Development and statistical validation of a diagrammatic scale for assessing rhizome necrosis severity in banana caused by Radopholus similis

Development and statistical validation of a diagrammatic scale for assessing rhizome necrosis severity in banana caused by Radopholus similis | 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 statistical validation of a diagrammatic scale for assessing rhizome necrosis severity in banana caused by Radopholus similis Amanda Bahiano Passos Sousa, Anelita de Jesus Rocha, Bruno Santos Louzado das Neves, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8920541/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate quantification of rhizome necrosis caused by Radopholus similis is essential for resistance studies, phenotyping, epidemiological analyses, and nematode management in banana. However, visual assessment of rhizome necrosis remains poorly standardized and highly subjective. This study aimed to develop and statistically validate a diagrammatic scale to quantify the severity of rhizome necrosis in banana plants infected by R. similis. One hundred rhizomes exhibiting a wide range of necrosis levels were selected from greenhouse-grown, inoculated plants. Rhizomes were photographed, digitally processed, and the necrotic area was quantified using ImageJ software. Based on these measurements, a diagrammatic scale with eight severity levels (0, 5.4, 9.6, 20.0, 30.5, 40.1, 56.7, and 83.4%) was constructed. Scale validation was performed by ten inexperienced evaluators who estimated disease severity in two independent assessments conducted with and without the aid of the proposed scale. Accuracy, precision, and agreement were evaluated using linear regression analyses, coefficients of determination (R²), and Lin’s concordance correlation coefficient (CCC). Use of the diagrammatic scale significantly improved evaluator performance, resulting in higher R² and CCC values and a substantial reduction in systematic bias. The results demonstrate that the proposed diagrammatic scale is a robust, accurate, and reproducible tool for standardizing the assessment of banana rhizome necrosis caused by R. similis , with direct applicability to resistance screening and banana breeding programs. Accuracy Banana Disease severity Diagrammatic scale Plant-parasitic nematode Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Banana is an important staple food crop and a major source of income for smallholder farmers in approximately 150 tropical and subtropical countries (Tripathi et al. 2022 ). In 2023, global banana exports reached approximately 19.1 million tonnes (FAO 2024). Despite its socioeconomic relevance, banana production is affected by a wide range of pests and diseases (Santos et al. 2013), among which the burrowing nematode Radopholus similis is one of the most destructive. This nematode is widely distributed in banana-growing regions worldwide and causes substantial yield losses (Gowen and Quénéhervé 1990 ; Sarah 1990 ; Chabrier and Quénéhervé 2003 ). High infestations of R. similis cause cracking along the roots, after which the nematodes migrate to the rhizome, leading to tissue decay and the formation of large, dark cavities (Agrios 2005 ). This process compromises water and nutrient uptake, resulting in reduced sucker growth, lower bunch weight, and plant toppling (Gowen and Quénéhervé 1990 ; Sarah 1999 ; Blomme et al. 2004 ; Lafont et al. 2007 ). Given the importance of banana as a staple food and income source, together with the significant impact of nematodes on productivity, greater efforts are required to develop Musa genotypes resistant or tolerant to these pathogens (Viaene et al. 2003 ). Resistant cultivars represent one of the most sustainable management strategies and are equally accessible to subsistence farmers and commercial producers (Wuyts et al. 2007 ). Several approaches are used to characterize plant resistance, tolerance, or susceptibility to nematodes. In banana, the most common methods include nematode population density and reproduction factor analyses, morphological and molecular characterization, enzymatic activity, gene expression analyses, agronomic traits, and symptom evaluation (Sousa et al. 2024 ). Symptoms caused by R. similis can be assessed using lesion indices, the number of functional roots, and/or rhizome necrosis indices (Sousa et al. 2024 ). Disease severity is usually estimated visually as the percentage of damaged tissue relative to the total affected area (Duarte et al. 2013 ; Ruiz et al. 2021 ). The use of scales to support disease severity assessment is more common for foliar diseases (Goulart 2018 ). Despite the importance of R. similis in banana production, no quantitatively validated diagrammatic scale is currently available for assessing rhizome necrosis severity in banana, limiting the comparability of results across studies. According to Sousa et al. ( 2024 ), two scales have been described for evaluating rhizome necrosis caused by phytoparasitic nematodes in banana; however, both present limitations, including the absence of visual support. The scale proposed by Pinochet ( 1988 ), based on lesion size and number on the rhizome surface, can be impractical for large sample sizes. The scale proposed by Bridge ( 1988 ), adapted from root necrosis assessments, may also be limited because its intervals were not derived from actual rhizome measurements. To improve disease quantification accuracy, several strategies have been proposed, among which diagrammatic scales stand out. These scales consist of illustrated representations of plant organs showing different disease severity levels and should be easy and fast to use under diverse conditions, producing accurate, precise, and reproducible results (Berger 1980 ; Bergamin Filho and Amorim 1996 ; Martins et al. 2004 ). When developing diagrammatic scales, it is essential to consider upper and lower limits corresponding to the maximum and minimum disease severity observed (Horsfall and Barratt 1945 ; James 1974 ; Bergamin Filho and Amorim 1996 ; Godoy et al. 2006 ). Furthermore, diagrammatic scales must be validated before being proposed as standard assessment tools and should be corrected if they produce unsatisfactory results (Martins et al. 2004 ; Godoy et al. 2006 ). Given the growing need for studies on R. similis control in banana and plantain, together with the lack of standardized methods for disease quantification, the objective of this study was to develop and statistically validate a diagrammatic scale to classify plant responses to R. similis infection based on the presence and percentage of rhizome necrosis in banana seedlings grown under greenhouse conditions. Material and Methods Development of the Diagrammatic Scale The diagrammatic scale was developed using 100 rhizomes obtained from a greenhouse experiment involving 340 banana plants, including genotypes from the Prata (AAB) and Cavendish (AAA) subgroups, as well as somaclones of the cultivar Grande Naine. Sample selection was structured to ensure complete coverage of the observed severity spectrum, encompassing the full range of rhizome necrosis recorded in the original population, thereby ensuring adequate phenotypic representativeness for scale development. Plants were inoculated approximately 40 days after planting with R. similis by applying a suspension containing 1.000 nematodes per plant, following the methodology described by Rocha et al. ( 2020 ). Samples were collected 90 days after inoculation (dai). Rhizomes were immediately separated from roots and shoots, washed, and longitudinally sectioned with a knife. All rhizomes were then photographed. Selected images were processed using ImageJ software to determine total and necrotic areas. Based on these measurements, rhizomes exhibiting the lowest and highest necrosis levels were identified, establishing the lower and upper limits of the scale. Six intermediate severity levels were then defined to represent intervals observed in the original sample set. These levels were selected to reflect lesion severity, tissue integrity, and the typical distribution and shape of lesions for each severity class, following the methodology proposed by Franceschi et al. ( 2020 ). Once disease severity percentages were established, a standard rhizome of known area was illustrated eight times, starting from the minimum severity level and following a logistic increment of disease severity. This process reproduced symptoms observed in rhizomes at 90 dai under greenhouse conditions, resulting in the final diagrammatic scale. Scale Validation Scale validation was performed using images of the same 100 rhizomes representing all severity levels. Images were randomly arranged on individual slides and displayed using Microsoft® PowerPoint® 2010 (Microsoft Corporation, Redmond, WA, USA). Ten evaluators with no prior experience in disease severity assessment were selected to test the robustness and applicability of the scale under non-specialized conditions, simulating real-use scenarios. In the first assessment, evaluators estimated rhizome necrosis severity without the aid of any scale. Seven days later, the same evaluators assessed the same images using the proposed diagrammatic scale. The number of evaluators (n = 10) followed established methodologies for diagrammatic scale development, as previous studies have successfully employed similar sample sizes to ensure reliability and repeatability of visual estimates (Braga et al. 2020 ; Borges Junior et al. 2020 ; Figueiredo et al. 2021; Pietrobon et al. 2022 ). Statistical Analyses The precision and accuracy of each evaluator’s visual estimates were assessed using linear regression analysis, considering actual disease severity as the independent variable and estimated severity as the dependent variable. Precision was evaluated using the coefficient of determination (R²) and the variance of absolute errors (estimated minus actual severity). Accuracy was assessed using t-tests applied to the intercept (β₀) and slope (β₁) of the