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Automated identification of sugar beet diseases using smartphones
L. Hallau
, M. Neumann
, B. Klatt
, B. Kleinhenz
, T. Klein
, C. Kuhn
, M. Röhrig
, C. Bauckhage
, K. Kersting
, A. K. Mahlein
, U. Steiner
, E. C. Oerke
Department of Computer Science & Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
54
Scopus citations
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Keyphrases
Smartphone
100%
Automated Identification
100%
Sugar Beet
100%
Cercospora Leaf Spot
100%
Leaf Disease
60%
Support Vector Machine
20%
Rapid Identification
20%
Image Acquisition
20%
Classification Accuracy
20%
Molecular Methods
20%
Abiotic Stress
20%
Image Segmentation
20%
Accurate Identification
20%
Disease Control
20%
Texture Features
20%
Intensity Value
20%
Infected Tissue
20%
Serological Methods
20%
Multi-class Classification
20%
Visual Symptoms
20%
Automated Detection
20%
Smartphone Camera
20%
Automated Classification
20%
RGB Image
20%
Bacterial Blight
20%
Foliar Pathogen
20%
Integrated Management
20%
Image Database
20%
RBF Kernel
20%
Economic Risk
20%
Color Intensity
20%
Gradient Value
20%
Beet
20%
Color Value
20%
Spot Identification
20%
Ramularia Leaf Spot
20%
Color Gradient
20%
Image Data Processing
20%
Integrated Disease Control
20%
Sugar Beet Leaves
20%
Binary Class
20%
Sugar Beet Yield
20%
Phoma Leaf Spot
20%
Agricultural and Biological Sciences
Sugar Beet
100%
Leaf Spot
100%
Cercospora
71%
Blight
14%
Support Vector Machine
14%
Decision Making
14%
Data Processing
14%
Phoma
14%
Ramularia
14%
Abiotic Stress
14%