://doi.org/10.1111/2041210X.13803 Ojeda-Barrios D, Abadía J, Lombardini L, Abadía A, Vázquez S (2012) Zinc deficiency in field-grown pecan trees: Changes in leaf nutrient concentrations and structure. Journal of the Science of Food and Agriculture , 92 (8), 1672–1678. https://doi.org/10.1002/jsfa.5530 Pereira WEL, de Andrade SMP, Del Ponte EM, Esteves MB, Canale MC, Takita MA, Coletta-Filho H Della, De Souza AA (2020) Severity assessment in the Nicotiana tabacum-Xylella fastidiosa subsp. pauca pathosystem: design and interlaboratory validation of a standard area diagram set. Tropical Plant Pathology 45 (6): 710–722. https://doi.org/10.1007/s40858-020-00401-5 Poletto T, Muniz MFB, Fantinel VS, Da Silva Martello L, Graciolli Savian L, Harakava R, Guatimosim E, Poletto I, Stefenon VM (2021) Characterization of the brown leaf spots pathosystem in Brazilian pecan orchards: Pathogen morphology and molecular identification. Annals of Forest Research 64 (1): 75–86 https://doi.org/10.15287/afr.2021.1957 R Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Grauke LJ, Wood BW, Harris MK (2016) Crop vulnerability: Carya. Hortsci 51:653–63. Reilly CC, Hotchkiss MW, Hendrix FF (1998) Phytophthora shuck and kernel rot, a new disease of pecan caused by Phytophthora cactorum Plant Disease 82 (3): 347–349 https://doi.org/10.1094/PDIS.1998.82.3.347 Revelle W (2020) psych: Procedures for Psychological, Psychometric and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.0.9. URL: https://cran.rproject.org/web/packages/psych/ Samaniego-Gaxiola JA, Aguilar-Pérez H, Pedroza-Sandoval A, Samaniego-Gaxiola JA, Aguilar-Pérez H, Pedroza-Sandoval A (2015) Epidemiología de la mancha vellosa ( Mycosphaerella caryigena ) y su impacto en la defoliación del nogal pecanero Revista Mexicana de Fitopatología 33 (2): 211–218 Sanabria-Velazquez, AD, Enciso-Maldonado GA, Maidana-Ojeda M, Diaz-Najera JF, Thiessen LD, Shew HD (2023) Validation of Standard Area Diagrams to Estimate the Severity of Septoria Leaf Spot on Stevia in Paraguay, Mexico, and the USA. Plant Disease 107: 1829-1838. https://doi.org/10.1094/PDIS-07-22-1609-RE Sanderlin RS (1983) Epidemiology and Control of Vein Spot Disease of Pecan Caused by Gnomonia nerviseda . Plant Disease 67 (11): 1209. https://doi.org/10.1094/pd-67-1209 Shoukri MM and Pause CA (1999) Statistical Methods for Health Science 2nd edition. CRC Press Sisson AJ and Mueller DS (2023) Training resource for improvement of visual Assessment of field crop disease and insect defoliation severity. PhytoFrontiers 3: 697-700 Sun Z, He S (1982) The history, present, and prospect of pecan, in China ( Carya illinoensis , Carya cathayensis , cultivation and breeding). Pecan South 9:5. Sparks D (2005) Adaptability of pecan as a species. HortScience 40 (5): 1175–1189. https://doi.org/10.21273/hortsci.40.5.1175 Stevenson M, Nunes T, Heuer C, Marshall J, Sanchez J, Thornton R, Reiczigel J, Robinson-Cox J, Sebastiani P, Solymos P, Yoshida K, Jones G, Pirikahu, Firestone S, Kyle R, Popp J, Jay M, Reynard C (2019) epiR: tools for the analysis of epidemiological data. R package version 1.0–4. https://CRAN.R-project.org/package=epiR Yan L, Yang X, Wang Z, Zhu H, Qian Y, & Wu W (2022) First report of Alternaria tenuissima causing leaf black spot on pecan in China. Plant Disease, 106 (6): 1748. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 28 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 20 May, 2025 Editor assigned by journal 20 May, 2025 First submitted to journal 13 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6655217","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459315490,"identity":"5fb512a7-8716-4818-9bf9-bcabc10358a1","order_by":0,"name":"María Victoria Coronel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Victoria","lastName":"Coronel","suffix":""},{"id":459315491,"identity":"b9c934ee-ed32-4739-8d85-6b207ef02b5a","order_by":1,"name":"Franca Denise Carrasco","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Franca","middleName":"Denise","lastName":"Carrasco","suffix":""},{"id":459315492,"identity":"084400a8-6be2-4f1f-a5ef-f48657804cf2","order_by":2,"name":"Ruth Mariela Kaen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"Mariela","lastName":"Kaen","suffix":""},{"id":459315493,"identity":"2933090f-1dfe-486b-a55f-c07d9f900318","order_by":3,"name":"Juan Andres Paredes","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Andres","lastName":"Paredes","suffix":""},{"id":459315494,"identity":"64f34728-0c87-4988-80cc-bddff67364f6","order_by":4,"name":"Sami Jorge Michereff","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"Jorge","lastName":"Michereff","suffix":""},{"id":459315495,"identity":"bae6872f-a1c0-4872-ab71-33ca6b1024d4","order_by":5,"name":"Cinthia Conforto","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cinthia","middleName":"","lastName":"Conforto","suffix":""},{"id":459315496,"identity":"7dad9c5f-2810-4197-a168-84fc659c72b7","order_by":6,"name":"Nelson Bernardi Lima","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nelson","middleName":"Bernardi","lastName":"Lima","suffix":""},{"id":459315497,"identity":"511d4f51-dba4-4d45-9276-35cf7a0acc87","order_by":7,"name":"Luis Ignacio Cazón","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACNjBpAOV9qLABkoyNB4jWwjjjTBqIasCrBQUw87YdBjPwauHjX3vww48CG3n+/sPHHs44c95ubfthoC01NtE4HSbxLlmyxyDNcMaBY+kGHypuJ287kwjUciwttwGnljMG0gwGhxMYDvaYSc44czvZ7ABQC2PDYXxajH+DtMgf5jGT5m07l2x2/iEBLfw9ZmBbDI6BtRywM7tB0BYeM0uQXzaeYUsDOiw5wewG0JYEPH6R7z9jfOPHHxt5ufOHj0l8qLCzNzuf/vDBhxobnFoYJBJQ+YlglQkY6pAA/wFUvj0+xaNgFIyCUTAyAQCXpGLUDdQ78QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5973-3338","institution":"INTA: