Classification of olive leaf diseases using deep convolutional neural networks

In recent years, there have been significant achievements in object classification with various techniques using several deep learning architectures. These architectures are now also used for classification and detection of many plant diseases. Olives are important plant species which are grown in c...

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Vydané v:Neural computing & applications Ročník 33; číslo 9; s. 4133 - 4149
Hlavní autori: Uğuz, Sinan, Uysal, Nese
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.05.2021
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Artificial neural networks
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data augmentation
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Machine learning
Olives
Optimization
Optimization algorithms
Original Article
Plant diseases
Probability and Statistics in Computer Science
Deep learning
Data augmentation
Convolutional neural networks
Transfer learning
Olive plant disease
ISSN:0941-0643, 1433-3058
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Shrnutí:In recent years, there have been significant achievements in object classification with various techniques using several deep learning architectures. These architectures are now also used for classification and detection of many plant diseases. Olives are important plant species which are grown in certain regions of the world. The disease types that affect the olive plants vary on the region where it is grown. This study presents a data set consisting of 3400 olive leaves samples which also includes healthy leaves so as to detect Aculus olearius and Olive peacock spot diseases, which are common olive plant diseases in Turkey. This experimental study used transfer learning methods on VGG16 and VGG19 architectures, as well as on our proposed CNN architecture. Effects of data augmentation on performance were one aim of this research. In the experimental studies which applied data augmentation the highest success value in trained models was 95%, whereas in the experiments without data augmentation the highest value was 88%. Another subject of this research is the Adam, AdaGrad, Stochastic gradient descent and RMS Prop optimization algorithms’ effect on the network’s performance. As a result of the conducted experiments, Adam and SGD optimization algorithms were generally observed to generate superior results.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05235-5