regression equations. Scale reproducibility was evaluated using coefficients of determination from linear regressions between severity estimates of evaluator pairs, as proposed by Nutter Jr. and Schultz ( 1995 ) and Nutter Jr. et al. ( 1993 ). In addition to linear regression, Lin’s concordance correlation coefficient (CCC) was used to evaluate agreement between estimated and actual severity values (Lin 1989 ). Five parameters were assessed: Lin’s concordance coefficient, scale shift coefficient, location shift coefficient, bias correction factor, and Pearson’s correlation coefficient. Confidence intervals (95%) were calculated to test differences (P < 0.05) between assessments performed with and without the diagrammatic scale (Figueiredo et al. 2022 ). All statistical analyses were performed using R software (R Core Development Team 2016) with the packages ggplot2, dplyr, broom, and ggpubr. The epi.ccc function from the epiR package was used to calculate CCC values. Results Among the evaluated rhizomes, 97% exhibited severity levels between 0 and 59.99%, whereas only 3% showed necrosis levels above 60% (Fig. 1 ). This distribution is associated with host defense mechanisms and the presence of plants with different susceptibility levels within the evaluated population. These results are consistent with subsequent analyses related to the reproduction factor (RF) of R. similis (Sasser et al. 1987 ). Furthermore, even in highly susceptible plants, nematode populations were predominantly concentrated in secondary and superficial roots. Consequently, different degrees of rhizome necrosis were observed within the same group of plants. Therefore, the absence of severe rhizome symptoms does not necessarily indicate resistance but rather reflects the dynamics of the plant-nematode interaction at the time of evaluation. For this reason, the present study did not aim to characterize the resistance status of individual rhizomes; instead, it focused on representing distinct levels of rhizome necrosis to develop a reliable diagrammatic scale. Based on the analysis of the 100 selected rhizomes, the diagrammatic scale developed in this study comprised eight severity levels, with lower and upper limits of 0% (no symptoms) and 83.4% necrotic area, respectively (Fig. 2 ). Severity values above 83.4% were not included in the scale, as they were not observed under greenhouse conditions. To classify plant responses to R. similis , severity intervals and corresponding grades were assigned as follows: no symptoms (0); 60% necrosis (grade 4) (Table 1 ). Grade 0 corresponded to rhizomes without visible necrosis; grade 1 was characterized by small, dark, diffuse necrotic lesions emerging from the rhizome margin; grade 2 consisted of moderate, diffuse dark lesions appearing at multiple marginal points; grade 3 was defined by large, dark necrotic lesions covering most of the rhizome margin and extending toward the central region; and grade 4 was characterized by extensive dark necrosis affecting more than 60% of the rhizome, encompassing the entire marginal region and part of the central tissue. Table 1 Classification scale used to evaluate banana plant responses to Radopholus similis based on rhizome necrosis severity. Rhizome Lesion Index (%) Grade No symptoms 0 60% 4 To ensure the applicability of the proposed scale, ten inexperienced evaluators assessed images of the selected rhizomes in two independent sessions, performed with and without the diagrammatic scale and separated by a seven-day interval. Joint analysis of the regression coefficients revealed substantially higher accuracy and precision when the diagrammatic scale was used compared with assessments performed without the scale (Table 2 ; Fig. 3 ). Without the scale, several evaluators exhibited intercepts significantly different from zero (evaluators 1, 2, 5, 6, and 10), indicating systematic bias. In addition, slopes significantly different from one were observed for evaluators 1, 5, 6, and 7, indicating distortion in severity estimates as disease severity increased. Although R² values without the scale ranged from moderate to high (approximately 0.57–0.78), considerable variability and estimation errors were evident. In contrast, when the diagrammatic scale was used, intercepts for nearly all evaluators did not differ significantly from zero, and slopes did not differ from one, indicating elimination of systematic bias. Coefficients of determination increased substantially for most evaluators; for example, R² values increased from 0.7271 to 0.9361 for evaluator 1, from 0.7559 to 0.9173 for evaluator 2, and from 0.7510 to 0.8633 for evaluator 10. Even evaluators who showed poor performance without the scale (e.g., evaluator 4, R² = 0.57) exhibited marked improvement when using the scale (R² = 0.8180). These results demonstrate that the diagrammatic scale enhanced evaluator accuracy and consistency while effectively eliminating statistically significant bias. Table 2 Intercept (β₀), slope (β₁), and coefficient of determination (R²) of linear regression equations relating visual severity estimates performed by evaluators without and with the use of the diagrammatic scale. Evaluator Without scale With scale β0 β1 R 2 β0 β1 R 2 1 -10.0890** 1.3354** 0.7271 -1.2634 ns 1.0472 ns 0.9361 2 -6.7782** 1.1251 ns 0.7559 -1.7506 ns 0.9499 ns 0.9173 3 3.2118 ns 1.0218 ns 0.7525 2.4000* 1.0029 ns 0.8813 4 5.3566 ns 1.0030 ns 0.5706 2.3543 ns 0.9416 ns 0.8180 5 10.0204** 0.8030** 0.7419 5.7299** 0.9519 ns 0.7298 6 -3.4895** 0.7425** 0.7789 0.8796 ns 0.8600** 0.8603 7 -0.9204 ns 1.3563** 0.7501 -2.5421 ns 1.0953 ns 0.8261 8 2.5973 ns 0.9520 ns 0.7769 0.1563 ns 0.9428 ns 0.8719 9 -6.7133 ns 1.2156 ns 0.5236 -5.6085* 1.1260 ns 0.6217 10 4.4664* 1.0843 ns 0.7510 -0.3105 ns 1.0274 ns 0.8633 ** and * significant at the 1% and 5% levels, respectively, according to the t-test. ns, not significant (β₀ = 0 and β₁ = 1). Residual analysis further confirmed the improvement in estimation quality provided by the scale (Fig. 4 ). Without the scale, residuals were widely dispersed and frequently distant from the zero line, reflecting low precision. Some evaluators showed a systematic tendency to overestimate (positive residuals) or underestimate (negative residuals) disease severity, particularly at intermediate and high severity levels. In contrast, when the scale was used, residuals were more tightly clustered around zero, with reduced dispersion, indicating greater consistency and a marked reduction in estimation error. Analysis of inter-evaluator agreement using coefficients of determination (R²) revealed substantial heterogeneity when assessments were performed without the scale (Table 3 ). While some evaluator pairs exhibited relatively high agreement (e.g., evaluators 1 × 2, R² = 0.88; evaluators 6 × 7, R² = 0.87), others showed weak agreement (e.g., evaluators 4 × 10, R² = 0.60), indicating the absence of a common assessment standard. When the diagrammatic scale was applied, R² values generally increased and became more consistent, typically ranging from 0.70 to 0.92. Notably, evaluator pairs with previously low agreement showed marked improvement (e.g., evaluators 4 × 10, R² increased from 0.60 to 0.82), demonstrating that the scale harmonized severity estimates among evaluators. Table 3 Coefficients of determination (R²) from linear regression analyses between evaluator pairs for assessments performed without and with the diagrammatic scale. Evaluators 2 3 4 5 6 7 8 9 10 Without scale 1 0,88 0,77 0,64 0,75 0,76 0,87 0,83 0,74 0,75 2 0,85 0,68 0,79 0,85 0,76 0,87 0,77 0,77 3 0,70 0,78 0,85 0,74 0,87 0,75 0,72 4 0,71 0,63 0,62 0,65 0,67 0,60 5 0,78 0,71 0,82 0,71 0,79 6 0,70 0,87 0,69 0,71 7 0,78 0,61 0,73 8 0,70 0,74 9 0,65 With scale 1 0,92 0,91 0,81 0,78 0,87 0,85 0,88 0,67 0,88 2 0,85 0,78 0,73 0,84 0,81 0,84 0,63 0,85 3 0,82 0,79 0,84 0,84 0,80 0,73 0,88 4 0,69 0,81 0,79 0,79 0,58 0,82 5 0,68 0,80 0,74 0,62 0,79 6 0,73 0,79 0,61 0,86 7 0,83 0,60 0,83 8 0,55 0,85 9 0,65 These patterns were clearly illustrated in the heatmap representation of inter-evaluator agreement (Fig. 5 ). Without the scale, the heatmap displayed pronounced heterogeneity, with wide variation in agreement levels across evaluator pairs. With the use of the diagrammatic scale, the heatmap became more homogeneous and darker, reflecting consistently higher R² values. Several evaluator pairs achieved agreement values ≥ 0.80, including pairs that previously exhibited poor concordance. Lin’s concordance correlation coefficient (CCC) analysis further supported the effectiveness of the proposed scale (Table 4 ). Without the scale, the CCC indicated moderate agreement, with a scale shift coefficient of 0.7489 and a negative location shift (–0.0731), reflecting systematic underestimation. When the scale was used, the CCC increased substantially, the scale shift coefficient rose to 0.8984, and the location shift approached zero (0.0078), indicating near elimination of systematic bias. The bias correction factor (Cb) approached unity, and Pearson’s correlation coefficient increased, confirming improvements in both accuracy and precision. Table 4 Comparison of visual severity estimates performed by ten evaluators without and with the use of the diagrammatic scale based on Lin’s concordance statistics. Statistics Without scale 95% confidence interval With scale 95% confidence interval Coeficiente de concordância a 0.7626 [0.730; 0.788] 0.8883 [0.869; 0.905] Desvio de escala b 0.7489 [0.717; 0.782] 0.8984 [0.870; 0.927] Desvio de localização c -0.0731 [-0.116; -0.028] 0.0078 [-0.021; 0.036] Fator de correção de viés d 0.9571 [0.943; 0.969] 0.9943 [0.990; 0.997] Coeficiente de correlação e 0.7967 [0.769; 0.821] 0.8935 [0.875; 0.910] a Lin’s concordance correlation coefficient: combines measures of precision and accuracy to assess the degree of agreement with the actual values. b Scale shift coefficient: coefficient relative to perfect agreement (1 = perfect agreement between x and y). c Location shift coefficient: coefficient relative to