Abstract
Machine learning, especially deep learning (DL), comprises a modern, recent technique to process the images and data, with promising outcomes and enormous potential. DL is acquiring prevalence as it proves its supreme computation power in terms of accuracy when there is a need to train the model with a massive consignment of data. As a subset of machine learning, DL has been effectively applied in different areas with improved accuracy. In the recent past, it has expected to bring a kind of revolution in farming. In this study, three DL models, namely AlexNet, VGG16, and GoogleNet, were implemented to classify the banana crop leaves diseases. The DL models rely on the convolutional neural network (CNN) as an algorithmic learning technique. To train the DL models, CNN used to extract the features automatically from the raw input images. It was observed that fine-tuning of pre-trained networks achieved better classification results than the training from scratch. Moreover, the fine-tuning of hyperparameters increases the accuracy of AlexNet from 0.882 (without fine-tuning) to 0.908 (fine-tuning), VGG16 from 0.896 to 0.9375, and GoogleNet from 0.901 to 0.9531. For visualization of results in the intermediate layers, activation maps (filters) were used to show the internal working of the convolutional layers. These maps also tend to visualize the symptoms and the diseased regions of the leaf image. GoogleNet outperformed the rest two of the models with an accuracy of 95.31%.
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Acknowledgements
Authors are thankful to the Department of Science and Technology, Government of India, Delhi, for funding a project on ‘Application of IoT in Agriculture Sector’ through ICPS division. This work is a part of the project work.
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Sahu, P., Chug, A., Singh, A.P., Singh, D., Singh, R.P. (2022). Classification and Activation Map Visualization of Banana Diseases Using Deep Learning Models. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_61
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DOI: https://doi.org/10.1007/978-981-16-3071-2_61
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