Spatial and Temporal Dynamics of Plant Pathogens

Nutter, Forrest W.; Rij, Neil van; Eggenberger, Sharon K.; Holah, Noha

doi:10.1007/978-90-481-9277-9_3

Cited by 24 publications

(19 citation statements)

References 37 publications

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“…changes in chlorophyll, xanthophyll, and water content, among others), caused by parasites on the plants and in the canopy. The predictions on the spread of the bark beetle as a result of direct and indirect detection of tree damage (Lausch et al, 2013;Fassnacht et al, 2014) as well as pestprotected plants (Nutter et al, 2010) are only possible using spatial and spectral high resolution RS data. There are also many applications in epidemiology that use EO for an indirect quantification of the distribution of mosquitos (Herbreteau et al, 2007).…”

Section: Detecting Animal Species Using Eomentioning

confidence: 99%

Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Lausch

Bannehr

Beckmann

et al. 2016

Ecological Indicators

147

145

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Section: Detecting Animal Species Using Eomentioning

confidence: 99%

Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Lausch

Bannehr

Beckmann

et al. 2016

Ecological Indicators

147

145

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“…Plant phenotyping is also critical to understanding plant phenotype behavior in response to genetic and environmental factors [73,74]. Nevertheless, the use of spectral analysis is a matter of debate in plant pathology, due to similar symptoms or physiologic alteration caused by different diseases [75]. Pathogens detected by image analysis is rapidly increasing [72], mostly involving widespread pathogens frequently recurrent in arable agriculture systems [76].…”

Section: Diagnostic Specificity: the Challenge Of Microorganism Controlmentioning

confidence: 99%

iPathology: Robotic Applications and Management of Plants and Plant Diseases

2017

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The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.

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“…8 It should also be recalled that numerically plot severity = incidence × leaf severity. Although the measurements were performed on a single row, in this work we use the term 'plot severity' by convention.…”

Section: Visual Disease Assessmentmentioning

confidence: 99%

“…8 In addition, a sensitive automatic mapping of diseases is needed for precision pest management. However, the accuracy and precision of visual disease assessments performed by different raters continues to be called into question.…”

Section: Proximal Sensing Of Diseasesmentioning

confidence: 99%

“…However, the accuracy and precision of visual disease assessments performed by different raters continues to be called into question. 8,18 Fluorescence, although technically more demanding than reflectance, is a far more sensitive technique. 17 Indeed, until now the successful reflectance-based remote sensing of diseases was limited to the changes in green biomass due to defoliation.…”

Section: Proximal Sensing Of Diseasesmentioning

confidence: 99%

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

Latouche

Debord

Raynal

et al. 2015

Photochem Photobiol Sci

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Early detection of fungal pathogen presence in the field would help to better time or avoid some of the fungicide treatments used to prevent crop production losses. We recently introduced a new phytoalexin-based method for a non-invasive detection of crop diseases using their fluorescence. The causal agent of grapevine downy mildew, Plasmopara viticola, induces the synthesis of stilbenoid phytoalexins by the host, Vitis vinifera, early upon infection. These stilbenoids emit violet-blue fluorescence under UV light. A hand-held solid-state UV-LED-based field fluorimeter, named Multiplex 330, was used to measure stilbenoid phytoalexins in a vineyard. It allowed us to non-destructively detect and monitor the naturally occurring downy mildew infections on leaves in the field.

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Spatial and Temporal Dynamics of Plant Pathogens

Cited by 24 publications

References 37 publications

Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

iPathology: Robotic Applications and Management of Plants and Plant Diseases

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

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