Instituto Nacional de Tecnologia Agropecuaria","correspondingAuthor":true,"prefix":"","firstName":"Luis","middleName":"Ignacio","lastName":"Cazón","suffix":""}],"badges":[],"createdAt":"2025-05-13 12:02:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6655217/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6655217/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83513729,"identity":"551047f1-e08a-404a-99ae-6f40bd5725bc","added_by":"auto","created_at":"2025-05-27 17:42:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4000029,"visible":true,"origin":"","legend":"\u003cp\u003eStandard area diagrams for visual estimation of the severity of Alternaria black spot (ABS), caused by \u003cem\u003eAlternaria \u003c/em\u003esp. in Pecan (\u003cem\u003eCarya illinoinensis\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6655217/v1/e77efd7d0969ca9d411c8b53.png"},{"id":83513727,"identity":"5e4a9026-4503-4dc2-ba2d-c007d7234ffa","added_by":"auto","created_at":"2025-05-27 17:42:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":290343,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot representing the relationship between the rater's absolute errors and the actual severity percentage before using SAD (Unaided) and after using SAD (Aided). Dots represent the individual error. The red line is a smooth conditional mean produced as default using the geom_smooth function in the ggplot2 package of R.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6655217/v1/daba3b70e420cbb5156b87d6.png"},{"id":83514091,"identity":"262f1b37-585d-4465-9193-48ee23e07ca5","added_by":"auto","created_at":"2025-05-27 17:50:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47159,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall accuracy of the rater's estimates of Alternaria black spot (ABS) severity before (Unaided) and after (Aided) using the SAD. Dots represent raters' individual Lin’s concordance correlation coefficient (LCCC). Error bars represent the 95% confidence interval for the mean LCCC. Letters represent the Tukey mean comparison test groupings (5% probability). Different letters represent different groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6655217/v1/9a30e7f740e7c3faa74876c9.png"},{"id":83514706,"identity":"b02a2f51-6ef7-4c77-a78c-81235b857ba0","added_by":"auto","created_at":"2025-05-27 17:58:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110798,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between unaided precision (r) and the gain in precision achieved through the use of SAD. Each dot represents the accuracy gain (calculated as the difference between SAD-aided and SAD-unaided accuracy) for an individual rater. The red line marks the threshold separating accuracy gains (above the line) from accuracy losses (below the line).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6655217/v1/a5e931318b5625d751ac3223.png"},{"id":83514704,"identity":"5c222ec6-446c-4bb5-92b9-21892590526e","added_by":"auto","created_at":"2025-05-27 17:58:50","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":373085,"visible":true,"origin":"","legend":"\u003cp\u003eDifference between lesions caused by Alternaria black spot (\u003cstrong\u003eA\u003c/strong\u003e) and lesions caused by zinc deficiency (\u003cstrong\u003eB\u003c/strong\u003e) in pecan leaves.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6655217/v1/4b6cba89e4b19efb854e1fdf.jpeg"},{"id":83515125,"identity":"b7563b5f-6267-4282-854b-52c2ea28e944","added_by":"auto","created_at":"2025-05-27 18:06:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4961199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6655217/v1/e1097908-a0bc-4951-ba98-dfb46e14c5c2.pdf"}],"financialInterests":"","formattedTitle":"Development and validation of a standard area diagram (SAD) set for assessing Alternaria black spot severity in pecan leaves","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePecan (\u003cem\u003eCarya illinoinensis\u003c/em\u003e), a member of the family \u003cem\u003eJuglandaceae\u003c/em\u003e, is a tree native to the south-central and southeastern United States and northeast Mexico (Sparks \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Nadler et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is the most commercially cultivated species within the genus \u003cem\u003eCarya\u003c/em\u003e, recognized as an important nut and woody oil tree worldwide (Sun y He 1982; Grauke et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Argentina, pecan cultivation is an emerging and expanding fruit production system. According to data from the Argentine Pecan Cluster (CAPPECAN 2024), there are approximately 10,000 hectares dedicated to pecans in the country, yielding over 2,000 tons of production. High international market demand and its excellent nutritional value compared to other nuts make pecan a highly profitable crop (Atanasov et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Castillo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Catamarca Province, commercial pecan cultivation began in 2016, with the current planted area reaching approximately 458.4 hectares and plans for further expansion. Catamarca ranks as the second-largest province in planted area, following Entre R\u0026iacute;os, the leading producer province (National Institute of Statistics and Census 2018).