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A mobile-based deep learning model for cassava disease diagnosis
Amanda Ramcharan
, Peter McCloskey
, Kelsee Baranowski
, Neema Mbilinyi
, Latifa Mrisho
, Mathias Ndalahwa
, James Legg
,
David P. Hughes
Entomology
Huck Institutes of the Life Sciences
Center for Infectious Disease Dynamics
Plant Institute
Research output
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Contribution to journal
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Article
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peer-review
231
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Keyphrases
Real Image
100%
Disease Diagnosis
100%
F1 Score
100%
Disease Phenotype
100%
Plant Disease
100%
Mobile-based
100%
Convolutional Neural Network
100%
Deep Learning Model
100%
Convolutional Neural Network Model
100%
Image Data
50%
Early Detection
50%
Two-level
50%
Disease Symptoms
50%
Computer Vision
50%
Video Data
50%
Severity Level
50%
Training Data
50%
Model Performance
50%
Mobile Application
50%
Real-world Application
50%
Device Modeling
50%
Symptom Severity
50%
Agricultural Fields
50%
Cassava
50%
Foliar Symptoms
50%
Tanzania
50%
Mobile Devices
50%
Evaluation Model
50%
Specialist Training
50%
Desired Performance
50%
Real-time Video
50%
Mobile Video
50%
Manihot Esculenta Crantz
50%
Disease Categories
50%
Object Detection Algorithm
50%
Mobile Testing
50%
Mobile Image
50%
Computer Science
Deep Learning Model
100%
Convolutional Neural Network
100%
Neural Network Model
50%
Related Performance
25%
World Application
25%
Performance Model
25%
Training Data
25%
Mobile App
25%
Early Detection
25%
Assessment Model
25%
Object Detection
25%
Specialized Training
25%
Computer Vision
25%
Mobile Device
25%
Engineering
Deep Learning Method
100%
Convolutional Neural Network
100%
Network Model
50%
Early Detection
25%
Input Data
25%
Computervision
25%
Real World Application
25%
Mobile App
25%
Mathematics
Convolutional Neural Network
100%
Deep Learning Method
100%
Network Model
50%
Training Data
25%
Input Data
25%
Performance Model
25%