Identification method of multi-feature weed based on multi-spectral images and data mining

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering

Volumn 29, Issue 2, 2013, Pages 192-198

  Zhao, Chuanyuan ^a   He, Dongjian ^a   Qiao, Yongliang ^a  

^a NORTHWEST A F UNIVERSITY   (China)

Author keywords

Clustering algorithms; Data mining; Image processing; Multi feature recognition; Multi spectral image; Weed recognition

Indexed keywords

AGRICULTURE AND FORESTRY UNIVERSITIES; C4.5 ALGORITHM; COLOR SPACE; COMPLEX ENVIRONMENTS; CONTOUR LINE; CONTOUR TRACING; DECISION LEVEL FUSION; ERROR BACK PROPAGATION; EUCLIDEAN DISTANCE; FEATURE RECOGNITION; IDENTIFICATION ACCURACY; IDENTIFICATION METHOD; IMAGE-BASED; K-MEANS CLUSTERING ALGORITHM; MULTI FEATURES; MULTI-SPECTRAL; MULTISPECTRAL IMAGES; RANDOM DISTRIBUTION; RANDOM SAMPLE; RECOGNITION RATES; ROBUST ALGORITHM; SHAPE FEATURES; SMALL HOLE; SPECTRAL INFORMATION; STATISTIC ANALYSIS; SVM ALGORITHM; SVM(SUPPORT VECTOR MACHINE); TEST FIELDS; TEST SAMPLES; TEXTURE FEATURES; TRAINING SAMPLE; UNEVEN ILLUMINATIONS; VARIABLE PRECISION; VARIABLE SPRAYING; WEED DETECTION; WEED RECOGNITION;

CLUSTERING ALGORITHMS; COLOR; DATA MINING; DECISION THEORY; DECISION TREES; FORESTRY; FRACTAL DIMENSION; HERBICIDES; MATHEMATICAL MORPHOLOGY; SPECTROSCOPY; SUPPORT VECTOR MACHINES; TEXTURES; VEGETATION; WEED CONTROL;

IMAGE PROCESSING;

ALGORITHMS; COLOR; DATA PROCESSING; DECISION THEORY; FORESTRY; HERBICIDES; IMAGE ANALYSIS; PLANTS; SPECTROSCOPY; TEXTURE; WEED CONTROL;

EID: 84874374021     PISSN: 10026819     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.1002-6819.2013.02.027     Document Type: Article

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