Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance

Cao, Xueren; Luo, Yunbo; Zhou, Yilin; Duan, Xiayu; Cheng, Dan

doi:10.1016/j.cropro.2012.12.002

Cited by 67 publications

(37 citation statements)

References 39 publications

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“…Traditionally, assessment of plant disease severity relies on visual inspection of plant tissue by trained raters [36][37][38][39][40]53], who categorize disease severity according to a discrete scale [31]. Disease symptoms often result from physiological changes that may alter the spectral pattern of the plant [54]. We have shown that image acquisition and processing offers a feasible alternative to the more classical visual assessments of disease severity evolution and quantification in plants [55].…”

Section: Discussionmentioning

confidence: 99%

Using UV-C radiation and image analysis for fungus control in tomato plants

Valencia¹,

Patiño²,

Herrera-Ramírez³

et al. 2017

Opt. Pura Apl.

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Section: Discussionmentioning

confidence: 99%

Using UV-C radiation and image analysis for fungus control in tomato plants

Valencia¹,

Patiño²,

Herrera-Ramírez³

et al. 2017

Opt. Pura Apl.

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“…By analyzing the confusion matrix, the misclassification from infected pixels to healthy pixels was the major error, which led to a high omission error (31.58 %) and a relatively low commission error (18.75 %). Such misclassifications usually occur in samples with slight infection symptoms and particularly in fields with a high level of heterogeneity (Zhang et al 2012;Cao et al 2013). Unlike some data mining methods (e.g., artificial neural network, support vector machine) with complex principles, the SAM has an explicit physical basis and a straightforward computational scheme, which makes it efficient for disease mapping.…”

Section: Mapping Of Powdery Mildew With Sam Modelmentioning

confidence: 98%

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Yuan

Zhang

et al. 2015

Precision Agric

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Efficient crop protection management requires timely detection of diseases. The rapid development of remote sensing technology provides a possibility of spatial continuous monitoring of crop diseases over a large area. In this study, to monitor powdery mildew in winter wheat in an area where a severe disease infection occurred, the capability of high resolution (6 m) multi-spectral satellite imagery, SPOT-6, in disease mapping was assessed and validated using field survey data. Based on a rigorous feature selection process, five disease sensitive spectral features: green band, red band, normalized difference vegetation index, triangular vegetation index, and atmospherically-resistant vegetation index were selected from a group of candidate spectral features/variables. A spectral correction was processed on the selected features to eliminate possible baseline effect across different regions. Then, the disease mapping method was developed based on a spectral angle mapping technique. By validating against a set of field survey data, an overall mapping accuracy of 78 % and kappa coefficient of 0.55 were achieved. Such a moderate but practically acceptable accuracy suggests that the high resolution multispectral satellite image data would be of great potential in crop disease monitoring.

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“…Assim, a razão entre MCARI e TCARI com OSAVI combina com as respectivas habilidades de cada um, de forma a responder às variações da clorofila, minimizando os efeitos de reflectância de fundo de solo (Zarco-Tejada et al, 2004;Haboudane et al, 2002). Cao et al (2013) apresentaram resultados similares a este, no caso, a correlação significativa de Soil-Adjusted Vegetation Index (SAVI) com índice de severidade de doença do trigo em diferentes estádios fenológicos. Observa-se ainda que entre os índices testados (Tabela 1), os da família CARI são os únicos que utilizam comprimentos de onda e bandas multiespectrais do red-edge, o que corrobora com a importância desta faixa espectral para a estimativa do mofo-branco.…”

Section: Resultsunclassified

Estimativa de severidade do mofo-branco em lavouras de feijão utilizando-se sensores hiper e multiespectral

Machado

Pinto

Queiroz

et al. 2015

Rev. bras. eng. agríc. ambient.

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R E S U M ONeste estudo objetivou-se identificar comprimentos de onda e faixas espectrais provenientes de reflectâncias hiper e multiespectrais utilizando regressão PLS e promover avaliação comparativa desses métodos e de dez índices de vegetação, para determinar aqueles que melhor estimam níveis de severidade de mofo-branco em feijão. Foram implantados experimentos nos municípios de Viçosa e de Oratórios, estado de Minas Gerais. Reflectâncias hiperespectrais foram obtidas com espectroradiômetro cuja faixa útil de leitura adotada foi entre 440 e 900 nm. Reflectâncias multiespectrais foram obtidas de imagens de câmara constituídas de cinco bandas (vermelho, verde, azul, Red-edge e infravermelho). Os índices de severidade da doença foram baixos; em Viçosa a média foi de 5,8% e em Oratórios, 7,4%. Modelos matemáticos utilizando reflectâncias hiperespectrais tiveram melhor desempenho para estimar mofo-branco; a banda do red-edge apresentou os comprimentos de onda que melhor estimam a severidade do mofo-branco. Índices de vegetação resistentes a efeitos da reflectância de solo estimaram melhor o mofo-branco do que os demais índices.Estimative of white mold severity in common bean crops using hyper and multispectral sensors A B S T R A C TThis study aimed to identify wavelengths and spectral ranges from hyper and multispectral reflectance using PLS regression; and to promote comparative evaluation of these methods and ten vegetation indices to determine those that best estimate levels of white mold severity in common beans. Experiments were implemented in the municipalities of Viçosa and Oratorios, Minas Gerais state. Hyperspectral reflectance measurements were acquired with the spectroradiometer, whose useful reading range was between 440 and 900 nm. Multispectral reflectance measurements were obtained from camera images comprising five bands (red, green, blue, red-edge and infrared). The indexes of disease severity were low. In Viçosa the average was 5.8% and in Oratorios, 7.4%. Mathematical models using hyperspectral reflectance performed better for estimating white mold. The red-edge band presented the wavelengths that best estimate the severity of white mold. Vegetation index resistant to the soil reflectance effects was better to estimate white mold than the other indices. Palavras-chave:regressão PLS índice de vegetação agricultura de precisão

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Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance

Cited by 67 publications

References 39 publications

Using UV-C radiation and image analysis for fungus control in tomato plants

Using UV-C radiation and image analysis for fungus control in tomato plants

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Estimativa de severidade do mofo-branco em lavouras de feijão utilizando-se sensores hiper e multiespectral

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