\u003c/p\u003e \u003cp\u003eFrom a plant health perspective, pecan production faces significant challenges due to fungal diseases, which are among the most damaging threats. Key diseases affecting pecan health include pecan scab (\u003cem\u003eVenturia effusa\u003c/em\u003e, syn. \u003cem\u003eCladosporium caryigenum\u003c/em\u003e), vein spot (\u003cem\u003eGnomia nerviseda\u003c/em\u003e), downy spot (\u003cem\u003eMycosphaerella caryigena\u003c/em\u003e), brown leaf spot (\u003cem\u003eRagnhildiana diffusa\u003c/em\u003e), shuck and kernel rot (\u003cem\u003ePhytophthora cactorum\u003c/em\u003e), pink mold (\u003cem\u003eTrichothecium roseum\u003c/em\u003e), cankers (\u003cem\u003ePhomopsis\u003c/em\u003e sp.), anthracnose (\u003cem\u003eColletotrichum gloeosporioides\u003c/em\u003e), wilt (\u003cem\u003eFusarium\u003c/em\u003e spp.), and black spot disease (\u003cem\u003eAlternaria\u003c/em\u003e spp.) (Sanderlin \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Reilly et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Mantz et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mantz et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mantz et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Samaniego-Gaxiola et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Noelting et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Madero et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Poletto et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These diseases can significantly impact on pecan production, reducing nut quality. Such is the case of Alternaria black spot (ABS), which has recently become an emerging foliar fungal disease of pecan in producing countries such as South Africa and China. This disease can induce premature defoliation and nut abortion. It can also retard growth and decrease tree vigor over time (Yan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Achilonu 2023; Fan et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2021 in pecan crops in the Central Valley of Catamarca, symptoms associated with ABS were observed with a high incidence rate. Symptoms included irregular necrotic lesions and dark brown to black spots on the leaf blades and fruit surfaces. Leaves and fruit with symptoms associated with the disease were collected to confirm the etiological agent. The samples were analyzed at the Plant Pathology Laboratory of the National University of Catamarca (FCA, UNCA). Morphological characterization of the pathogen on PDA medium and the completion of Koch's postulates identified \u003cem\u003eAlternaria\u003c/em\u003e sp. as the causal agent.\u003c/p\u003e \u003cp\u003eIn Argentina, limited studies have been conducted to assess or manage the impact of ABS on pecan production. Accurate quantification of disease severity is critical for conducting epidemiological studies, evaluating cultivar resistance, and developing effective management strategies, including fungicide applications (Bock et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough advanced methods like remote sensing and image analysis are available (Nomura and Oki \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), visual estimation remains widely utilized. This traditional approach involves assigning a value (e.g., a score or percentage) to the severity of symptoms based on the rater's visual perception (Bock et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Visual estimation is both time- and cost-efficient, requiring no specialized infrastructure or expensive equipment. However, this method has limitations, including errors that arise from the subjective nature of the rater's assessment abilities (Bock et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Del Ponte et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, two primary tools are used to enhance the accuracy of visual disease severity estimation. The first is training systems, which have proven effective in improving visual assessment skills. Sanabria-Velazquez et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasized the value of training systems in enhancing the ability to estimate the severity of septoria leaf spot in stevia. These findings align with recent results reported by Caz\u0026oacute;n et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), highlighting the significant improvements in accuracy and precision for peanut late leaf spot assessments after just a few training sessions. Currently, various computer-based tools are available in online training interfaces. Notable examples include the \u003cem\u003eTraineR2\u003c/em\u003e system (Del Ponte \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003eSteviaScout\u003c/em\u003e (Sanabria-Velazquez et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the \u003cem\u003eField Crop Disease and Insect Defoliation Severity Training Tool\u003c/em\u003e for field crops (Sisson and Mueller \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second tool, and likely the most commonly used in phytopathometry to enhance the rater's ability, is the standard area diagrams (SAD) set (Mazaro et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Del Ponte et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bock et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lavilla et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These