T<scp>HE</scp>P<scp>OTENTIAL OF</scp>O<scp>PTICAL</scp>C<scp>ANOPY</scp>M<scp>EASUREMENT FOR</scp>T<scp>ARGETED</scp>C<scp>ONTROL OF</scp>F<scp>IELD</scp>C<scp>ROP</scp>D<scp>ISEASES</scp>

West, Jonathan; Bravo, C.; Oberti, R.; Lemaire, D.; Moshou, Dimitrios; McCartney, H. A.

doi:10.1146/annurev.phyto.41.121702.103726

Cited by 258 publications

(55 citation statements)

References 63 publications

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“…2), which is in agreement with previous findings of Franke et al (2005) for leaf rust detection by means of reflection technique. Healthy green leaves are usually characterised by a low red reflectance due to the strong absorption in this region by chlorophyll molecules and by a high reflection of NIR caused by multiple scattering at the air-cell interfaces in the internal leaf tissues (West et al 2003). The observed decrease of NDVI during development of both pathogens in our experiment was due to the increase of red reflectance at constant NIR values.…”

Section: Discussionsupporting

confidence: 47%

Temporal and spatial changes of chlorophyll fluorescence as a basis for early and precise detection of leaf rust and powdery mildew infections in wheat leaves

2008

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Temporal and spatial changes in parameters of fast chlorophyll fluorescence kinetics (ground fluorescence, Fo and maximal fluorescence, Fm) and red/NIR reflectance were assessed with a Pulse-Amplitude-Modulated (PAM)-Imaging system on a daily basis over a period of 2 weeks following inoculation of wheat leaves with powdery mildew and leaf rust. The early detection of these infections by means of fluorescence imaging was possible 2-3 days before visual symptoms or significant changes in normalised-differenced-vegetation index (NDVI) became apparent. The initial infection of both fungi caused an increase in Fo and decrease in photochemical efficiency (Fv/Fm, Fv/Fo). The appearance and development of fungal pustules was accompanied by reduction in Fo and Fm. This resulted mainly from lower absorption of fluorescence exciting light by the leaf mesophyll due to the shielding effect of fungal mycelium, and to lesser extent from the chlorophyll breakdown underneath pustules. Among the evaluated fluorescence parameters, Fv/Fo displayed the most pronounced response to both kinds of infection. Mildew infection influenced chlorophyll fluorescence neither in the direct vicinity of mycelium nor in the apparently healthy leaf regions. Rust infected plants, in contrast, displayed significantly reduced photochemical efficiency Fv/Fm and Fv/Fo in chlorotic tissue around pustules. The same, but less pronounced tendency was found in the apparently healthy regions of rust infected leaves in the last days of the experiment. Dark adaptation of leaves proved to be necessary for accurate detection of both pathogen infections by means of fluorescence imaging. Additional experiments are needed to estimate the potential of this technique for remote sensing under field conditions.

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Section: Discussionsupporting

confidence: 47%

Temporal and spatial changes of chlorophyll fluorescence as a basis for early and precise detection of leaf rust and powdery mildew infections in wheat leaves

2008

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“…Remote sensing provides the ability for relatively rapid and early detection of the spatial distribution of a disease using methods based on thermography, chlorophyll fluorescence and canopy reflectance [14][15][16][17][18][19][20][21][22][23]. High resolution spectroscopic sensing has the ability to identify subtle changes in crop biophysical conditions that are unique to a specific disease or stressor.…”

Section: Introductionmentioning

confidence: 99%

Soybean Disease Monitoring with Leaf Reflectance

Bajwa

Rupe

Mason³

2017

Remote Sensing

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Crop disease detection with remote sensing is a challenging area that can have significant economic and environmental impact on crop disease management. Spectroscopic remote sensing in the visible and near-infrared (NIR) region has the potential to detect crop changes due to diseases. Soybean cyst nematode (SCN) and sudden death syndrome (SDS) are two common soybean diseases that are extremely difficult to detect in the early stages under mild to moderate infestation levels. The objective of this research study was to relate leaf reflectance to disease conditions and to identify wavebands that best discriminated these crop diseases. A microplot experiment was conducted. Data collected included 800 leaf spectra, corresponding leaf chlorophyll content and disease rating of four soybean cultivars grown under different disease conditions. Disease conditions were created by introducing four disease treatments of control (no disease), SCN, SDS, and SCN+SDS. Crop data were collected on a weekly basis over a 10-week period, starting from 71 days after planting (DAP). The correlation between disease rating and selected vegetation indices (VI) were evaluated. Wavebands with the most disease discrimination capability were identified with stepwise linear discriminant analysis (LDA), logistic discriminant analysis (LgDA) and linear correlation analysis of pooled data. The identified band combinations were used to develop a classification function to identify plant disease condition. The best correlation (>0.8) between disease rating and VI occurred during 112 DAP. Both LDA and LgDA identified several bands in the NIR, red, green and blue regions as critical for disease discrimination. The discriminant models were able to detect over 80% of the healthy plants accurately under cross-validation but showed poor accuracy in discriminating individual diseases. A two-class discriminant model was able to identify 97% of the healthy plants and 58% of the infested plants as having some disease from the plant spectra.

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“…The use of large amounts of fungicide can effectively control most crop diseases and decrease the crop production loss. However, pesticide increases costs to growers while environmental problems may be incurred to soil, water, air and ecological systems (West et al, 2003). A proper on-time characterization and assessment of disease distribution and intensity could provide useful information for decision making about the timing of fungicide application in precise pest management.…”

Section: Introductionmentioning

confidence: 99%

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

Liu

Huang

Shi

et al. 2007

J. Zhejiang Univ. - Sci. B

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Abstract:Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

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THEPOTENTIAL OFOPTICALCANOPYMEASUREMENT FORTARGETEDCONTROL OFFIELDCROPDISEASES

Cited by 258 publications

References 63 publications

Temporal and spatial changes of chlorophyll fluorescence as a basis for early and precise detection of leaf rust and powdery mildew infections in wheat leaves

Temporal and spatial changes of chlorophyll fluorescence as a basis for early and precise detection of leaf rust and powdery mildew infections in wheat leaves

Soybean Disease Monitoring with Leaf Reflectance

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

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