perfect agreement (0 = perfect agreement between x and y). d Bias correction factor: measures the extent to which the fitted regression line deviates from the 45° line. No bias occurs when Cb = 1. It is a measure of accuracy calculated from the scale and location shift coefficients. e Pearson’s correlation coefficient: measures precision (the strength of the linear relationship). Graphical representation of Lin’s method further illustrated these improvements (Fig. 6 ). Without the scale, estimated severity values were widely dispersed around the identity line, and Lin’s regression line deviated substantially from the 45° line, indicating bias and low agreement. In contrast, when the scale was used, estimates clustered closely around the identity line, and Lin’s regression line closely overlapped the line of perfect concordance, demonstrating high accuracy and precision. Finally, residual plots derived from Lin’s method confirmed these findings (Fig. 7 ). Without the scale, residuals were large and inconsistent, indicating low agreement between estimated and actual severity values. With the diagrammatic scale, residuals were more homogeneous and closer to zero, confirming that the scale effectively standardized disease severity assessments and improved their reliability. Discussion The diagrammatic scale proposed in this study did not include severity values above 83.4%, as such levels were not observed under greenhouse conditions. This outcome is directly related to host behavior at the evaluation time. As nematode populations increase, secondary symptoms associated with root necrosis emerge (Agrios 2005 ; Gebremichael 2015 ), facilitating nematode migration toward the rhizome and leading to tissue decay and the formation of extensive dark cavities (Agrios 2005 ). A genotype is considered susceptible when it allows greater nematode reproduction and exhibits more extensive lesions in roots or rhizomes following infection (Stoffelen et al. 2000 ). In this study, we developed and validated the first diagrammatic scale specifically designed to assess R. similis severity in banana rhizomes. Lesion areas were objectively quantified using digital image analysis in 100 rhizomes, enabling the construction of a diagrammatic scale based on real severity values. This approach is fundamental for disease assessment, as it reduces the subjectivity inherent to visual estimations (Martins et al. 2004 ; Lopes et al. 2022 ). Consequently, evaluators produced estimates closer to actual values, achieving reliable results even under varying conditions (Nutter et al. 1991 ; Nutter and Schultz 1995 ; Lopes et al. 2022 ). Visual estimators are naturally prone to overestimating disease severity in the absence of diagrammatic references, often relying on lesion size and number as primary cues (Capucho et al. 2011 ; Lopes et al. 2022 ). In this context, evaluator training has been shown to be an effective strategy for reducing bias and improving accuracy (Teramoto et al. 2011 ; Sousa et al. 2014 ). These findings reinforce the concept that assessment quality depends not only on the evaluation tool but also on individual factors such as training, experience, and visual perception (Amorim and Bergamin Filho 2018 ; Figueiredo et al. 2022 ). Indeed, targeted training can significantly enhance estimation quality (Michereff et al. 2000 ). Conversely, lesion morphology complexity, psychological responses to visual stimuli, fatigue, and emotional state may negatively affect assessment precision (Kranz 1988 ; Sherwood et al. 1983 ; Nutter Jr. et al. 2006 ; Sachs et al. 2011 ; Sousa et al. 2014 ). Inaccurate visual estimates may compromise experimental conclusions (Parker et al. 1995 ). Therefore, the establishment of a standardized system for quantifying banana rhizome necrosis represents a valuable contribution to studies on R. similis severity. In the present study, disease severity assessments performed with the proposed scale showed high precision among evaluators. Estimated severity values closely matched actual measurements, confirming the scale’s accuracy. In this context, accuracy is defined as the proximity between observed and estimated values and can be evaluated through regression intercept (β₀) and slope (β₁). Deviations from zero (β₀) and one (β₁) indicate constant or systematic errors in estimation (Belasque Jr. et al. 2005 ; Sussel et al. 2009 ; Lenz et al. 2010 ; Sousa et al. 2014 ). Use of the proposed scale resulted in greater homogeneity and closer agreement with actual values, confirming its effectiveness in standardizing and validating visual severity assessments. Accordingly, the results demonstrate that the diagrammatic scale significantly reduced overestimation errors, particularly among inexperienced evaluators (Sousa et al. 2014 ). For this reason, similar studies proposing diagrammatic scales have been increasingly reported for different crops and pathosystems. For example, the diagrammatic scale developed by de Andrade et al. ( 2024 ) represents a significant advance in assessing soybean seed coating quality by standardizing a process traditionally subject to evaluator subjectivity. That scale was constructed using artificial intelligence tools to objectively quantify coating coverage, enabling reliable visual classification into quality categories. Likewise, Figueiredo et al. ( 2022 ) established a diagrammatic scale for whole leaves of Coffea arabica with seven severity levels, which demonstrated greater precision, accuracy, and reproducibility after validation with ten evaluators, making it suitable for both field and laboratory assessments. Rojas-Chacón et al. ( 2024 ) adapted this approach to leaf discs of mutagenized plants, developing a specific scale (0–52.15%) capable of discriminating genotypes with different resistance levels and providing practical benefits for breeding programs. Similarly, Todd et al. ( 2025 ) proposed a standardized diagrammatic scale for assessing Fusarium yellows severity in sugar beet, which outperformed traditional ordinal scales and showed high precision and accuracy (Cb = 0.99) when tested by inexperienced evaluators. Unlike ordinal classification systems, the diagrammatic scale developed in the present study allows precise determination of the percentage of affected area using real reference values (Stonehouse 1994 ; Godoy et al. 1997 ; Leite and Amorim 2002; Belasque et al. 2005 ; Michereff et al. 2006 ; Spolti et al. 2011 ; Yadav et al. 2013 ). This feature enhances symptom quantification and makes the proposed scale particularly relevant for banana breeding programs worldwide. Moreover, the results confirm its applicability in future research conducted under greenhouse conditions. Finally, although lesion classification scales are an important approach for symptom evaluation in phytoparasitic nematode studies, they should not be used in isolation. Complementary parameters, such as nematode population density, percentage of dead roots, and root necrosis indices, must also be considered. Integration of these indicators provides a more reliable assessment of genotype resistance or susceptibility (Sousa et al. 2024 ). Conclusion This study presents the first quantitatively defined and statistically validated diagrammatic scale for assessing rhizome necrosis severity in banana caused by Radopholus similis. The developed scale demonstrated high accuracy, precision, and reproducibility, effectively reducing systematic bias and variability associated with visual assessments, even when applied by inexperienced evaluators. The use of real severity values obtained through digital image analysis enabled the construction of a robust and representative tool, fully aligned with methodological principles recommended for diagrammatic scale development. Statistical validation confirmed its applicability as a standardized method for quantifying banana rhizome necrosis under greenhouse conditions. The proposed scale represents a practical and reliable tool for resistance studies, phenotyping, and banana breeding programs, contributing to the standardization of disease assessments and improving comparability among experiments. When used in combination with other phytopathological and nematological parameters, this scale can strengthen the characterization of Musa genotype responses to R. similis infection. Declarations Conflict of Interest The authors declare no conflict of interest. Funding This study was financially supported by the Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES). Author Contributions Conceptualization: ABPS; methodology: ABPS, AdJR, BSLdN, LSL, KVMC, LSR, and EPA; original draft preparation: ABPS; data interpretation: ABPS and CAdSL; funding acquisition, supervision, manuscript review and editing: EPA. Acknowledgements The authors thank the Graduate Program in Biotechnology (PPGBiotec) at the State University of Feira de Santana for institutional support. The authors also acknowledge the National Council for Scientific and Technological Development (CNPq) for the research fellowship granted to researcher Amorim EP, and the Coordination for the Improvement of Higher Education Personnel (CAPES) for financial support. The authors express their special appreciation to the reviewers who contributed to this study. References Agrios, G. N. (2005). Plant diseases caused by fungi. In G. N. Agrios (Ed.), Plant Pathology (pp. 386–614). Academic. Amorim, L., & Bergamin Filho, A. (2018). Fenologia, Pantometria e Quantificação de danos. In Amorim L. Rezende JAM, Bergamin Filho A. (Eds.), Manual de fitopatologia: princípios e conceitos vol.1 (5th ed., pp. 499–518). Agronômica Ceres . Barnhart, H. X., Haber, M. J., & Lin, L. I. (2007). 