consist of sets of illustrations representing incremental percentage severity values, designed to assist raters in accurately interpolating disease severity percentages (Nutter et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Book et al. 2016; Del Ponte et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Numerous studies have demonstrated the benefits of SAD set in reducing bias and improving accuracy (Nutter et al. 1998; Del Ponte et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bock et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite being over a century old, SADs continue to be widely used. According to the SADBank, a curated repository of SAD studies (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://edelponte.shinyapps.io/sadbank/\u003c/span\u003e\u003cspan address=\"https://edelponte.shinyapps.io/sadbank/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, more than 187 SADs for fungal, viral, bacterial, and oomycete diseases were published and validated between 1993 and 2025. This consistent frequency of publication highlights the enduring relevance and utility of these tools, even amidst advancements in modern technologies.\u003c/p\u003e \u003cp\u003eGiven the lack of standardized tools for quantifying the impact of ABS on pecan leaves in Argentina, this study aims to design and validate a standard area diagram (SAD) to improve the accuracy and reliability of visual disease severity estimation. This tool will contribute to a better understanding and management of this emerging pathology within Argentina's pecan production system.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eSAD development and validation.\u003c/b\u003e A total of 255 leaves displaying various levels of ABS severity were collected in May 2024 from 15-year-old pecan trees in Capay\u0026aacute;n, Catamarca, Argentina (28\u0026deg; 41\u0026rsquo; 43\u0026rdquo; S, 65\u0026deg; 56\u0026rsquo; 09\u0026rdquo; W). These leaves were taken to the laboratory of Plant Pathology of the Catamarca National University and photographed using a 50mp smartphone on a blue background. The images were then processed using \u003cem\u003eAdobe Photoshop software\u003c/em\u003e (2011) to eliminate shadows cast by leaf edges on the background which could affect severity measurement by the software. Each image was saved in JPG format at a resolution of 300 dpi. The percentage of leaf area affected by \u003cem\u003eAlternaria\u003c/em\u003e sp. was quantified using the \"analyze objects\" function of the \u003cem\u003ePliman\u003c/em\u003e package (Olivoto \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in \u003cem\u003eR\u003c/em\u003e (R Core Team, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This function automates image analysis by applying segmentation and quantification techniques to distinguish diseases from healthy tissue. The SAD set was designed in \u003cem\u003eMicrosoft PowerPoint\u003c/em\u003e (Microsoft \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) considering the minimum and maximum severities observed in the samples collected. The diagrams were distributed at linear intervals, and an additional diagram was included at low severity levels to reduce overestimation (Del Ponte et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Caz\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSAD set validation was performed using the online training platform \u003cem\u003eTraineR2\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://delponte.shinyapps.io/traineR2/\u003c/span\u003e\u003cspan address=\"https://delponte.shinyapps.io/traineR2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Del Ponte \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This application, developed in the \u003cem\u003eR\u003c/em\u003e environment (R Core Team \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with the \u003cem\u003eShiny\u003c/em\u003e package (Chang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), incorporates real photographs of diseased leaves, each annotated with their corresponding severity percentage. On its homepage, the right-hand panel contains three tabs. The first tab (\u003cb\u003eInstruction\u003c/b\u003e) provides usage guidelines; the second tab (\u003cb\u003eEstimates\u003c/b\u003e) displays the actual severity values and those estimated during the training; and the third tab (\u003cb\u003ePlot and Accuracy\u003c/b\u003e) presents key statistical parameters during training, such as Lin CCC, Bias Coefficient, Precision, and training time. This tab also provides a real-time graph plotting actual versus estimated severity. To ensure unbiased results during the validation process, the 'Estimate' and 'Plot and Accuracy' tabs remained hidden at all times, preventing evaluators from accessing statistical parameters or results during their assessments.