Abstract
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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Claims 9
Claim · model-written summary
A method automatically estimates pustule and leaf area parameters.
- Outcome
- pustule number, pustule size, leaf area, and percentage of pustule coveragee0
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Verbatim
e0“The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage.”
Claim · model-written summary
The method reconstructs leaf time series and disease progression parameters.
- Outcome
- disease progression parameterse0
Not reportedTaxonValue
Verbatim
e0“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.”
Claim · model-written summary
Disease response varied significantly among pea genotypes using visual and image analysis.
- Outcome
- variation in disease responsese0
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Verbatim
e0“Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis.”
Claim · model-written summary
NGRDI analyzed 600 leaflets at 60% resolution in 62 seconds.
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Verbatim
e0“the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing.”
Claim · model-written summary
Image and visual pustule counts had concordance above 0.98.
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Verbatim
e0“Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution.”
Claim · model-written summary
Lower image resolution slightly reduced accuracy, without significant differences for most progression measures.
Not reportedTaxonValue
Verbatim
e0“While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space.”
Claim · model-written summary
Lower resolution reduced processing time and storage space.
Not reportedTaxonValue
Verbatim
e0“significantly reducing processing time and storage space.”
Claim · model-written summary
NGRDI was optimal at all time points with high accuracy and little accumulated error.
- Outcome
- highly accurate estimations with minimal accumulated errore0
Not reportedTaxonValue
Verbatim
e0“NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error.”
Claim · model-written summary
The method was more accurate than other image approaches and comparable to visual methods.
Not reportedTaxon
Verbatim
e0“It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches.”