Treffer: Colour and shape analysis techniques for weed detection in cereal fields

Title:
Colour and shape analysis techniques for weed detection in cereal fields
Authors:
PEREZ, A. J, LOPEZ, F, BENLLOCH, J. V, CHRISTENSEN, S
Source:
The First European Conference for Information Technology in AgricultureComputers and electronics in agriculture. 25(3):197-212
Publisher Information:
Amsterdam: Elsevier, 2000.
Publication Year:
2000
Physical Description:
print, 22 ref
Original Material:
INIST-CNRS
Subject Terms:
Agronomy, agriculture, phytopathology, Agronomie, agriculture, phytopathologie, Electronics, Electronique, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Sciences de l'information. Documentation, Information science. Documentation, Technologie de la communication et de l'information, Information and communication technologies, Technologies de l'information: supports, équipements, Information technologies: storage media, equipment, Applications (par exemple: numérisation,...), Applications (e.g. Digitizing,...), Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Phytopathologie. Zoologie agricole. Protection des cultures et des forets, Phytopathology. Animal pests. Plant and forest protection, Plantes parasites. Mauvaises herbes, Parasitic plants. Weeds, Malherbologie, Weeds, Généralités, botanique, écologie, dégâts, importance économique, Generalities, botany, ecology, damages, economic importance, Sciences de l'information et de la communication, Information and communication sciences, Assistance ordinateur, Computer aid, Asistencia ordenador, Technologie information, Information technology, Tecnología información, Traitement informatique, Computerized processing, Tratamiento informático, Algorithme, Algorithm, Algoritmo, Analyse forme, Pattern analysis, Análisis forma, Champ cultivé, Cultivated field, Campo cultivado, Désherbage chimique, Chemical weed control, Deshierba química, Image couleur, Color image, Imagen color, Malherbologie, Weed science, Ciencia malas hierbas, Mauvaise herbe, Weed, Malezas, Mesure au niveau sol, Ground-level measurement, Medida a nivel tierra, Plante céréalière, Cereal crop, Planta cerealista, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Traitement image, Image processing, Procesamiento imagen, Vision ordinateur, Computer vision, Visión ordenador
Subject Geographic:
Europe, Europa, Pays Scandinaves, Scandinavia, Países escandinavos, Danemark, Denmark, Dinamarca
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Departamento de Informática de Sistemas y Computadores, Universidad Politécnica de Valencia, P.O. Box 22012, 46071 Valencia, Spain
Department of Plant Protection, Danish Institute of Agricultural Sciences, Slagelse, Denmark
ISSN:
0168-1699
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=1386838
Rights:
Copyright 2000 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:
Phytopathology. Agricultural zoology. Crops and forests protection

Sciences of information and communication. Documentation

FRANCIS
Accession Number:
edscal.1386838
Database:
PASCAL Archive

Weitere Informationen

Information on weed distribution within the field is necessary to implement spatially variable herbicide application. This paper deals with the development of near-ground image capture and processing techniques in order to detect broad-leaved weeds in cereal crops under actual field conditions. The proposed methods use colour information to discriminate between vegetation and background, whilst shape analysis techniques are applied to distinguish between crop and weeds. The determination of crop row position helps to reduce the number of objects to which shape analysis techniques are applied. The performance of algorithms was assessed by comparing the results with a human classification, providing an acceptable success rate. The study has shown that despite the difficulties in accurately determining the number of seedlings (as in visual surveys), it is feasible to use image processing techniques to estimate the relative leaf area of weeds (weed leaf area/total leaf area of crop and weeds) while moving across the field and use these data in a stratified manual weed survey of the field.