\u003c/p\u003e \u003cp\u003eThirty-five representative images of ABS-affected leaves were included in November 2024 in the \u003cem\u003eTraineR2\u003c/em\u003e database. The validation process involved 34 raters with no prior experience using SADs. Among the raters, 19 were affiliated with the Centro de Investigaciones Agropecuarias (CIAP-INTA), located in C\u0026oacute;rdoba province, while the remaining 15 were from the Facultad de Ciencias Agropecuarias at Universidad Nacional de Catamarca (FCA-UNCA). The methodology followed the protocol described by Caz\u0026oacute;n et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and to maintain impartiality in the evaluations, all participants in the study had no prior exposure to this online database. Raters performed the task on their personal computers, without time restrictions, and received a brief tutorial on system usage before beginning the assessment. The validation process was conducted in two phases: first, the raters assessed disease severity without SAD assistance. Upon completion, one of the study's authors downloaded the spreadsheets containing accuracy metrics from \u003cem\u003eTraineR2\u003c/em\u003e to prevent raters from accessing their own results. After a ten-minute interval, the second phase started, during which raters performed the same task, but with SAD assistance this time. For raters affiliated with CIAP-INTA, the SAD was provided as a full-color printed A4 sheet, while for the FCA-UNCA raters, the SAD was projected onto a screen.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAccuracy and reliability.\u003c/b\u003e The absolute error (the difference between the estimated and actual severity) and its standard deviation (indicating error variability) were calculated for each rater across different assessment rounds (Del Ponte \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The agreement level between the actual and estimated severity by raters was determined by calculating Lin\u0026rsquo;s concordance correlation coefficient (Lin\u0026rsquo;s CCC) (Lin \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Madden et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Del Ponte et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Key accuracy metrics, including overall accuracy (ρc), generalized bias coefficient (Cb), and precision (Pearson's r), were calculated and displayed in \u003cem\u003eTraineR2\u003c/em\u003e after each evaluation round. The inter-rater reliability was evaluated using the intraclass correlation coefficient (ICC) (Shoukri and Pause \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) based on the 95% confidence interval, and the overall concordance correlation coefficient (OCCC), an improved Lin\u0026rsquo;s CCC method for multiple raters (Barnhart et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The ICC was computed using the \"ICC\" function from the \u003cem\u003epsych R\u003c/em\u003e package (Revelle \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with the \u0026ldquo;lmer\u0026rdquo; parameter set to \u0026ldquo;TRUE\u0026rdquo; to estimate confidence intervals and reliability scores (Caz\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The OCCC was calculated with the \"epi.occc\" function from the \u003cem\u003eepiR\u003c/em\u003e package in R (Stevenson et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSAD effect on accuracy.\u003c/b\u003e Accuracy (ρc), precision (Pearson's r), and bias coefficient (Cb) during the SAD validation were modeled using generalized linear mixed-effects models, where the interaction between aid (Unaided, Aided) was included as a fixed effect, and the raters as random intercepts. The quality of the model fit was checked via simulation of the residuals provided by the \u003cem\u003eDHARMa\u003c/em\u003e R package, which provides p-values for assessing the normality of residuals, overdispersion, and outliers (Hartig \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Significant differences between the means resulting from the different stages of the validation and training processes were compared using Tukey's mean comparison test.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFollowing the recommendations of Del Ponte et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the developed SAD set consists of eight true-color representative images, ranging from 2.2\u0026ndash;88.9% severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As previously described, approximately 15% intervals were defined between diagrams, with an additional diagram at 9.1% severity inserted between the minimum (2.2%) and 15.2% severity, resulting in the \"amended-linear\" percent scale as suggested by Liu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNo statistically significant interaction was observed between the laboratory affiliation and the aided results during the second phase of the SAD validation process. Thus, all raters were treated as a single group.\u003c/p\u003e \u003cp\u003eThe use of the SAD tool significantly reduced the absolute error (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and minimized the dispersion of estimates compared to unaided evaluations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, the standard deviation decreased from 9.66 (unaided) to 6.25 (aided) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), improving consistency in severity estimation across raters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of the standard area diagram (SAD) to improve the visual severity estimation of ABS on the LCCC (c), precision (r), bias coefficient (Cb), error, standard deviation (Sd), agreement between raters described by the intraclass correlation coefficient (ICC) at 95% of the confidence interval (95% CI) and meantime of severity estimation per image.