Result: DART: a software to analyse root system architecture and development from captured images

Title:
DART: a software to analyse root system architecture and development from captured images
Authors:
LE BOT, Jacques, SERRA, Valérie, FABRE, José, DRAYE, Xavier, ADAMOWICZ, Stéphane, PAGES, Loïc
Source:
Plant and soil. 326(1-2):261-273
Publisher Information:
Dordrecht: Springer, 2010.
Publication Year:
2010
Physical Description:
print, 1 p
Original Material:
INIST-CNRS
Subject Terms:
Agronomy, agriculture, phytopathology, Agronomie, agriculture, phytopathologie, Plant biology and physiology, Biologie et physiologie végétales, Ecology, Ecologie, Soil science, pedology, Science du sol, pédologie, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Ecologie animale, vegetale et microbienne, Animal, plant and microbial ecology, Agronomie. Sciences du sol et productions vegetales, Agronomy. Soil science and plant productions, Agronomie générale. Phytotechnie, General agronomy. Plant production, Relations sol-plante. Fertilité des sols. Fertilisation. Amendement, Soil-plant relationships. Soil fertility. Fertilization. Amendments, Relations sol-plante. Fertilité des sols, Soil-plant relationships. Soil fertility, Appareil végétatif, Vegetative apparatus, Sistema vegetativo, Dimension, Dimensión, Analyse, Analysis, Análisis, Caractéristique dynamique, Dynamic characteristic, Característica dinámica, Densité, Density, Densidad, Développement, Development, Desarrollo, Image, Imagen, Logiciel, Software, Logicial, Longueur, Length, Longitud, Orienté objet, Object oriented, Orientado objeto, Programme ordinateur, Computer program, Programa informático, Racine, Root, Raíz, Ramification, Branching, Ramificación, Rhizotron, 2D dynamic analysis, Branching density, Digitalization, Java, Object-oriented computer program
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
INRA, UR 1115 Plantes et Systèmes de culture Horticoles, 84000 Avignon, France
Unité d'écophysiologie et d'amélioration végétale (AGRO/BAPA/ECAV), Faculté d'ingénierie biologique, agronomique et environnementale, Université catholique de Louvain, Place Croix du Sud 2-11, 1348 Louvain-la-Neuve, Belgium
ISSN:
0032-079X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=22379838
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Agronomy. Soil sciences and vegetal productions

Animal, vegetal and microbial ecology
Accession Number:
edscal.22379838
Database:
PASCAL Archive

Further Information

Image analysis is used in numerous studies of root system architecture (RSA). To date, fully automatic procedures have not been good enough to completely replace alternative manual methods. DART (Data Analysis of Root Tracings) is freeware based on human vision to identify roots, particularly across time-series. Each root is described by a series of ordered links encapsulating specific information and is connected to other roots. The population of links constitutes the RSA. DART creates a comprehensive dataset ready for individual or global analyses and this can display root growth sequences along time. We exemplify here individual tomato root growth response to shortfall in solar radiation and we analyse the global distribution of the inter-root branching distances. DART helps in studying RSA and in producing structured and flexible datasets of individual root growth parameters. It is written in JAVA and relies on manual procedures to minimize the risks of errors and biases in datasets.