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S., Pozza, E. A., Porto, A. C. M., da Silva, C. M., Miguel, L. A., & Pereira, W. A. (2022). Development and validation of a diagrammatic scale for white mold incidence in tobacco leaf discs. Australasian Plant Pathol , 51 (1), 31–38. Martins, M. C., Guerzoni, R. A., Câmara, G. M. S., Mattiazzi, P., Lourenço, S. A., & Amorim, L. (2004). Escala diagramática para a quantificação do complexo de doenças foliares de final de ciclo em soja. Fitopatol Bras , 29 , 179–184. Michereff, S. J., Maffia, L. A., & Noronha, M. A. (2000). Escala diagramática para avaliação da severidade da queima das folhas do inhame. Fitopatol Bras , 25 (4), 612–619. Michereff, S. J., Noronha, M. A., de Andrade, D. E. G. T., & de Oliveira, E. P. (2006). Development and validation of a diagrammatic key for Cercospora leaf spot of sweet pepper. Summa Phytopathol , 32 , 260–266. Xavier Filha MS, Moreira PAA. Nita, M., Ellis, M. A., & Madden, L. V. (2003). Reliability and accuracy of visual estimation of Phomopsis leaf blight of strawberry. Phytopathol , 93 (8), 995–1005. https://doi.org/10.1094/PHYTO.2003.93.8.995 Nutter, F. W., Teng, P. S., & Shokes, F. M. (1991). Disease assessment term and concepts. Plant Disease , 75 , 1187–1188. NutterJr, F. W., Esker, P. D., & Coelho Netto, R. A. (2006). Disease assessment concepts and the advancements made in improving the accuracy and precision of plant disease data. European Journal Of Plant Pathology , 115 , 95–103. NutterJr, F. W., Gleason, M. L., Jenco, J. H., & Christians, N. C. (1993). Assessing the accuracy, intra-rater repeatability, and inter-rater reliability of disease assessment systems. Phytopathol , 83 , 806–812. NutterJr, F. W., & Schultz, P. M. (1995). Improving the accuracy and precision of disease assessments: selection of methods and use of computer-aided training programs. Canadian Journal Of Plant Pathology , 17 , 174–184. Parker, S. R., Shaw, M. W., & Royle, D. J. (1995). The reliability of visual estimates of disease severity on cereal leaves. Plant Pathology , 44 , 856–864. Pietrobon, A. J., Duarte Júnior, J. B., & Kuhn, O. J. (2022). Elaboração e validação de escala diagramática para avaliação da severidade de estria bacteriana ( Xanthomonas vasicola pv. vasculorum ) em folhas de milho. Revista de Ciências Agro-Ambientais , 20 (2), 66–72. https://doi.org/10.30681/rcaa.v20i2.5858 Pinochet, J. (1988). A method of screening bananas and plantains to lesion forming nematodes. In: Nematodes and borer weevil in bananas: present status of research and outlook. Montpellier, INIBAP. pp. 62–65. Rocha, A. J., Ferreira, M. D. S., Rocha, L. D. S., Oliveira, S. A. S., Amorim, E. P., Mizubuti, E. S. G., & Haddad, F. (2020). Interaction between Fusarium oxysporum f. sp. cubense and Radopholus similis can lead to changes in the resistance of banana cultivars to Fusarium wilt. European Journal Of Plant Pathology , 158 , 403–417. Rojas-Chacón, J. A., Echeverría-Beirute, F., Till, B. J., & Gatica-Arias, A. (2024). Assessment of Hemileia vastatrix resistance in chemically mutagenized Coffea arabica L. leaf discs and the emergence of a novel resistance scale. Journal of Plant Pathol , 106 (3), 1093–1106. Ruiz, A. M. M., Pieri, C., Pieroni, L. P., Porcena, A. S., Albuquerque, F. S., & Furtad, E. L. (2021). Escala diagramática para avaliação da severidade de oídio em eucalipto. Ci Fl , 31 (3), 1535–1546. Sachs, P. D., Neves, C. J., Canteri, M. G., & Sachs, L. G. (2011). Diagrammatic scale for assesment of the phaeosphaeria leaf spot severity in maize. Summa Phytopathol , 37 (4), 202–204. Santos, J. R. P., Faleiro, F. G., Costa, D. C., Amorim, E. P., Silva, S. O., & Cares, J. E. (2023). Banana horizontal and vertical resistance to the burrowing nematode depends on the level of aggressiveness or virulence of the nematode population. Rev Bras Frutic , 45 , e–070. Sarah, J. L. (1990). Les nématodes et le parasitisme des racines de bananiers. Fruits (Spécial Bananes) , 45 , 60–67. Sarah, J. L. (1999). Nematode pathogens: burrowing nematode. In D. R. Jones (Ed.), Diseases of Banana, Abacá and Enset (pp. 295–303). CAB International. Sasser, J. N., Hartman, K. M., & Carter, C. C. (1987). Summary of preliminary crop germplasm evaluations for resistance to root-knot nematodes. Department of Plant Pathology at North Carolina State University . United States Agency for International Development. Sherwood, R. T., BergCC, Hoover, M. R., & Zeiders, K. E. (1983). Illusions in visual assessment of Stagonospora leaf spot of orchard grass. Phytopathol , 73 , 173–177. Sousa, A. B. P., Rocha, A. J., Oliveira, W. D. S., Rocha, L. S., & Amorim, E. P. (2024). Phytoparasitic Nematodes of Musa spp. with Emphasis on Sources of Genetic Resistance: A Systematic Review. Plants , 13 , 1299. Sousa, S. C. R., Santos GRd, Rodrigues, A. C., Bonifácio, A., Dalcin, M. S., & Juliatti, F. C. (2014). Diagrammatic scale for evaluation of gummy stem blight severity in watermelon. Biosci J , 30 (5), 1314–1324. Spolti, P., Schneider, L., Sanhueza, R. M. V., Batzer, J. C., Gleason, M. L., & Medeiros Del Ponte, E. (2011). Improving sooty blotch and flyspeck severity estimation on apple fruit with the aid of standard area diagrams. European Journal Of Plant Pathology , 129 , 21–29. Stoffelen, K., Verlinden, R., Pinochet, J., Swennen, R. L., & De Waele, D. (2000). Host plant response of Fusarium wilt resistant Musa genotypes to Radopholus similis and Pratylenchus coffeae . Int J Pest Manag , 46 , 289–293. Stonehouse, J. (1994). Assessment of Andean bean diseases using visual keys. Plant Pathology , 43 , 519–527. Sussel, A. A. B., Pozza, E. A., & Castro, H. A. (2009). Elaboration and validation of diagrammatic scale to evaluate gray mold severity in castor bean. Trop Plant Pathol , 34 (3), 186–191. Teramoto, A., Aguiar, R. A., Garcia, R. A., Martins, M. C., & Cunha, M. G. (2011). Diagrammatic scale to evaluate target spot severity in cucumber plant leaves. Pesq Agropec Trop , 41 (3), 439–445. Todd, O. E., Hanson, L. E., & Dorn, K. M. (2025). A standard area diagram for Fusarium yellows rating in sugar beet (Beta vulgaris). Plant Pathology , 74 (2), 422–430. Tripathi, L., Ntui, V. O., & Tripathi, J. N. (2022). Control of Bacterial Diseases of Banana Using CRISPR/Cas-Based Gene Editing. International Journal Of Molecular Sciences , 23 , 3619. Viaene, N., De Waele, D., Durán, L., Rivera, J. M., Dueñas, J., & Rowe, P. (2003). Responses of banana and plantain cultivars, lines and hybrids to the burrowing nematode. Radopholus similis Nematology , 5 (1), 85–98. Wuyts, N., Lognay, G., Verscheure, M., Marlier, M., De Waele, D., & Swennen, R. (2007). Potential physical and chemical barriers to infection by the burrowing nematode Radopholus similis in roots of susceptible and resistant banana ( Musa spp). Plant Pathology , 56 , 878–890. Yadav, N. V. S., de Vos, S. M., Bock, C. H., & Wood, B. W. (2013). Development and validation of standard area diagrams to aid assessment of pecan scab symptoms on fruit. Plant Pathology , 62 , 325–335. Cite Share Download PDF Status: Posted Version 1 posted 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-8920541","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599076269,"identity":"561f72ca-66ef-4604-9218-477a50fc236b","order_by":0,"name":"Amanda Bahiano Passos Sousa","email":"","orcid":"","institution":"UEFS: Universidade Estadual de Feira de Santana","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"Bahiano Passos","lastName":"Sousa","suffix":""},{"id":599076270,"identity":"39d528b7-0e95-4e63-9950-abc82715ab7c","order_by":1,"name":"Anelita de Jesus Rocha","email":"","orcid":"","institution":"EMBRAPA 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Tropical","correspondingAuthor":true,"prefix":"","firstName":"Edson","middleName":"Perito","lastName":"Amorim","suffix":""}],"badges":[],"createdAt":"2026-02-19 20:02:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8920541/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8920541/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103990450,"identity":"cd5e94e7-badf-4416-b61d-93b7f887ecbb","added_by":"auto","created_at":"2026-03-05 11:26:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":373065,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of severity levels based on 100 rhizome images used to develop the standard area diagrammatic scale for assessing banana rhizome necrosis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/648dab5dbf3f8abb020764f9.png"},{"id":103990468,"identity":"64e1b4fd-de0a-41c6-bb40-0ca317f8ce29","added_by":"auto","created_at":"2026-03-05 11:26:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":341268,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrammatic scale of banana rhizome necrosis caused by \u003cem\u003eRadopholus similis\u003c/em\u003e at 90 days after inoculation under greenhouse conditions\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/f96bc24c92d0859e69a83707.png"},{"id":104402027,"identity":"29c324b4-8e30-4849-b607-eb65e59d022d","added_by":"auto","created_at":"2026-03-11 12:14:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":736741,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression models relating actual and visually estimated disease severity values assessed by evaluators without and with the use of the diagrammatic scale\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/767dd5b4b9f9bf4b4f621713.png"},{"id":103990474,"identity":"abb3621b-1c92-4ba4-ac30-213da9940734","added_by":"auto","created_at":"2026-03-05 11:26:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":708192,"visible":true,"origin":"","legend":"\u003cp\u003eResidual distribution (estimated severity − actual severity) for disease assessments performed without and with the use of the diagrammatic scale\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/8b1584a17a12ba94200ba48e.png"},{"id":103990469,"identity":"6974d617-93cb-4c53-b36e-04eb20eae0ef","added_by":"auto","created_at":"2026-03-05 11:26:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181239,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of coefficients of determination (R²) between evaluator pairs for assessments performed without and with the diagrammatic scale\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/d9cb89be27cb44e04c565cf3.png"},{"id":103990454,"identity":"d11bdaf7-8c4d-48f1-a90f-18233de6c452","added_by":"auto","created_at":"2026-03-05 