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eρ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnaided\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAided\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eρ\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.80\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.90\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003e Means followed by a common letter are not significantly different by the Tukey-test at the 5% significance level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of accuracy, Lins\u0026acute;CCC increased from 0.93 (unaided) to 0.97 (aided), representing a notable gain of 0.04 points. Precision, measured by Pearson\u0026rsquo;s r also improved by 0.05, rising from 0.92 (unaided) to 0.97 (aided). These results underscore the effectiveness of SAD in enhancing the alignment between estimated and actual severity values.\u003c/p\u003e \u003cp\u003eAmong the 34 evaluators who participated in the validation process, 29 exhibited improvements in precision after using the SAD tool. Four evaluators maintained their initial levels of precision, while only one evaluator experienced a slight decrease in performance, indicating the robustness of the SAD tool implementation.\u003c/p\u003e \u003cp\u003eAfter the validation process, both the ICC and OCCC parameters showed similar values, increasing from 0.86 (unaided) to 0.93 (aided). For that reason, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e only provides information on the ICC value. These results highlight the tool's ability to reduce variability among evaluators and improve agreement in severity assessments.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe nut sector is one of the most promising economies in Argentina. Pecan production demonstrates capabilities to boost the sector by the availability of cultivars adapted to different soil and climatic conditions in the country, which drives the production of this crop in several regions, such as Northeast, Central, Patagonia, and Northwest (Bischoff et al. 2020). In Catamarca province, pecan farming began less than a decade ago, leaving many production-related challenges yet to be addressed, including plant health issues and their impact on yield. Among these issues, ABS has recently been identified as a concern.\u003c/p\u003e \u003cp\u003eAs previously mentioned, identifying and quantifying diseases are crucial for understanding their impact on crops and determining the appropriate management strategies (Del Ponte et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the case of ABS, its presence was initially overlooked following the crop's introduction due to the striking similarity of its symptoms to those caused by zinc deficiency. Because the pecan crop has a greater need for Zn than most other fruit and nut trees (Ojeda-Barrios et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Heerema \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and the availability of this microelement is low in soils such as those of the central valley of Catamarca (ph\u0026thinsp;\u0026gt;\u0026thinsp;7.0). However, clear distinguishing features exist between these two conditions. Lesions caused by zinc deficiency are typically small, necrotic, and uniformly distributed across the entire leaf, reflecting a generalized physiological imbalance in the plant. In contrast, lesions caused by \u003cem\u003eAlternaria\u003c/em\u003e sp. are generally larger, irregular, and exhibit an aggregated pattern, with spots concentrated in specific areas of the leaf due to the localized activity of the pathogen (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These symptomatic patterns are well defined in our SAD, so in addition to providing support to quantify the disease, it allows us to identify it more clearly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile there is a SAD for estimating the severity of brown leaf spots (Poletto et al., 2020), we identified several reasons why the development of a new one was necessary for this pathosystem. First, the SAD designed by Poletto et al. (2020) was specifically developed to assist in severity assessments of brown leaf spot caused by \u003cem\u003eRagnhildiana diffusa\u003c/em\u003e in pecan. In this case, the diagrams do not allow for precise visualization of symptoms, as brown-colored markings represent them. Additionally, the highest severity levels include chlorotic areas around the lesions, which may introduce difficulties in symptom recognition. In contrast, our SAD incorporates images of actual lesions caused by a different pathogen (\u003cem\u003eAlternaria\u003c/em\u003e sp.), with clearly defined boundaries between diseased and healthy leaf tissue. Other considerations are related to the severity intervals between diagrams representing each SAD. Although both SADs contain eight diagrams, our SAD employs linear severity intervals and includes an additional diagram for lower severity levels to minimize overestimation at these early disease stages Del Ponte et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Caz\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e. This feature aligns with the best practices for developing diagrammatic scales, as recommended by Del Ponte et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Overall, we believe the new SAD offers a more accurate and standardized approach for assessing the severity of Alternaria leaf spot in pecan. The inclusion of real lesion images, well-defined linear severity intervals between diagrams, and its specific design for evaluating a disease caused by a different pathogen are key features that justify the development and adoption of this tool for disease assessments.