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Paddy crop and weed classification using color features for computer vision based precision agriculture
Radhika Kamath
*
,
Mamatha Balachanra
,
Srikanth Prabhu
*
Corresponding author for this work
School of Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
9
Citations (Scopus)
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INIS
computers
100%
vision
100%
classification
100%
precision
100%
color
100%
weeds
100%
crops
100%
agriculture
100%
images
62%
datasets
50%
grass
25%
least square fit
12%
texture
12%
shape
12%
detection
12%
accuracy
12%
soils
12%
vectors
12%
randomness
12%
forests
12%
cameras
12%
natural lighting
12%
Agricultural and Biological Sciences
Precision Agriculture
100%
Paddies
100%
Computer Vision
100%
Cyperaceae
40%
Least Square
20%
Support Vector Machine
20%
Biochemistry, Genetics and Molecular Biology
Cyperaceae
100%
Support Vector Machine
50%
Random Forest
50%
K Nearest Neighbor
50%