11:26:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":271608,"visible":true,"origin":"","legend":"\u003cp\u003eLin’s concordance analysis between actual and estimated severity values for assessments conducted without and with the diagrammatic scale. Each point represents one of 100 estimates performed by each evaluator\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/fb06fd4500f5875f89a6957c.png"},{"id":103990476,"identity":"e8f8ee6c-9b22-4ba8-8a40-9c642e8e0b5b","added_by":"auto","created_at":"2026-03-05 11:26:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":263337,"visible":true,"origin":"","legend":"\u003cp\u003eResidual plots derived from Lin’s concordance method for assessments performed without and with the diagrammatic scale. Points represent 100 estimates per evaluator\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/4cf86a20d469374447947d8b.png"},{"id":108490731,"identity":"54f09134-2858-4de3-81b9-432810b83310","added_by":"auto","created_at":"2026-05-05 09:47:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3341899,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8920541/v1/f1091321-9ae7-45d5-94a0-6b77a2987f5b.pdf"}],"financialInterests":"","formattedTitle":"Development and statistical validation of a diagrammatic scale for assessing rhizome necrosis severity in banana caused by Radopholus similis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBanana is an important staple food crop and a major source of income for smallholder farmers in approximately 150 tropical and subtropical countries (Tripathi et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In 2023, global banana exports reached approximately 19.1\u0026nbsp;million tonnes (FAO 2024). Despite its socioeconomic relevance, banana production is affected by a wide range of pests and diseases (Santos et al. 2013), among which the burrowing nematode \u003cem\u003eRadopholus similis\u003c/em\u003e is one of the most destructive. This nematode is widely distributed in banana-growing regions worldwide and causes substantial yield losses (Gowen and Qu\u0026eacute;n\u0026eacute;herv\u0026eacute; \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Sarah \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Chabrier and Qu\u0026eacute;n\u0026eacute;herv\u0026eacute; \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh infestations of \u003cem\u003eR. similis\u003c/em\u003e cause cracking along the roots, after which the nematodes migrate to the rhizome, leading to tissue decay and the formation of large, dark cavities (Agrios \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This process compromises water and nutrient uptake, resulting in reduced sucker growth, lower bunch weight, and plant toppling (Gowen and Qu\u0026eacute;n\u0026eacute;herv\u0026eacute; \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Sarah \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Blomme et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lafont et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the importance of banana as a staple food and income source, together with the significant impact of nematodes on productivity, greater efforts are required to develop Musa genotypes resistant or tolerant to these pathogens (Viaene et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Resistant cultivars represent one of the most sustainable management strategies and are equally accessible to subsistence farmers and commercial producers (Wuyts et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral approaches are used to characterize plant resistance, tolerance, or susceptibility to nematodes. In banana, the most common methods include nematode population density and reproduction factor analyses, morphological and molecular characterization, enzymatic activity, gene expression analyses, agronomic traits, and symptom evaluation (Sousa et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Symptoms caused by \u003cem\u003eR. similis\u003c/em\u003e can be assessed using lesion indices, the number of functional roots, and/or rhizome necrosis indices (Sousa et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Disease severity is usually estimated visually as the percentage of damaged tissue relative to the total affected area (Duarte et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ruiz et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe use of scales to support disease severity assessment is more common for foliar diseases (Goulart \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite the importance of \u003cem\u003eR. similis\u003c/em\u003e in banana production, no quantitatively validated diagrammatic scale is currently available for assessing rhizome necrosis severity in banana, limiting the comparability of results across studies.\u003c/p\u003e \u003cp\u003eAccording to Sousa et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), two scales have been described for evaluating rhizome necrosis caused by phytoparasitic nematodes in banana; however, both present limitations, including the absence of visual support. The scale proposed by Pinochet (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), based on lesion size and number on the rhizome surface, can be impractical for large sample sizes. The scale proposed by Bridge (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), adapted from root necrosis assessments, may also be limited because its intervals were not derived from actual rhizome measurements.\u003c/p\u003e \u003cp\u003eTo improve disease quantification accuracy, several strategies have been proposed, among which diagrammatic scales stand out. These scales consist of illustrated representations of plant organs showing different disease severity levels and should be easy and fast to use under diverse conditions, producing accurate, precise, and reproducible results (Berger \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Bergamin Filho and Amorim \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Martins et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen developing diagrammatic scales, it is essential to consider upper and lower limits corresponding to the maximum and minimum disease severity observed (Horsfall and Barratt \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; James \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Bergamin Filho and Amorim \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Godoy et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, diagrammatic scales must be validated before being proposed as standard assessment tools and should be corrected if they produce unsatisfactory results (Martins et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Godoy et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the growing need for studies on \u003cem\u003eR. similis\u003c/em\u003e control in banana and plantain, together with the lack of standardized methods for disease quantification, the objective of this study was to develop and statistically validate a diagrammatic scale to classify plant responses to \u003cem\u003eR. similis\u003c/em\u003e infection based on the presence and percentage of rhizome necrosis in banana seedlings grown under greenhouse conditions.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of the Diagrammatic Scale\u003c/h2\u003e \u003cp\u003eThe diagrammatic scale was developed using 100 rhizomes obtained from a greenhouse experiment involving 340 banana plants, including genotypes from the Prata (AAB) and Cavendish (AAA) subgroups, as well as somaclones of the cultivar Grande Naine. Sample selection was structured to ensure complete coverage of the observed severity spectrum, encompassing the full range of rhizome necrosis recorded in the original population, thereby ensuring adequate phenotypic representativeness for scale development.\u003c/p\u003e \u003cp\u003ePlants were inoculated approximately 40 days after planting with \u003cem\u003eR. similis\u003c/em\u003e by applying a suspension containing 1.000 nematodes per plant, following the methodology described by Rocha et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Samples were collected 90 days after inoculation (dai). Rhizomes were immediately separated from roots and shoots, washed, and longitudinally sectioned with a knife. All rhizomes were then photographed. Selected images were processed using ImageJ software to determine total and necrotic areas.\u003c/p\u003e \u003cp\u003eBased on these measurements, rhizomes exhibiting the lowest and highest necrosis levels were identified, establishing the lower and upper limits of the scale. Six intermediate severity levels were then defined to represent intervals observed in the original sample set. These levels were selected to reflect lesion severity, tissue integrity, and the typical distribution and shape of lesions for each severity class, following the methodology proposed by Franceschi et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce disease severity percentages were established, a standard rhizome of known area was illustrated eight times, starting from the minimum severity level and following a logistic increment of disease severity. This process reproduced symptoms observed in rhizomes at 90 dai under greenhouse conditions, resulting in the final diagrammatic scale.\u003c/p\u003e \u003cp\u003eScale Validation\u003c/p\u003e \u003cp\u003eScale validation was performed using images of the same 100 rhizomes representing all severity levels. Images were randomly arranged on individual slides and displayed using Microsoft\u0026reg; PowerPoint\u0026reg; 2010 (Microsoft Corporation, Redmond, WA, USA).\u003c/p\u003e \u003cp\u003eTen evaluators with no prior experience in disease severity assessment were selected to test the robustness and applicability of the scale under non-specialized conditions, simulating real-use scenarios. In the first assessment, evaluators estimated rhizome necrosis severity without the aid of any scale. Seven days later, the same evaluators assessed the same images using the proposed diagrammatic scale.