Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine

• Published in: Computers and electronics in agriculture Vol. 80; pp. 89 - 96
• Main Authors: Rumpf, Till, Römer, Christoph, Weis, Martin, Sökefeld, Markus, Gerhards, Roland, Plümer, Lutz
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 2012; Elsevier
• Subjects: Accuracy; Agronomy. Soil science and plant productions; Algorithms; automatic detection; barley; Biological and medical sciences; Cirsium arvense; Classification; developmental stages; Early weed detection; Feature selection; Fundamental and applied biological sciences. Psychology; Galium aparine; Herbicides; image analysis; Liliopsida; Mathematical models; Parasitic plants. Weeds; Phytopathology. Animal pests. Plant and forest protection; Sequential classification; Support vector machines; weed control; Weeds; Support vector machines; Feature selection; Sequential classification; Early weed detection; Cirsium arvense; Galium aparine; Grains; Weed; Support vector machine; Compositae; Discrimination; Sequential; Dicotyledones; Angiospermae; Rubiaceae; Classification; Spermatophyta; Species

Site-specific weed management can reduce the amount of herbicides used in comparison to classical broadcast applications. The ability to apply herbicides on weed patches within the field requires automation. This study focuses on the automatic detection of different species with imaging sensors. Ima...