Early Disease Detection in Wheat Fields using Spectral Reflectance

Bravo, C.; Moshou, Dimitrios; West, Jonathan; McCartney, Alastair; Ramón, Herman

doi:10.1016/s1537-5110(02)00269-6

Cited by 239 publications

(123 citation statements)

References 8 publications

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“…Bravo et al (2003) used in-field spectral images for an early detection of yellow rust infecting wheat, Hillnhütter et al (2011b) successfully discriminated symptoms caused by H. schachtii and Rhizoctonia solani on sugar beet. At the present time, hyperspectral imaging is more widespread in the field of monitoring fruit security and quality.…”

Section: Spectral Signature Of Plant Diseasesmentioning

confidence: 99%

Recent advances in sensing plant diseases for precision crop protection

et al. 2012

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Near-range and remote sensing techniques have demonstrated a high potential in detecting diseases and in monitoring crop stands for sub-areas with infected plants. The occurrence of plant diseases depends on specific environmental and epidemiological factors; diseases, therefore, often have a patchy distribution in the field. This review outlines recent insights in the use of non-invasive optical sensors for the detection, identification and quantification of plant diseases on different scales. Most promising sensor types are thermography, chlorophyll fluorescence and hyperspectral sensors. For the detection and monitoring of plant disease, imaging systems are preferable to non-imaging systems. Differences and key benefits of these techniques are outlined. To utilise the full potential of these highly sophisticated, innovative technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach-including plant pathology, engineering, and informatics-is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide screening can be optimized by these innovative technologies.

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Section: Spectral Signature Of Plant Diseasesmentioning

confidence: 99%

Recent advances in sensing plant diseases for precision crop protection

et al. 2012

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“…Traditionally proportion of diseases and pest damage in a large plant population is assessed based on visual observation of symptomatic plants. However, there are several limitations in assessment of viral diseases with traditional approach that it is often time consuming, labour intensive and erroneous as induction of similar symptoms due to several other reasons [3,6]. Remote sensing data especially reflectance found to be capable of detecting changes in the biophysical properties of plant and canopy associated with pathogens [13,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…However, measurement of reflectance contiguously (hyperspectral remote sensing) as a series of narrow wavelength band provides pertinent information for discrimination of disease and other plant stresses. Use of hyerspectral remote sensing techniques and studies [21,39] has indicated the immense potentiality of hyperspectral imaging for discrimination of disease and other plant stresses and early their detection [3,38]. A few studies have demonstrated the feasibility of using remote sensing data for detection of virus diseases like grapevine leaf roll [23], tobacco mosaic [26], sugarcane yellow leaf [8], barley yellow dwarf and wheat streak mosaic [29,40], potato yellow vein [7] and mungbean yellow mosaic [27].…”

Section: Introductionmentioning

confidence: 99%

Spectral reflectance pattern in soybean for assessing yellow mosaic disease

et al. 2013

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Remote sensing technique is useful for monitoring large crop area at a single time point, which is otherwise not possible by visual observation alone. Yellow mosaic disease (YMD) is a serious constraint in soybean production in India. However, hardly any basic information is available for monitoring YMD by remote sensing. Present study examines spectral reflectance of soybean leaves due to Mungbean yellow mosaic India virus (MYMIV) infection in order to identify YMD sensitive spectral ratio or reflectance. Spectral reflectance measurement indicated significant (p \ 0.001) change in reflectance in the infected soybean canopy as compared to the healthy one. In the infected canopy, reflectance increased in visible region and decreased in near infra-red region of spectrum. Reflectance sensitivity analysis indicated wavelength *642, *686 and *750 nm were sensitive to YMD infection. Whereas, in yellow leaves induced due to nitrogen deficiency, the sensitive wavelength was *589 nm. Due to viral infection, a shift occurred in red and infra-red slope (called red edge) on the left in comparison to healthy one. Red edge shift was a good indicator to discriminate yellow mosaic as chlorophyll gets degraded due to MYMIV infection. Correlation of reflectance at 688 nm (R688) and spectral reflectance ratio at 750 and 445 nm (R750/R445) with the weighted mosaic index indicated that detection of yellow mosaic is possible based on these sensitive bands. Our study for the first time identifies the yellow mosaic sensitive band as R688 and R750/R445, which could be utilized to scan satellite data for monitoring YMD affected soybean cropping regions.

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“…The detection of rhizomania in sugar beet fields was also feasible (Steddom et al 2003). Using a quadratic discriminating model based on reflectance, Bravo et al (2003) classified yellow rust infestation on winter wheat with a reliability of 96%. Larsolle and Hamid Muhammed (2007) computed disease specific spectral signatures of Drechslera triticirepentis infected spring wheat.…”

Section: Introductionmentioning

confidence: 99%

Spectral signatures of sugar beet leaves for the detection and differentiation of diseases

et al. 2010

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This study examines the potential of hyperspectral sensor systems for the nondestructive detection and differentiation of plant diseases. In particular, a comparison of three fungal leaf diseases of sugar beet was conducted in order to facilitate a simplified and reproducible data analysis method for hyperspectral vegetation data. Reflectance spectra (400-1050 nm) of leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew and rust, respectively, were recorded repeatedly during pathogenesis with a spectro-radiometer and analyzed for disease-specific spectral signatures. Calculating the spectral difference and reflectance sensitivity for each wavelength emphasized regions of high interest in the visible and near infrared region of the spectral signatures. The best correlating spectral bands differed depending on the diseases. Spectral vegetation indices related to physiological parameters were calculated and correlated to the severity of diseases. The spectral vegetation indices Normalised Difference Vegetation Index (NDVI), Anthocyanin Reflectance Index (ARI) and modified Chlorophyll Absorption Integral (mCAI) differed in their ability to assess the different diseases at an early stage of disease development, or even before first symptoms became visible. Results suggested that a distinctive differentiation of the three sugar beet diseases using spectral vegetation indices is possible using two or more indices in combination.

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Early Disease Detection in Wheat Fields using Spectral Reflectance

Cited by 239 publications

References 8 publications

Recent advances in sensing plant diseases for precision crop protection

Recent advances in sensing plant diseases for precision crop protection

Spectral reflectance pattern in soybean for assessing yellow mosaic disease

Spectral signatures of sugar beet leaves for the detection and differentiation of diseases

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