\u003c/p\u003e \u003cp\u003eRegarding the methodology used in this work, the validation process of our SAD was conducted using the \u003cem\u003eTraineR2\u003c/em\u003e system, which proved to be highly effective for this purpose (Caz\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Initially, 35 images were included in the system for training disease severity estimation, and these same images were consistently employed throughout the validation process. Although Del Ponte et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) recommend using 40 to 50 images for this purpose, we opted to work with the 35 images available. This limitation was mitigated by including a substantial cohort of 34 evaluators from two different locations, ensuring robust validation of the SAD.\u003c/p\u003e \u003cp\u003eDespite the differences in the second stage of the validation process between laboratories, no statistically significant variations were detected in the parameters related to accuracy. For this pathosystem, the size of the SAD set and its presentation format to raters (whether as a printed scale on A4 sheets or projected on a screen) did not impact the results obtained. However, in a previous study, Pethibridge and Nelson (2018), suggest that the size and type of device used can influence the accuracy of evaluators, particularly when employing mobile devices. For this reason, the use of mobile phones was not considered during our validation process to ensure consistency and minimize potential biases. It is worth noting that the way a SAD set is presented may influence visual perception, depending on factors such as screen resolution, lighting conditions, time of assessment, and viewing distance (Sanabria-Velazquez et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although our findings did not show an impact on this specific system, future studies should explore these variables further by using both digital or printed images of the SAD under various environmental conditions. This approach could help us gain insights into the reliability and reproducibility of this tool.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the dispersion of absolute errors is significantly reduced with the use of SAD, as evidenced by the decrease in the standard deviation from 9.66 (unaided) to 6.25 (aided) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding the absolute error values, the mean reveals negative values in aided evaluations (-0.22), indicating a slight systematic bias introduced by the scale. This bias tends to underestimate severity values, particularly within the mid-range (40\u0026ndash;70% severity), as illustrated by the red smooth line in Figure XX. Given the characteristics of this pathosystem, along with recommendations from Liu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to include additional diagrams for lower severity levels, it may also be advisable to incorporate another diagram for mid-range severity values to address this slight bias. Nevertheless, considering the significant improvements observed in other parameters (LCCC, r, standard deviation, ICC), this bias can be considered negligible.\u003c/p\u003e \u003cp\u003eThe SAD developed in this study demonstrated a significant improvement in raters' abilities. Among the 34 raters who participated in the assessment process, only one experienced a slight decrease in performance. This outcome is consistent with findings in the literature, which suggest that raters with high baseline accuracy levels may exhibit minimal or negative changes when using SADs (Del Ponte et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Caz\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this study, the rater in question initially achieved an LCCC of 0.96 without assistance, which slightly declined to 0.93 when using the SAD (Fig. XX). Regarding precision (Pearson\u0026rsquo;s r), we observed a statistically significant increase from 0.92 (unaided) to 0.97 (aided). While this improvement of 0.05 may seem modest, it aligns with data reported by Del Ponte et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their meta-analytical approach revealed that symptom patterns influence disease severity assessments. According to their findings, pathosystems characterized by a low number of large, coalescent lesions are easier to evaluate than those exhibiting more complex symptom patterns. As a result, severity assessments of pathosystems with simpler lesion patterns tend to have a higher baseline precision than those with numerous small lesions. This explains the benefits of using our SAD for diseases similar to Alternaria black spot.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the effectiveness of the SAD developed for assessing Alternaria black spot in pecan leaves. The tool significantly improved evaluator accuracy and precision, underscoring its potential to enhance disease severity assessments in epidemiological studies and management strategies. Evaluating the adaptation of this tool under different environmental conditions would further improve its applicability and robustness, contributing to more effective disease management in pecan cultivation and beyond.