How much do standard area diagrams improve accuracy of visual estimates of the percentage area diseased? A systematic review and meta-analysis

Ponte, Emerson M. Del; Cazón, Luis Ignácio; Alves, Kaique S.; Pethybridge, Sarah J.; Bock, Clive H.

doi:10.1007/s40858-021-00479-5

Cited by 12 publications

(4 citation statements)

References 39 publications

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“…It provides a comprehensive assessment of the agreement by measuring both the correlation and the bias between the predicted and real values. Therefore, the ccc has been widely recommended and utilized in studies that involve comparing estimated severity values with actual severity values in phytopathometry [ 25 ][ 87 ][ 88 ]. This parameter was calculated using a resampling approach between predicted and visual values for each parameter using the R package “ yardstick ” [ 89 ].…”

Section: Methodsmentioning

confidence: 99%

“…Despite being widely used, visual DS estimations can be imprecise and biased for diseases with small and numerous lesions like rust [ 25 ]. Therefore, researchers usually also analyse IF and/or PS that are less prone to user bias to quantify more precisely partial resistance in pea [ 71 ].…”

Section: Processing Time Optimizationmentioning

confidence: 99%

“…These parameters defined as objective are weakly affected by user bias but highly time consuming when screening large germplasm collections. Contrary to IF or CS, DS is a subjective parameter highly dependent on the user interpretation that requires specialized training [ 25 ]. DS is also affected by IT, so the user can confound the area surrounding pustules that sometimes develop chlorotic/necrotic regions.…”

Section: Introductionmentioning

confidence: 99%

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RGB image-based method for phenotyping rust disease progress in pea leaves using R

Osuna-Caballero,

Olivoto,

Jiménez-Vaquero

et al. 2023

Plant Methods

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Background Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. Results A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method’s optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin’s concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. Conclusions A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.

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Section: Methodsmentioning

confidence: 99%

Section: Processing Time Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RGB image-based method for phenotyping rust disease progress in pea leaves using R

Osuna-Caballero,

Olivoto,

Jiménez-Vaquero

et al. 2023

Plant Methods

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“…For GLS, the intensity of the disease can be expressed by determining the severity, that is, by the proportion or percentage of diseased tissue in relation to the total tissue (Madden et al, 2007). As a strategy for quantifying diseases in this way, standard area diagram sets (SADs) stand out, helping to minimize errors during estimates by providing illustrated representations of the possible severity levels of the disease (Del Ponte et al, 2017Ponte et al, , 2022. However, a SADs for the foliar assessment of grey leaf spot in corn pathology has not yet been developed, validated and used.…”

mentioning

confidence: 99%

Development and validation of a standard area diagram set to assess corn grey leaf spot severity and foliar fungicide control efficacy

Rocha,

Custódio,

Fantin

et al. 2024

Journal of Phytopathology

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Grey leaf spot (GLS), caused by Cercospora spp., has become a very important foliar disease for second‐crop corn season, mainly in South America. Therefore, this study aimed to develop and validate a standard area diagram set (SADs) to estimate the severity of GLS and to apply the SADs established in this study to evaluate the efficacy of fungicides for GLS control in field trials. The SADs with nine levels of severity (0.5; 2; 5; 10; 20; 30; 40; 50 and 60%) improved accuracy, precision and reliability. To evaluate the control of GLS in corn, two experiments spraying chemical fungicides were conducted, one in Rio Verde (GO) (experiment A) and another in Chapadão do Sul (MS) (experiment B). Both experiments were conducted in randomized block design, with 10 treatments and four replicates. For the severity assessment, the SADs elaborated in this study was used. From this severity data over time, disease progress curves, area under the disease progress curve (AUDPC), and yield were obtained. Based on the AUDPC and yield values, all treatments differed from the control. The fungicide fluxapyroxad + pyraclostrobin + mefentrifluconazole had the highest value, giving 72.8% control efficacy and the highest yield maintenance of 43.5%. The other treatments had an intermediate control efficacy and yield. The SADs proposed here is a useful tool for improving visual assessments of GLS severity on corn leaves and fungicides can be used for integrated disease management.

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Incidence-severity relationships in non-treated and fungicide-treated wheat head blast epidemics in Brazil

2022

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How much do standard area diagrams improve accuracy of visual estimates of the percentage area diseased? A systematic review and meta-analysis

Cited by 12 publications

References 39 publications

RGB image-based method for phenotyping rust disease progress in pea leaves using R

RGB image-based method for phenotyping rust disease progress in pea leaves using R

Development and validation of a standard area diagram set to assess corn grey leaf spot severity and foliar fungicide control efficacy

Incidence-severity relationships in non-treated and fungicide-treated wheat head blast epidemics in Brazil

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