Treffer: Discrimination between organically and conventionally grown winter wheat farm pair samples using the copper chloride crystallisation method in combination with computerised image analysis
Title:
Discrimination between organically and conventionally grown winter wheat farm pair samples using the copper chloride crystallisation method in combination with computerised image analysis
Authors:
Source:
Computers and electronics in agriculture. 74(2):218-222
Publisher Information:
Amsterdam: Elsevier, 2010.
Publication Year:
2010
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Agronomy, agriculture, phytopathology, Agronomie, agriculture, phytopathologie, Electronics, Electronique, Computer science, Informatique, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Agronomie. Sciences du sol et productions vegetales, Agronomy. Soil science and plant productions, Agronomie générale. Phytotechnie, General agronomy. Plant production, Généralités. Systèmes agraires et d'exploitation. Développement agricole, Generalities. Agricultural and farming systems. Agricultural development, Agroécologie générale. Systèmes agraires et d'exploitation. Développement agricole. Aménagement rural. Paysagisme, General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping, Systèmes agraires et d'exploitation, Agricultural and farming systems, Angiospermae, Gramineae, Monocotyledones, Plante céréalière, Cereal crop, Planta cerealista, Spermatophyta, Agriculture biologique, Organic agriculture, Agricultura biológica, Agriculture conventionnelle, Conventional agriculture, Agricultura convencional, Analyse image, Image analysis, Análisis imagen, Analyse statistique, Statistical analysis, Análisis estadístico, Chlorure de cuivre, Copper chloride, Cobre cloruro, Cristallisation, Crystallization, Cristalización, Discrimination, Discriminación, Echantillon, Sample, Muestra, Image numérique, Digital image, Imagen numérica, Système agraire, Agricultural system, Sistema agrario, Traitement informatique, Computerized processing, Tratamiento informático, Triticum aestivum, Culture d'hiver, Winter crops, Cultivos de invierno, Copper chloride crystallisation, Wheat
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Organic Food Quality and Food Culture, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany
Department of Healthcare and Nutrition, Louis Bolk Instituut, 3972 LA Driebergen, Netherlands
Department of Healthcare and Nutrition, Louis Bolk Instituut, 3972 LA Driebergen, Netherlands
ISSN:
0168-1699
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
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
Accession Number:
edscal.23382557
Database:
PASCAL Archive
Weitere Informationen
Organic and conventional winter wheat farm pair grain samples were tested with the copper chloride crystallisation method and submitted to computerised image analyses followed by pattern recognition and classification with multivariate statistical tools. Appropriate discriminant analyses (DA) models were established. Depending on the analysed region of interest up to 100% of unknown samples could be correctly predicted using the DA models.