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMVC\u0026nbsp;\u003c/strong\u003econceptualized the study, collected leaf samples, contributed to the SAD validation process, conducted the bibliographic research, and wrote the manuscript. \u003cstrong\u003eFDC, RMK, and JAP\u0026nbsp;\u003c/strong\u003econtributed to the SAD validation process, participated in data analysis, and co-wrote the manuscript.\u003cstrong\u003e\u0026nbsp;SM and CC\u0026nbsp;\u003c/strong\u003eprovided critical feedback and contributed to manuscript writing. \u003cstrong\u003eNBL and LIC\u0026nbsp;\u003c/strong\u003econceptualized the study, coordinated the SAD validation process and data analysis, and co-wrote the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available at the following repository link: https://repositorio.inta.gob.ar/xmlui/handle/20.500.12123/22231\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank INTA and Universidad Nacional de Catamarca for providing resources for compiling this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eUniversidad Nacional de Catamarca \u0026ndash; Departamento de Sanidad Vegetal\u003c/li\u003e\n \u003cli\u003eINTA - PD-L01-I074: Bases ecol\u0026oacute;gicas y epidemiol\u0026oacute;gicas para el dise\u0026ntilde;o de estrategias de manejo de plagas agr\u0026iacute;colas y forestales.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAchilonu CC, Marais GJ, Ghosh S, Gryzenhout M (2023) Multigene Phylogeny and Pathogenicity Trials Revealed \u003cem\u003eAlternaria alternata\u003c/em\u003e as the Causal Agent of Black Spot Disease and Seedling Wilt of Pecan (\u003cem\u003eCarya illinoinensis\u003c/em\u003e) in South Africa. 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HortScience 40 (5): 1175\u0026ndash;1189. https://doi.org/10.21273/hortsci.40.5.1175\u003c/li\u003e\n\u003cli\u003eStevenson M, Nunes T, Heuer C, Marshall J, Sanchez J, Thornton R, Reiczigel J, Robinson-Cox J, Sebastiani P, Solymos P, Yoshida K, Jones G, Pirikahu, Firestone S, Kyle R, Popp J, Jay M, Reynard C (2019) epiR: tools for the analysis of epidemiological data. R package version 1.0\u0026ndash;4. https://CRAN.R-project.org/package=epiR \u003c/li\u003e\n\u003cli\u003eYan L, Yang X, Wang Z, Zhu H, Qian Y, \u0026amp; Wu W (2022) First report of \u003cem\u003eAlternaria tenuissima\u003c/em\u003e causing leaf black spot on pecan in China. Plant Disease, 106 (6): 1748.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tppa","sideBox":"Learn more about [Tropical Plant Pathology](https://www.springer.com/journal/40858)","snPcode":"40858","submissionUrl":"https://www.editorialmanager.com/tppa","title":"Tropical Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carya Illinoinensis, foliar disease, phytopathometry, epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-6655217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6655217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePecan (\u003cem\u003eCarya illinoinensis\u003c/em\u003e) cultivation is expanding in Argentina, with Catamarca Province emerging as a significant production region. However, fungal diseases such as Alternaria black spot (ABS), caused by \u003cem\u003eAlternaria\u003c/em\u003e spp., pose an increasing threat to crop yield and health. Considering that disease quantification is crucial for epidemiological studies and management, this study aimed to design and validate a standard area diagram (SAD) set to improve the visual estimation of ABS severity on pecan leaves. Using 255 diseased leaves, an eight-image SAD set with severity levels that linearly ranged from 2.2\u0026ndash;88.9% was designed. Thirty-four raters participated in the validation process using the online platform \u003cem\u003eTraineR2\u003c/em\u003e in two phases: unaided and aided assessments. The use of the SAD set significantly improved accuracy metrics. Lin\u0026rsquo;s concordance correlation coefficient (CCC) increased from 0.93 to 0.97, while precision (r) rose from 0.92 to 0.97. Additionally, inter-rater reliability, measured using the intraclass correlation coefficient (ICC), improved from 0.86 to 0.93. This study demonstrates the effectiveness of the SAD set tool in enhancing the accuracy and consistency of ABS severity estimations, highlighting its potential as a practical resource for pecan producers and researchers.\u003c/p\u003e","manuscriptTitle":"Development and validation of a standard area diagram (SAD) set for assessing Alternaria black spot severity in pecan leaves","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 17:42:45","doi":"10.21203/rs.3.rs-6655217/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2025-06-28T09:45:56+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-03T11:55:02+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-20T12:20:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-20T09:43:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Plant Pathology","date":"2025-05-13T10:20:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tppa","sideBox":"Learn more about [Tropical Plant Pathology](https://www.springer.com/journal/40858)","snPcode":"40858","submissionUrl":"https://www.editorialmanager.com/tppa","title":"Tropical Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ee1e9080-e44e-4267-bda5-d0dd9bb78847","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-09T12:30:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-27 17:42:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6655217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6655217","identity":"rs-6655217","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}