First detection of the presence of naturally occurring grapevine downy mildew in the field by a fluorescence-based method

Latouche, Gwendal; Debord, Christian; Raynal, M.; Milhade, Charlotte; Cerović, Zoran G.

doi:10.1039/c5pp00121h

Cited by 18 publications

(10 citation statements)

References 26 publications

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“…Some bacterial and fungal infections have been studied by this technique (Granum et al, 2015; Pérez-Bueno et al, 2015, 2016). Currently, the use of this technique on proximal sensing is limited to the use of few available devices (Cerovic et al, 2012; Latouche et al, 2015). These devices have proved useful in the assessment of flavonoids content in grapes or kiwifruit, and in the detection of the nitrogen status for several species (Julkunen-Tiitto et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

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Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as “healthy” or “infected.” The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.

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Section: Discussionmentioning

confidence: 99%

“…In the past, MCFI was applied in crop fields for some particular cases, although no systematic analysis of the images could be carried out at that time (Heisel et al, 1996; Johansson et al, 1996; Saito et al, 1998). In later years, advances have been made in its implementation at large scale (Tremblay et al, 2012; Latouche et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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“…The lowest level of phenotyping applications are hand held devices applied by an operator. The Multiplex (Force A, Paris, France), a non-imaging fluorescence sensor was used to assess health status [ 5 , 6 ] or maturity [ 7 , 8 ] in grapevine. Some image-based hand-held prototypes have been used to evaluate yield parameters on grapes still attached to the vine using an artificial background and an RGB camera [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Phenoliner: A New Field Phenotyping Platform for Grapevine Research

Kicherer

Herzog

Bendel

et al. 2017

Sensors

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In grapevine research the acquisition of phenotypic data is largely restricted to the field due to its perennial nature and size. The methodologies used to assess morphological traits and phenology are mainly limited to visual scoring. Some measurements for biotic and abiotic stress, as well as for quality assessments, are done by invasive measures. The new evolving sensor technologies provide the opportunity to perform non-destructive evaluations of phenotypic traits using different field phenotyping platforms. One of the biggest technical challenges for field phenotyping of grapevines are the varying light conditions and the background. In the present study the Phenoliner is presented, which represents a novel type of a robust field phenotyping platform. The vehicle is based on a grape harvester following the concept of a moveable tunnel. The tunnel it is equipped with different sensor systems (RGB and NIR camera system, hyperspectral camera, RTK-GPS, orientation sensor) and an artificial broadband light source. It is independent from external light conditions and in combination with artificial background, the Phenoliner enables standardised acquisition of high-quality, geo-referenced sensor data.

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“…This can make them an indicator for B. graminis . Latouche et al [66] used Multiplex R to successfully identify Plasmopara viticola in vineyards, yet the sensor setup was modified. Time-wise, BGF G , BGF UV , FER RG and ANTH RG were able to identify the fungal infection in the second 10-day period.…”

Section: Multiplexmentioning

confidence: 99%

Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

et al. 2016

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Abstract:The success of precision agriculture relies largely on our ability to identify how the plants' growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the key limitation, in order to decide upon the correct contra-action, e.g., herbicide application. We performed a pot experiment, in which spring wheat was exposed to water shortage, nitrogen deficiency, weed competition (Sinapis alba L.) and fungal infection (Blumeria graminis f. sp. tritici) in a complete, factorial design. A range of sensor measurements were taken every third day from the two-leaf stage until booting of the wheat (BBCH 12 to 40). Already during the first 10 days after stress induction (DAS), both fluorescence measurements and spectral vegetation indices were able to differentiate between non-stressed and stressed wheat plants exposed to water shortage, weed competition or fungal infection. This meant that water shortage and fungal infection could be detected prior to visible symptoms. Nitrogen shortage was detected on the 11-20 DAS. Differentiation of more than one stress factors with the same index was difficult.

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First detection of the presence of naturally occurring grapevine downy mildew in the field by a fluorescence-based method

Cited by 18 publications

References 26 publications

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Phenoliner: A New Field Phenotyping Platform for Grapevine Research

Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

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