Classification of olive leaf diseases using deep convolutional neural networks

• Published in: Neural computing & applications Vol. 33; no. 9; pp. 4133 - 4149
• Main Authors: Uğuz, Sinan, Uysal, Nese
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2021; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s00521-020-05235-5

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.