Measuring plant disease severity in R: introducing and evaluating the pliman package

Olivoto, Tiago; Andrade, Sheila Maria Pereira de; Ponte, Emerson M. Del

doi:10.1007/s40858-021-00487-5

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…In this study, the images of the single diseased wheat leaves of all the severity classes of wheat stripe rust were acquired by using digital camera and smartphones, the segmented leaf images and the segmented lesion images were obtained by using manual image segmentation method in the Adobe Photoshop 2022 software, then the numbers of the whole leaf region pixels and lesion region pixels of each single wheat leaf were achieved by viewing the histogram panel in the software, and subsequently the actual percentage of the lesion area in the area of each whole diseased leaf was calculated for further data processing. In terms of obtaining the actual ratios of lesion areas to the areas of the corresponding whole diseased plant units, in addition to the method of obtaining the actual percentages of lesion areas in the areas of the corresponding whole diseased wheat leaves used in this study, automatic image processing methods can be used to carry out disease image segmentation and obtain the actual ratios of lesion areas to the areas of the corresponding whole diseased plant units by programming or by using the developed software and packages such as APS Assess ( Lamari, 2008 ), ImageJ ( Schneider et al., 2012 ), Leaf Doctor ( Pethybridge and Nelson, 2015 ), and the pliman package ( Olivoto et al., 2022 ), and in some situations, the graph paper method ( Li et al., 2011 ) and the paper-weighing method ( Li et al., 2011 ) can be used to achieve the actual ratios of lesion areas. After obtaining the actual ratios of lesion areas to the areas of the corresponding whole diseased plant units for a plant disease, the reference ranges corresponding to all the plant disease severity classes can be determined according to the methods proposed in this study and then can be used to carry out the disease severity assessment, or the disease severity assessment can be directly carried out according to the severity grading standard established based on the actual ratios of lesion areas to the areas of the corresponding whole diseased plant units.…”

Section: Discussionmentioning

confidence: 99%

Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici

Jiang

Wang

2022

Front. Plant Sci.

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Accurate severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici is of great significance for phenotypic determination, prediction, and control of the disease. To achieve accurate severity assessment of the disease based on the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves, two new methods were proposed for severity assessment of the disease. In the Adobe Photoshop 2022 software, the acquired images of single diseased leaves of each severity class of the disease were manually segmented, and the numbers of the leaf region pixels and lesion pixels of each diseased leaf were obtained by pixel statistics. After calculation of the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves based on the obtained pixel numbers, the training sets and testing sets were constructed for each severity class by using the system sampling method with two sampling ratios of 4:1 and 3:2. Then the mean and standard deviation of the actual percentages of lesion areas contained in each training set were calculated, respectively. For each sampling ratio, two methods, one based on the midpoint value of the means of the actual percentages of lesion areas corresponding to two adjacent severity classes and the other based on the distribution range of most of the actual percentages of lesion areas, were used to determine the midpoint-of-two-adjacent-means-based actual percentage reference range and the 90%, 95%, and 99% reference ranges of the actual percentages of lesion areas for each severity class. According to the determined reference ranges, the severity of each diseased leaf in the training sets and testing sets was assessed. The results showed that high assessment accuracies (not lower than 85%) for the training sets and testing sets were achieved, demonstrating that the proposed methods could be used to conduct severity assessment of wheat stripe rust based on the actual percentages of lesion areas. This study provides a reference for accurate severity assessments of plant diseases.

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Section: Discussionmentioning

confidence: 99%

Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici

Jiang

Wang

2022

Front. Plant Sci.