\u003c/p\u003e \u003cp\u003eThe number of evaluators (n\u0026thinsp;=\u0026thinsp;10) followed established methodologies for diagrammatic scale development, as previous studies have successfully employed similar sample sizes to ensure reliability and repeatability of visual estimates (Braga et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Borges Junior et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Figueiredo et al. 2021; Pietrobon et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical Analyses\u003c/p\u003e \u003cp\u003eThe precision and accuracy of each evaluator\u0026rsquo;s visual estimates were assessed using linear regression analysis, considering actual disease severity as the independent variable and estimated severity as the dependent variable. Precision was evaluated using the coefficient of determination (R\u0026sup2;) and the variance of absolute errors (estimated minus actual severity). Accuracy was assessed using t-tests applied to the intercept (β₀) and slope (β₁) of the regression equations.\u003c/p\u003e \u003cp\u003eScale reproducibility was evaluated using coefficients of determination from linear regressions between severity estimates of evaluator pairs, as proposed by Nutter Jr. and Schultz (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Nutter Jr. et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to linear regression, Lin\u0026rsquo;s concordance correlation coefficient (CCC) was used to evaluate agreement between estimated and actual severity values (Lin \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Five parameters were assessed: Lin\u0026rsquo;s concordance coefficient, scale shift coefficient, location shift coefficient, bias correction factor, and Pearson\u0026rsquo;s correlation coefficient. Confidence intervals (95%) were calculated to test differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between assessments performed with and without the diagrammatic scale (Figueiredo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R software (R Core Development Team 2016) with the packages ggplot2, dplyr, broom, and ggpubr. The epi.ccc function from the epiR package was used to calculate CCC values.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the evaluated rhizomes, 97% exhibited severity levels between 0 and 59.99%, whereas only 3% showed necrosis levels above 60% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This distribution is associated with host defense mechanisms and the presence of plants with different susceptibility levels within the evaluated population. These results are consistent with subsequent analyses related to the reproduction factor (RF) of R. similis (Sasser et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Furthermore, even in highly susceptible plants, nematode populations were predominantly concentrated in secondary and superficial roots. Consequently, different degrees of rhizome necrosis were observed within the same group of plants. Therefore, the absence of severe rhizome symptoms does not necessarily indicate resistance but rather reflects the dynamics of the plant-nematode interaction at the time of evaluation. For this reason, the present study did not aim to characterize the resistance status of individual rhizomes; instead, it focused on representing distinct levels of rhizome necrosis to develop a reliable diagrammatic scale.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the analysis of the 100 selected rhizomes, the diagrammatic scale developed in this study comprised eight severity levels, with lower and upper limits of 0% (no symptoms) and 83.4% necrotic area, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Severity values above 83.4% were not included in the scale, as they were not observed under greenhouse conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo classify plant responses to \u003cem\u003eR. similis\u003c/em\u003e, severity intervals and corresponding grades were assigned as follows: no symptoms (0); \u0026lt;10% necrosis (grade 1); 10\u0026ndash;29.99% necrosis (grade 2); 30\u0026ndash;59.99% necrosis (grade 3); and \u0026gt;\u0026thinsp;60% necrosis (grade 4) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Grade 0 corresponded to rhizomes without visible necrosis; grade 1 was characterized by small, dark, diffuse necrotic lesions emerging from the rhizome margin; grade 2 consisted of moderate, diffuse dark lesions appearing at multiple marginal points; grade 3 was defined by large, dark necrotic lesions covering most of the rhizome margin and extending toward the central region; and grade 4 was characterized by extensive dark necrosis affecting more than 60% of the rhizome, encompassing the entire marginal region and part of the central tissue.\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\u003eClassification scale used to evaluate banana plant responses to \u003cem\u003eRadopholus similis\u003c/em\u003e based on rhizome necrosis severity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhizome Lesion Index (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10% \u0026minus;\u0026thinsp;29.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30% \u0026minus;\u0026thinsp;59.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo ensure the applicability of the proposed scale, ten inexperienced evaluators assessed images of the selected rhizomes in two independent sessions, performed with and without the diagrammatic scale and separated by a seven-day interval.\u003c/p\u003e \u003cp\u003eJoint analysis of the regression coefficients revealed substantially higher accuracy and precision when the diagrammatic scale was used compared with assessments performed without the scale (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Without the scale, several evaluators exhibited intercepts significantly different from zero (evaluators 1, 2, 5, 6, and 10), indicating systematic bias. In addition, slopes significantly different from one were observed for evaluators 1, 5, 6, and 7, indicating distortion in severity estimates as disease severity increased. Although R\u0026sup2; values without the scale ranged from moderate to high (approximately 0.57\u0026ndash;0.78), considerable variability and estimation errors were evident.\u003c/p\u003e \u003cp\u003eIn contrast, when the diagrammatic scale was used, intercepts for nearly all evaluators did not differ significantly from zero, and slopes did not differ from one, indicating elimination of systematic bias. Coefficients of determination increased substantially for most evaluators; for example, R\u0026sup2; values increased from 0.7271 to 0.9361 for evaluator 1, from 0.7559 to 0.9173 for evaluator 2, and from 0.7510 to 0.8633 for evaluator 10. Even evaluators who showed poor performance without the scale (e.g., evaluator 4, R\u0026sup2; = 0.57) exhibited marked improvement when using the scale (R\u0026sup2; = 0.8180). These results demonstrate that the diagrammatic scale enhanced evaluator accuracy and consistency while effectively eliminating statistically significant bias.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntercept (β₀), slope (β₁), and coefficient of determination (R\u0026sup2;) of linear regression equations relating visual severity estimates performed by evaluators without and with the use of the diagrammatic scale.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvaluator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eWithout scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eWith scale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.0890**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3354**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.2634\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0472\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.7782**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1251\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.7506\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9499\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2118\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0218\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0029\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3566\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0030\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3543\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9416\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0204**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8030**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7299**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9519\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.4895**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7425**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8796\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8600**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9204\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3563**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.5421\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0953\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5973\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9520\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1563\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9428\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.7133\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2156\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.6085*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1260\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4664*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0843\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3105\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0274\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** and * significant at the 1% and 5% levels, respectively, according to the t-test. ns, not significant (β₀ = 0 and β₁ = 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResidual analysis further confirmed the improvement in estimation quality provided by the scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Without the scale, residuals were widely dispersed and frequently distant from the zero line, reflecting low precision. Some evaluators showed a systematic tendency to overestimate (positive residuals) or underestimate (negative residuals) disease severity, particularly at intermediate and high severity levels. In contrast, when the scale was used, residuals were more tightly clustered around zero, with reduced dispersion, indicating greater consistency and a marked reduction in estimation error.