Aerial and terrestrial digital images for quantification of powdery mildew severity in Ayocote bean (Phaseolus coccineus)
By
Alfonso Muñoz Alcalá,
Gerardo Acevedo Sánchez,
Diana Gutiérrez Esquivel,
Oscar Bibiano Nava,
Ivonne García González,
Norma Ávila Alistac,
María José Armenta Cárdenas,
María del Carmen Zúñiga Romano,
Juan José Coria Contreras,
Serafín Cruz Contreras,
Gustavo Mora Aguilera*
* Corresponding Author. Email: / Institution: Colegio de Postgraduados
Accepted: 19/February/2024 – Published: 06/March/2024 – DOI: https://doi.org/10.18781/R.MEX.FIT.2312-1
Abstract Background/Objective. Epidemiological research on Phaseolus coccineus is lacking. The aim was to develop and validate digital methods to quantify the severity associated with powdery mildew in ayocote bean.
Materials and Methods. An ayocote bean plot with 65.3 % incidence and 22.7 % average powdery mildew foliar severity was selected. Based on 250 leaves collected in field with varying severity degrees, eight 7- and 8-class logarithmic-diagrammatic scales (ELD) were designed and validated in a controlled environment (CEV) and field (FV). In Rstudio®, accuracy (β), precision (R2), reproducibility (r), and agreement level were determined with Cohen’s kappa index (κw) and Lin’s concordance coefficient (LCC). Additionally, a Hierarchical Cluster Analysis (HCA) was performed by scale and assessment environment for clustering by similarity evaluation. In ArcMap® v10.3, in a 15-quadrant block, an ‘image segmentation’ analysis was performed using supervised classification and maximum likelihood to estimate powdery mildew severity and an indicator of canopy coverage index (VCI).
Results. In VEC-1, v1r2 (ELD-7c; β=1.07, R2=0.93, r=0.87) and v1r1 (ELD-8c; β=0.97, R2=0.85, r=0.87) scales were best evaluated. In VEC-2, comparing clusters conformed in the HCA, the ELD-7c was the best scored with perfect accuracy (β>0.96), very high precision (R2>0.94), very high reproducibility (r=0.97-0.99) and very high agreement (κw>0.96; LCC>0.97); and in ELD-8c reproducibility and agreement decreased. In VCa, ELD-7c maintained optimal metrics, but ELD-8c reached ideal parameters for preventive ELD in early stages of powdery mildew (β>0.98, R2>0.98, r=0.99, κw=0.99-0.999, LCC=0.98-0.999). Image analysis estimated severity = 8.4 % (CI = 5.3 - 12.6 %) and ICV = 0.88 (CI = 0.76 - 0.99), contrasting with field assessment 47 % (CI = 38.8 - 55.3 %) and 0.46 (CI = 0.76 - 0.99), respectively, mainly with ICV > 0.94 due to less symptomatic leaf exposure. Suggests applicability for canopy estimation with restrictions for severity based on pathogen expression.
Conclusion. A methodology for ELD development is proposed, comprising: image acquisition, processing and quantification; controlled validation and field validation. Validation statistics included precision (R2); accuracy (β); reproducibility (Pearson’s coefficient and Hierarchical Cluster Analysis); and agreement (Lin’s Coefficient and Kappa Index), proposed in a comprehensive approach for first time. RGB-drone images are proposed to estimate a comprehensive vigor and severity coverage index.
Keywords:
Erysiphe vignae, scales-logarithmic, RStudio
Figure 1. Final versions of logarithmic-diagrammatic severity scales for powdery mildew in Ayocote bean (Phaseolus coccineus), selected for field validation. A) 7-class logarithmic-diagrammatic scales; B) 8-class logarithmic-diagrammatic scales.
Figure 2. A1 and B1. Logarithmic-diagrammatic scales of 7 and 8 classes for assessment of powdery mildew severity on Ayocote bean (P. coccineus) leaves, during the Controlled Environment Validation (CEV) process of 30 leaves by nine raters. A2 and B2. Heatmap of Pearson correlation coefficient (r) among nine raters by severity scale. Values of r = 0.8 – 1 indicate the reproducibility of each scale among raters. A3 and B3. Heatmap of severity class in 30 leaves evaluated by scale and rater. The color represents the class value assigned by the rater for each leaf. For rater and leaves, a Hierarchical cluster analysis is plotted, grouped by the complete method and Euclidean distance.
Figure 3. Correlation graphs between severity (y) assessed using the scale and real values (x) by nine raters during the Controlled Environment Validation (CEV) with 30 Phaseolus coccineus leaves. The linear regression equation (y = βo + βx + e) is fitted to determine β, R2, and p-value parameters using the stat_poly_eq function. A. LDS-7 classes. B. LDS-8 classes.
Figure 4. A1 and B1. Logarithmic-diagrammatic scale of 7 and 8 classes for assessing powdery mildew severity on Ayocote bean (P. coccineus) leaves during the validation process of 30 leaves in the field by four selected raters. A2 and B2. Heatmap of Pearson correlation coefficient (r) among nine raters based on severity scale. Values of r = 0.8 – 1 indicate the reproducibility level of each scale among raters. A3 and B3. Heatmap of severity class on 30 leaves assessed by scale and rater. The color represents the class value assigned by the rater to each leaf. Hierarchical cluster analysis is performed by grouping raters and leaves using the ‘complete’ method and Euclidean distance
Figure 5. Correlation plots between severity (y) assessed by scale and actual values (x) from four raters during Field Validation (VCa) with 30 Phaseolus coccineus leaves. The linear regression equation (y = βo + βx + e) is fitted to determine parameters β, R2, and p-value using the stat_poly_eq function. A. LDS-7classes. B. LDS-8classes.
Figure 6. Estimation of canopy and severity indicators using RGB imagery (13 mpx) from Phantom 3 processed through supervised segmentation algorithm in ArcMap® v10.3. A1. Image of the total experimental area (40 x 52 m). Captured at 50 m altitude. A2. Block of 15 selected quadrants based on uniformity in host continuity, canopy, and maximum inductivity. Continuous yellow lines depict quadrant divisions. Dashed white lines represent selected blocks for algorithm versus real image estimation. Captured at 27 m. A3. Image at 5 m of a selected sector for designing ‘RGB signature’ with crop categories (foliar tissue, flowering, powdery mildew, and soil coverage).
Table 1. Average accuracy (β), precision (R2), and reproducibility (r) of eight logarithmic-diagrammatic severity scales for evaluating powdery mildew in Phaseolus coccineus.
Table 2. Parametric comparison of nine raters relative to the real value, to determine accuracy (βx), precision (R2), and agreement (LCC, κw) by severity class assessed during the validation process in a controlled environment (CEV) and in the field (FV)
Table 3. Comparison of canopy coverage index (VCI), plant canopy (PVI), and powdery mildew severity percentage estimated using RGB-drone image and field assessments.