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“…An open data set made available by Olivoto et al (2022), Olivoto (2021 was used for validation. The authors used many state-of-the-art software to quantify the segmentation results of various plant diseases.…”

Section: Validation Of the Obtained Resultsmentioning

confidence: 99%

Novel approach for quantification for severity estimation of blight diseases on leaves of tomato plant

2022

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This study uses digital image processing and machine learning to quantify the infection patterns on tomato leaves due to blight diseases. Quantification, also known as severity measurement, is a technique to determine how much a leaf is diseased by calculating a numeric value. This value could be a fraction representing how much the diseased region is present on the leaf compared to the entire leaf region, or it can be a percentage value too. There are two main approaches to measuring disease severity; the first technique involves visual estimation using references like standard area diagrams. The second approach involves taking a digital image of the leaf, separating the diseased regions from the healthy regions, and then calculating the area of those two regions. The approach we took is similar. We first took the digital image and segmented the diseased and healthy regions. For quantification, we calculated the ratio of total pixels representing the diseased region to the total number of pixels representing the leaf. While finding ways to improve the accuracy of the segmentation algorithm, we also discovered our segmentation technique which automatically segments the diseased regions of the leaves from the healthy areas using k‐means clustering. The clustering‐segmentation algorithm did give good results for the sample images to which it was applied. The main thing about the clustering‐segmentation algorithm is that it tends to be automatic compared to some of the semi‐automatic segmentation approaches that have been discovered till now. We could reproduce the validated quantification results as other authors achieved in the recent work, which also validated our methodology.

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“…The actual percentage data can be obtained by assessors/raters using the visual observation method. In addition, the actual percentages of lesion areas can be obtained by programming to implement the segmentation of lesion images and the calculation of ratios of the lesion areas to the areas of the corresponding whole single diseased leaves (Li et al, 2011;Jiang et al, 2021), by using special software and packages (Lamari, 2008;Schneider et al, 2012;Pethybridge and Nelson, 2015;Olivoto et al, 2022), and by actual experimental operation methods such as graph paper method and paper-weighing method (Li et al, 2011). The automatic lesion segmentation methods and the automatic calculation methods of the actual percentages of lesion areas in the areas of the corresponding whole single diseased leaves can be combined with the severity assessment methods and models developed in this study to construct automatic severity assessment systems of wheat stripe rust, which is conducive to the implementation of automatic severity assessment of wheat stripe rust and the more convenient practical applications of related technical methods.…”

Section: Discussionmentioning

confidence: 99%

Severity assessment of wheat stripe rust based on machine learning

Jiang

Wang

2023

Front. Plant Sci.

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IntroductionThe accurate severity assessment of wheat stripe rust is the basis for the pathogen-host interaction phenotyping, disease prediction, and disease control measure making.MethodsTo realize the rapid and accurate severity assessment of the disease, the severity assessment methods of the disease were investigated based on machine learning in this study. Based on the actual percentages of the lesion areas in the areas of the corresponding whole single diseased wheat leaves of each severity class of the disease, obtained after the image segmentation operations on the acquired single diseased wheat leaf images and the pixel statistics operations on the segmented images by using image processing software, under two conditions of considering healthy single wheat leaves or not, the training and testing sets were constructed by using two modeling ratios of 4:1 and 3:2, respectively. Then, based on the training sets, two unsupervised learning methods including K-means clustering algorithm and spectral clustering and three supervised learning methods including support vector machine, random forest, and K-nearest neighbor were used to build severity assessment models of the disease, respectively.ResultsRegardless of whether the healthy wheat leaves were considered or not, when the modeling ratios were 4:1 and 3:2, satisfactory assessment performances on the training and testing sets can be achieved by using the optimal models based on unsupervised learning and those based on supervised learning. In particular, the assessment performances obtained by using the optimal random forest models were the best, with the accuracies, precisions, recalls, and F1 scores for all the severity classes of the training and testing sets equal to 100.00% and the overall accuracies of the training and testing sets equal to 100.00%.DiscussionThe simple, rapid, and easy-to-operate severity assessment methods based on machine learning were provided for wheat stripe rust in this study. This study provides a basis for the automatic severity assessment of wheat stripe rust based on image processing technology, and provides a reference for the severity assessments of other plant diseases.

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Measuring plant disease severity in R: introducing and evaluating the pliman package

Cited by 9 publications

References 23 publications

Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici

Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici

Novel approach for quantification for severity estimation of blight diseases on leaves of tomato plant

Severity assessment of wheat stripe rust based on machine learning

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