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of inter-evaluator agreement using coefficients of determination (R\u0026sup2;) revealed substantial heterogeneity when assessments were performed without the scale (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While some evaluator pairs exhibited relatively high agreement (e.g., evaluators 1 \u0026times; 2, R\u0026sup2; = 0.88; evaluators 6 \u0026times; 7, R\u0026sup2; = 0.87), others showed weak agreement (e.g., evaluators 4 \u0026times; 10, R\u0026sup2; = 0.60), indicating the absence of a common assessment standard. When the diagrammatic scale was applied, R\u0026sup2; values generally increased and became more consistent, typically ranging from 0.70 to 0.92. Notably, evaluator pairs with previously low agreement showed marked improvement (e.g., evaluators 4 \u0026times; 10, R\u0026sup2; increased from 0.60 to 0.82), demonstrating that the scale harmonized severity estimates among evaluators.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients of determination (R\u0026sup2;) from linear regression analyses between evaluator pairs for assessments performed without and with the diagrammatic scale.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvaluators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eWithout scale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWith scale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese patterns were clearly illustrated in the heatmap representation of inter-evaluator agreement (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Without the scale, the heatmap displayed pronounced heterogeneity, with wide variation in agreement levels across evaluator pairs. With the use of the diagrammatic scale, the heatmap became more homogeneous and darker, reflecting consistently higher R\u0026sup2; values. Several evaluator pairs achieved agreement values\u0026thinsp;\u0026ge;\u0026thinsp;0.80, including pairs that previously exhibited poor concordance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLin\u0026rsquo;s concordance correlation coefficient (CCC) analysis further supported the effectiveness of the proposed scale (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Without the scale, the CCC indicated moderate agreement, with a scale shift coefficient of 0.7489 and a negative location shift (\u0026ndash;0.0731), reflecting systematic underestimation. When the scale was used, the CCC increased substantially, the scale shift coefficient rose to 0.8984, and the location shift approached zero (0.0078), indicating near elimination of systematic bias. The bias correction factor (Cb) approached unity, and Pearson\u0026rsquo;s correlation coefficient increased, confirming improvements in both accuracy and precision.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of visual severity estimates performed by ten evaluators without and with the use of the diagrammatic scale based on Lin\u0026rsquo;s concordance statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoeficiente de concord\u0026acirc;ncia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.730; 0.788]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.869; 0.905]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesvio de escala\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.717; 0.782]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.870; 0.927]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesvio de localiza\u0026ccedil;\u0026atilde;o\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.116; -0.028]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[-0.021; 0.036]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFator de corre\u0026ccedil;\u0026atilde;o de vi\u0026eacute;s\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.943; 0.969]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.990; 0.997]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoeficiente de correla\u0026ccedil;\u0026atilde;o\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.769; 0.821]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.875; 0.910]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Lin\u0026rsquo;s concordance correlation coefficient: combines measures of precision and accuracy to assess the degree of agreement with the actual values.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Scale shift coefficient: coefficient relative to perfect agreement (1\u0026thinsp;=\u0026thinsp;perfect agreement between x and y).\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e Location shift coefficient: coefficient relative to perfect agreement (0\u0026thinsp;=\u0026thinsp;perfect agreement between x and y).\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e Bias correction factor: measures the extent to which the fitted regression line deviates from the 45\u0026deg; line. No bias occurs when Cb\u0026thinsp;=\u0026thinsp;1. It is a measure of accuracy calculated from the scale and location shift coefficients.\u003c/p\u003e \u003cp\u003e \u003csup\u003ee\u003c/sup\u003e Pearson\u0026rsquo;s correlation coefficient: measures precision (the strength of the linear relationship).\u003c/p\u003e \u003cp\u003eGraphical representation of Lin\u0026rsquo;s method further illustrated these improvements (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Without the scale, estimated severity values were widely dispersed around the identity line, and Lin\u0026rsquo;s regression line deviated substantially from the 45\u0026deg; line, indicating bias and low agreement. In contrast, when the scale was used, estimates clustered closely around the identity line, and Lin\u0026rsquo;s regression line closely overlapped the line of perfect concordance, demonstrating high accuracy and precision.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, residual plots derived from Lin\u0026rsquo;s method confirmed these findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Without the scale, residuals were large and inconsistent, indicating low agreement between estimated and actual severity values. With the diagrammatic scale, residuals were more homogeneous and closer to zero, confirming that the scale effectively standardized disease severity assessments and improved their reliability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe diagrammatic scale proposed in this study did not include severity values above 83.4%, as such levels were not observed under greenhouse conditions. This outcome is directly related to host behavior at the evaluation time. As nematode populations increase, secondary symptoms associated with root necrosis emerge (Agrios \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gebremichael \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), facilitating nematode migration toward the rhizome and leading to tissue decay and the formation of extensive dark cavities (Agrios \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A genotype is considered susceptible when it allows greater nematode reproduction and exhibits more extensive lesions in roots or rhizomes following infection (Stoffelen et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we developed and validated the first diagrammatic scale specifically designed to assess \u003cem\u003eR. similis\u003c/em\u003e severity in banana rhizomes. Lesion areas were objectively quantified using digital image analysis in 100 rhizomes, enabling the construction of a diagrammatic scale based on real severity values. This approach is fundamental for disease assessment, as it reduces the subjectivity inherent to visual estimations (Martins et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lopes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, evaluators produced estimates closer to actual values, achieving reliable results even under varying conditions (Nutter et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Nutter and Schultz \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Lopes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVisual estimators are naturally prone to overestimating disease severity in the absence of diagrammatic references, often relying on lesion size and number as primary cues (Capucho et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lopes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, evaluator training has been shown to be an effective strategy for reducing bias and improving accuracy (Teramoto et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sousa et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings reinforce the concept that assessment quality depends not only on the evaluation tool but also on individual factors such as training, experience, and visual perception (Amorim and Bergamin Filho \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Figueiredo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Indeed, targeted training can significantly enhance estimation quality (Michereff et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Conversely, lesion morphology complexity, psychological responses to visual stimuli, fatigue, and emotional state may negatively affect assessment precision (Kranz \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sherwood et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Nutter Jr. et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sachs et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sousa et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInaccurate visual estimates may compromise experimental conclusions (Parker et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Therefore, the establishment of a standardized system for quantifying banana rhizome necrosis represents a valuable contribution to studies on \u003cem\u003eR. similis\u003c/em\u003e severity. In the present study, disease severity assessments performed with the proposed scale showed high precision among evaluators. Estimated severity values closely matched actual measurements, confirming the scale\u0026rsquo;s accuracy. In this context, accuracy is defined as the proximity between observed and estimated values and can be evaluated through regression intercept (β₀) and slope (β₁). Deviations from zero (β₀) and one (β₁) indicate constant or systematic errors in estimation (Belasque Jr. et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sussel et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lenz et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sousa et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Use of the proposed scale resulted in greater homogeneity and closer agreement with actual values, confirming its effectiveness in standardizing and validating visual severity assessments.