Abstract
Recently, many researchers have been inspired by the success of deep learning in computer vision to improve the performance of detection systems for plant diseases. Unfortunately, most of these studies did not leverage recent deep architectures and were based essentially on AlexNet, GoogleNet or similar architectures. Moreover, the research did not take advantage of deep learning visualisation methods which qualifies these deep classifiers as black boxes as they are not transparent. In this chapter, we have tested multiple state-of-the-art Convolutional Neural Network (CNN) architectures using three learning strategies on a public dataset for plant diseases classification. These new architectures outperform the state-of-the-art results of plant diseases classification with an accuracy reaching 99.76%. Furthermore, we have proposed the use of saliency maps as a visualisation method to understand and interpret the CNN classification mechanism. This visualisation method increases the transparency of deep learning models and gives more insight into the symptoms of plant diseases.
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Notes
- 1.
Images are randomly cropped to be 299 \(*\) 299 for Inception v3 architecture and 224 \(*\) 224 for (AlexNet, DenseNet-169, ResNet-34, SqueezeNet-1.1 and VGG13).
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Brahimi, M., Arsenovic, M., Laraba, S., Sladojevic, S., Boukhalfa, K., Moussaoui, A. (2018). Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_6
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