Using colour image analysis for quantitative assessment of powdery mildew on cucumber

Kampmann, H.; Hansen, Ole Billing

doi:10.1007/bf00023572

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…However, it was mostly used in combined multi-spectral methods with other spectral indices where NDVI acted as a first level biomass sensor in order to discard non-plant spectra [31,32,48] and subsequently the analysis proceeded with the use of other indices. Moreover, these studies based on multi-spectral methods [31,32,48] and previous studies based on digital image analysis [27,29,30] focused on disease detection, leaf classification with regard to infection status and disease level, but its association with yield loss was not described. In this study, the effectiveness of NDVI with regard to GY prediction decreased with disease spread, suggesting that color changes at the canopy level associated disease spread were missed or omitted by NDVI.…”

Section: Performance Of Vegetation Indicesmentioning

confidence: 98%

“…The efficacy of RGB digital methods for the evaluation of a pest or disease at the leaf level has also been reported, including powdery mildew on cucumber leaves [27], assessment of foliar disease symptom severities in corn, wheat and soybean [28], determination of the impact of disease severity of specific grain diseases [29], and of different types of fungal diseases in wheat [30,31]. In all these cases image analysis techniques were employed to detect the presence of the pest or disease and the infected, necrotic and/or dry areas using scans or photographic images of leaves or other plant parts.…”

Section: Introductionmentioning

confidence: 98%

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Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

et al. 2015

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The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010-2013 a new strain of this pathogen (Warrior/Ambition), against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red, Green, Blue (RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index (NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R 2 = 0.581 and R 2 = 0.536, respectively), far surpassing the predictive ability of NDVI (R 2 = 0.118 and R 2 = 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords:Wheat yellow rust Field phenotyping NDVI Phenology, Puccinia striiformis RGB-based indices Triticum durumT H E C R O P J O U R N A L 3 ( 2 0 1 5 ) 2 0 0 -2 1 0

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Section: Performance Of Vegetation Indicesmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

et al. 2015

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“…Nesse sentido, torna-se essencial investir no desenvolvimento e utilização de novas técnicas que facilitem o trabalho de seleção. Os avanços da informática, nos últimos anos, têm viabilizado operações como a análise de imagens digitalizadas, que já vem sendo utilizada para quantificar área foliar infectada por moléstias (KAMPMANN & HANSEN, 1994;THOMÉ et al, 1997a). O melhoramento para caracteres quantitativos como a resistência parcial a moléstias torna-se difícil, principalmente porque os efeitos de genes individuais controlando estes caracteres não podem ser facilmente identificados.…”

Section: Uso Da Resistência Parcial Em Programas De Melhoramentounclassified

Melhoramento para resistência parcial a moléstias fúngicas em cereais

et al. 1999

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A resistência parcial é uma estratégia promissora para ser utilizada pelos melhoristas de plantas na tentativa de aumentar a eficiência no lançamento de cultivares com resistência durável. O objetivo deste artigo é o de discutir os principais aspectos da utilização prática desse tipo de resistência em programas de melhoramento de cereais. As formas de avaliação da resistência parcial, as dificuldades de selecionar esta característica e a durabilidade deste tipo de resistência são discutidas. Alternativas para utilização da resistência parcial em programas de melhoramento são apresentadas e representam, hoje, uma estratégia importante no desenvolvimento de genótipos com maior estabilidade de desempenho frente aos ambientes variáveis e propícios à ocorrência de moléstias fúngicas em cereais.

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“…Recent improvements in computer performance and reductions in the cost of digital imaging hardware and software, have contributed to the widespread use of digital image processing in biological and agricultural research. Digital image analysis has been applied to the quantitative evaluation of various features of biological organs, for example, in the assessment of various diseases (Lindow & Webb, 1983;Kampmann & Hansen, 1994;Tucker & Chakraborty, 1997;Martin & Rybicki, 1998;Martin et al, 1999;Niemira et al, 1999;Olmstead & Lang, 2001), measurement of plant canopies (Han & Hayes, 1990;Blackmer & Schepers, 1996;Adamsen et al, 1999;Ewing & Horton, 1999;Lukina et al, 1999;Purcell, 2000, Richardson et al, 2001Karcher & Richardson, 2003), and evaluation of tomato maturity (Choi et al, 1995;Polder et al, 2002). Despite the good progress in quantitative evaluation by digital imaging, no methods have yet demonstrated the effective evaluation of subtle variation in flower colour patterns.…”

Section: Introductionmentioning

confidence: 98%

Quantitative evaluation of flower colour pattern by image analysis and principal component analysis of Primula sieboldii E. Morren

et al. 2004

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In Primula sieboldii E. Morren, many cultivars have been bred with the purpose of obtaining various petal colour patterns. Colour pattern is an important breeding target in this species, and the availability of an objective and quantitative evaluation method is of vital importance for both genetic analysis and variety registration. Our objective was to establish a new quantitative evaluation method of P. sieboldii flower colour patterns in regions of interest (ROIs) by principal component analysis (PCA). We first set a ROI in each petal as a region that represented the petal colour pattern and defined the maximum square on each petal as the ROI. We then converted each ROI image to a 10 × 10 pixel mosaic image and defined a total of 300 variables (the colour values for red, green and blue components of the 100 pixels) per mosaic image. Finally, we summarized the information on the 300 variables by PCA, and redrew the mosaic images to correspond to some typical principal component scores to determine the effect of each principal component on colour pattern. By this method, we detected five different features of petal colour pattern, four of which were revealed to be mainly genetically controlled. Thus, we successfully established a procedure for evaluating petal colour patterns in P. sieboldii cultivars. This new procedure can be used as a basis for an objective and efficient variety registration system.

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Using colour image analysis for quantitative assessment of powdery mildew on cucumber

Cited by 11 publications

References 9 publications

Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

Melhoramento para resistência parcial a moléstias fúngicas em cereais

Quantitative evaluation of flower colour pattern by image analysis and principal component analysis of Primula sieboldii E. Morren

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