\u003c/p\u003e \u003cp\u003eAccordingly, the results demonstrate that the diagrammatic scale significantly reduced overestimation errors, particularly among inexperienced evaluators (Sousa et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For this reason, similar studies proposing diagrammatic scales have been increasingly reported for different crops and pathosystems. For example, the diagrammatic scale developed by de Andrade et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) represents a significant advance in assessing soybean seed coating quality by standardizing a process traditionally subject to evaluator subjectivity. That scale was constructed using artificial intelligence tools to objectively quantify coating coverage, enabling reliable visual classification into quality categories.\u003c/p\u003e \u003cp\u003eLikewise, Figueiredo et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) established a diagrammatic scale for whole leaves of \u003cem\u003eCoffea arabica\u003c/em\u003e with seven severity levels, which demonstrated greater precision, accuracy, and reproducibility after validation with ten evaluators, making it suitable for both field and laboratory assessments. Rojas-Chac\u0026oacute;n et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) adapted this approach to leaf discs of mutagenized plants, developing a specific scale (0\u0026ndash;52.15%) capable of discriminating genotypes with different resistance levels and providing practical benefits for breeding programs. Similarly, Todd et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) proposed a standardized diagrammatic scale for assessing Fusarium yellows severity in sugar beet, which outperformed traditional ordinal scales and showed high precision and accuracy (Cb\u0026thinsp;=\u0026thinsp;0.99) when tested by inexperienced evaluators.\u003c/p\u003e \u003cp\u003eUnlike ordinal classification systems, the diagrammatic scale developed in the present study allows precise determination of the percentage of affected area using real reference values (Stonehouse \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Godoy et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Leite and Amorim 2002; Belasque et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Michereff et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Spolti et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This feature enhances symptom quantification and makes the proposed scale particularly relevant for banana breeding programs worldwide. Moreover, the results confirm its applicability in future research conducted under greenhouse conditions.\u003c/p\u003e \u003cp\u003eFinally, although lesion classification scales are an important approach for symptom evaluation in phytoparasitic nematode studies, they should not be used in isolation. Complementary parameters, such as nematode population density, percentage of dead roots, and root necrosis indices, must also be considered. Integration of these indicators provides a more reliable assessment of genotype resistance or susceptibility (Sousa et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents the first quantitatively defined and statistically validated diagrammatic scale for assessing rhizome necrosis severity in banana caused by Radopholus similis. The developed scale demonstrated high accuracy, precision, and reproducibility, effectively reducing systematic bias and variability associated with visual assessments, even when applied by inexperienced evaluators. The use of real severity values obtained through digital image analysis enabled the construction of a robust and representative tool, fully aligned with methodological principles recommended for diagrammatic scale development. Statistical validation confirmed its applicability as a standardized method for quantifying banana rhizome necrosis under greenhouse conditions. The proposed scale represents a practical and reliable tool for resistance studies, phenotyping, and banana breeding programs, contributing to the standardization of disease assessments and improving comparability among experiments. When used in combination with other phytopathological and nematological parameters, this scale can strengthen the characterization of Musa genotype responses to R. similis infection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflict of Interest\u003c/strong\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was financially supported by the Coordination for the Improvement of Higher Education Personnel \u0026ndash; Brazil (CAPES).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eConceptualization: ABPS; methodology: ABPS, AdJR, BSLdN, LSL, KVMC, LSR, and EPA; original draft preparation: ABPS; data interpretation: ABPS and CAdSL; funding acquisition, supervision, manuscript review and editing: EPA.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank the Graduate Program in Biotechnology (PPGBiotec) at the State University of Feira de Santana for institutional support. The authors also acknowledge the National Council for Scientific and Technological Development (CNPq) for the research fellowship granted to researcher Amorim EP, and the Coordination for the Improvement of Higher Education Personnel (CAPES) for financial support. The authors express their special appreciation to the reviewers who contributed to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgrios, G. N. (2005). Plant diseases caused by fungi. In G. N. Agrios (Ed.), \u003cem\u003ePlant Pathology\u003c/em\u003e (pp. 386\u0026ndash;614). Academic.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmorim, L., \u0026amp; Bergamin Filho, A. (2018). Fenologia, Pantometria e Quantifica\u0026ccedil;\u0026atilde;o de danos. In Amorim L. Rezende JAM, Bergamin Filho A. 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Development and validation of standard area diagrams to aid assessment of pecan scab symptoms on fruit. \u003cem\u003ePlant Pathology\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e, 325\u0026ndash;335.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Accuracy, Banana, Disease severity, Diagrammatic scale, Plant-parasitic nematode","lastPublishedDoi":"10.21203/rs.3.rs-8920541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8920541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate quantification of rhizome necrosis caused by \u003cem\u003eRadopholus similis\u003c/em\u003e is essential for resistance studies, phenotyping, epidemiological analyses, and nematode management in banana. However, visual assessment of rhizome necrosis remains poorly standardized and highly subjective. This study aimed to develop and statistically validate a diagrammatic scale to quantify the severity of rhizome necrosis in banana plants infected by R. similis. One hundred rhizomes exhibiting a wide range of necrosis levels were selected from greenhouse-grown, inoculated plants. Rhizomes were photographed, digitally processed, and the necrotic area was quantified using ImageJ software. Based on these measurements, a diagrammatic scale with eight severity levels (0, 5.4, 9.6, 20.0, 30.5, 40.1, 56.7, and 83.4%) was constructed. Scale validation was performed by ten inexperienced evaluators who estimated disease severity in two independent assessments conducted with and without the aid of the proposed scale. Accuracy, precision, and agreement were evaluated using linear regression analyses, coefficients of determination (R\u0026sup2;), and Lin\u0026rsquo;s concordance correlation coefficient (CCC). Use of the diagrammatic scale significantly improved evaluator performance, resulting in higher R\u0026sup2; and CCC values and a substantial reduction in systematic bias. The results demonstrate that the proposed diagrammatic scale is a robust, accurate, and reproducible tool for standardizing the assessment of banana rhizome necrosis caused by \u003cem\u003eR. similis\u003c/em\u003e, with direct applicability to resistance screening and banana breeding programs.\u003c/p\u003e","manuscriptTitle":"Development and statistical validation of a diagrammatic scale for assessing rhizome necrosis severity in banana caused by Radopholus similis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 11:26:19","doi":"10.21203/rs.3.rs-8920541/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27794ded-ee8b-49fc-8b11-26fa31df007a","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T09:59:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 11:26:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8920541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8920541","identity":"rs-8920541","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}