Literature DB >> 27618193

Phenomic Approaches and Tools for Phytopathologists.

Ivan Simko1, Jose A Jimenez-Berni1, Xavier R R Sirault1.   

Abstract

Plant phenomics approaches aim to measure traits such as growth, performance, and composition of plants using a suite of noninvasive technologies. The goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and genome. Application of sensing technologies for detecting specific phenotypic reactions occurring during plant-pathogen interaction offers new opportunities for elucidating the physiological mechanisms that link pathogen infection and disease symptoms in the host, and also provides a faster approach in the selection of genetic material that is resistant to specific pathogens or strains. Appropriate phenomics methods and tools may also allow presymptomatic detection of disease-related changes in plants or to identify changes that are not visually apparent. This review focuses on the use of sensor-based phenomics tools in plant pathology such as those related to digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging. A brief introduction is provided for less used approaches like magnetic resonance, soft x-ray imaging, ultrasound, and detection of volatile compounds. We hope that this concise review will stimulate further development and use of tools for automatic, nondestructive, and high-throughput phenotyping of plant-pathogen interaction.

Entities:  

Mesh:

Year:  2016        PMID: 27618193     DOI: 10.1094/PHYTO-02-16-0082-RVW

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  11 in total

1.  Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors.

Authors:  Ivan Simko; Ryan J Hayes; Robert T Furbank
Journal:  Front Plant Sci       Date:  2016-12-27       Impact factor: 5.753

2.  In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR.

Authors:  Shangpeng Sun; Changying Li; Andrew H Paterson; Yu Jiang; Rui Xu; Jon S Robertson; John L Snider; Peng W Chee
Journal:  Front Plant Sci       Date:  2018-01-22       Impact factor: 5.753

Review 3.  Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

Authors:  Fernando Perez-Sanz; Pedro J Navarro; Marcos Egea-Cortines
Journal:  Gigascience       Date:  2017-11-01       Impact factor: 6.524

Review 4.  Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.

Authors:  Keiichi Mochida; Satoru Koda; Komaki Inoue; Takashi Hirayama; Shojiro Tanaka; Ryuei Nishii; Farid Melgani
Journal:  Gigascience       Date:  2019-01-01       Impact factor: 6.524

5.  Phenomic and Physiological Analysis of Salinity Effects on Lettuce.

Authors:  Neil D Adhikari; Ivan Simko; Beiquan Mou
Journal:  Sensors (Basel)       Date:  2019-11-05       Impact factor: 3.576

6.  Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves.

Authors:  Mirko Pavicic; Kirk Overmyer; Attiq Ur Rehman; Piet Jones; Daniel Jacobson; Kristiina Himanen
Journal:  Plants (Basel)       Date:  2021-01-15

7.  UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat.

Authors:  Sara Francesconi; Antoine Harfouche; Mauro Maesano; Giorgio Mariano Balestra
Journal:  Front Plant Sci       Date:  2021-04-01       Impact factor: 5.753

Review 8.  Sensor-based phenotyping of above-ground plant-pathogen interactions.

Authors:  Florian Tanner; Sebastian Tonn; Jos de Wit; Guido Van den Ackerveken; Bettina Berger; Darren Plett
Journal:  Plant Methods       Date:  2022-03-21       Impact factor: 5.827

9.  Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection.

Authors:  Jan Behmann; Kelvin Acebron; Dzhaner Emin; Simon Bennertz; Shizue Matsubara; Stefan Thomas; David Bohnenkamp; Matheus T Kuska; Jouni Jussila; Harri Salo; Anne-Katrin Mahlein; Uwe Rascher
Journal:  Sensors (Basel)       Date:  2018-02-02       Impact factor: 3.576

10.  Surveillance of panicle positions by unmanned aerial vehicle to reveal morphological features of rice.

Authors:  Daisuke Ogawa; Toshihiro Sakamoto; Hiroshi Tsunematsu; Toshio Yamamoto; Noriko Kanno; Yasunori Nonoue; Jun-Ichi Yonemaru
Journal:  PLoS One       Date:  2019-10-31       Impact factor: 3.240

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