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Plant Disease Detection and Classification by Deep Learning
by
Arif, Khalid Mahmood
, Potgieter, Johan
, Saleem, Muhammad Hammad
in
Accuracy
/ Algorithms
/ artificial intelligence
/ Business metrics
/ Classification
/ convolutional neural networks (cnn)
/ Datasets
/ Deep learning
/ Disease detection
/ Learning algorithms
/ Machine learning
/ Performance measurement
/ plant disease
/ Plant diseases
/ plant diseases and disorders
/ Review
/ Sensors
/ Signs and symptoms
/ Support vector machines
/ Visualization
2019
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Plant Disease Detection and Classification by Deep Learning
by
Arif, Khalid Mahmood
, Potgieter, Johan
, Saleem, Muhammad Hammad
in
Accuracy
/ Algorithms
/ artificial intelligence
/ Business metrics
/ Classification
/ convolutional neural networks (cnn)
/ Datasets
/ Deep learning
/ Disease detection
/ Learning algorithms
/ Machine learning
/ Performance measurement
/ plant disease
/ Plant diseases
/ plant diseases and disorders
/ Review
/ Sensors
/ Signs and symptoms
/ Support vector machines
/ Visualization
2019
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Do you wish to request the book?
Plant Disease Detection and Classification by Deep Learning
by
Arif, Khalid Mahmood
, Potgieter, Johan
, Saleem, Muhammad Hammad
in
Accuracy
/ Algorithms
/ artificial intelligence
/ Business metrics
/ Classification
/ convolutional neural networks (cnn)
/ Datasets
/ Deep learning
/ Disease detection
/ Learning algorithms
/ Machine learning
/ Performance measurement
/ plant disease
/ Plant diseases
/ plant diseases and disorders
/ Review
/ Sensors
/ Signs and symptoms
/ Support vector machines
/ Visualization
2019
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Plant Disease Detection and Classification by Deep Learning
Journal Article
Plant Disease Detection and Classification by Deep Learning
2019
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Overview
Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.
Publisher
MDPI AG,MDPI
Subject
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