Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance

Yang, Chao

doi:10.1007/s11119-009-9122-4

Cited by 66 publications

(42 citation statements)

References 24 publications

(38 reference statements)

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“…Os comprimentos de onda de 550, 553 e 554 nm ocorrem em 7 de 8 datas de DAE (Tabela 3). Nesta faixa espectral ocorre um aumento significativo na reflectância com a diminuição da concentração de clorofila (Yang, 2010).…”

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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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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“…This range is also represented by the wavelength of higher PLS regression coefficient (7,438.25), given by 532 nm in FEVP with 60 DAE. The disease progress promotes decreased chlorophyll concentration, causing a significant rise in reflectance in this spectral range (YANG, 2010). Other wavelengths exist in transition areas of green with ranges of blue and red, such as 506, 593 to 606 and 608 to 610 nm (frequency of seven times), as well as 504, 509 and 517 nm (greater regression coefficient in 25 DAE in FEVP, and 69 and 96 DAE in UFV, respectively).…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, reflectance in wavelengths near infrared (700-1,050 nm) is high due to air multiple spreading across cell interfaces in internal tissues (MAHLEIN et al, 2013;PRABHAKAR et al, 2013;YANG, 2010;PONZONI & SHIMABUKURO, 2009). Thus, specific wavelengths can provide useful information when monitoring reflectance spectroscopy in crops with environmental stresses.…”

Section: Introductionmentioning

confidence: 99%

White mold detection in common beans through leaf reflectance spectroscopy

Machado

Pinto²,

Paula

et al. 2015

Eng. Agríc.

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This study aimed to identify wavelengths based on leaf reflectance (400-1050 nm) to estimate white mold severity in common beans at different seasons. Two experiments were carried out, one during fall and another in winter. Partial Least Squares (PLS) regression was used to establish a set of wavelengths that better estimates the disease severity at a specific date. Therefore, observations were previously divided in two sub-groups. The first one (calibration) was used for model building and the second subgroup for model testing. Error measurements and correlation between measured and predicted values of disease severity index were employed to provide the best wavelengths in both seasons. The average indexes of each experiment were of 5.8% and 7.4%, which is considered low. Spectral bands ranged between blue and green, green and red, and red and infrared, being most sensitive for disease estimation. Beyond the transition ranges, other spectral regions also presented wavelengths with potential to determine the disease severity, such as red, green, and near infrared. KEYWORDS:

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“…However, the crude spectral resolution of the reflected and emitted energy from the earth is the primary limiting factor in differentiating subtle differences of plant stresses [10]. Consequently, hyperspectral remote sensing is currently being investigated by scientists with regard to the detection and identification of plant health/stress status [11]. Although airborne and spaceborne hyperspectral sensors can also acquire simultaneously imaging and spectrum in a single scanning process, lack of near-ground prior knowledge of stressed plants has greatly limited such data in the wide applications of precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer

Zhao

Zhang

Luo

et al. 2012

Computer and Computing Technologies in Agriculture V

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Abstract. This study is to identify leaf-scale wheat aphids using the nearground hyperspectral Pushbroom Imaging Spectrometer (PIS). Firstly, the spectral characteristics between normal and aphid-infested wheat leaves were compared in spectral reflectance. Concerning the serious aphid damage level, it is obvious that its spectral curve is badly flattened such as green peak (centered around 550 nm), red valley (centered around 680 nm), due to the influence of aphid. Specifically, in the visible spectrum (500-701 nm), the maximum delta (the maximum value minus the minimum value) is 3.3 and it is 7.5 in the nearinfrared spectrum (701-900 nm). Then, the spectral difference and change rate were further analyzed. It seems that both curves show the mirror symmetry and their maximum values are 55.8% and 17.4%, respectively. For the difference curve, the value is negative in the visible spectrum (400-700 nm), which shows that the reflectance of normal wheat leaf is less than that of the serious level. Conversely, it is greater in the near-infrared spectrum (700-900 nm). Finally, based on the high spatial resolution PIS image, ENvironment for Visualizing Images (ENVI-EX) was utilized to extract aphids and the overall accuracy reaches 97%. The result indicates that the PIS is sufficient to identify the wheat aphids and this study can lay a foundation for further applications in precision agriculture using such a hyperspectral imaging system.

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Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance

Cited by 66 publications

References 24 publications

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

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

White mold detection in common beans through leaf reflectance spectroscopy

Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer

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