Mobile-based Deep Learning Models for Banana Disease Detection

  • S. L. Sanga Nelson Mandela African Institute of Science and Technology, Tanzania
  • D. Machuve Nelson Mandela African Institute of Science and Technology, Tanzania
  • K. Jomanga International Institute of Tropical Agriculture, Tanzania

Abstract

In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high accuracies, varying from 95.41% for InceptionV3 to 99.2% for Resnet152. InceptionV3 was selected for mobile deployment because it demands much less memory. The developed tool was capable of detecting diseases with a confidence of 99% of the captured leaves from the real environment. This tool will help smallholder farmers conduct early detection of banana diseases and improve their productivity.

Keywords: deep learning, banana diseases, smartphones, early detection, android based application

Downloads

Download data is not yet available.

References

A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis, “Deep learning for computer vision : A brief review”, Computational Intelligence and Neuroscience, Vol. 2018, Article ID 7068349, 2018 DOI: https://doi.org/10.1155/2018/7068349

W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F. E. Alsaadi, “A survey of deep neural network architectures and their applications”, Neurocomputing, Vol. 234, pp. 11-26, 2017 DOI: https://doi.org/10.1016/j.neucom.2016.12.038

N. E. A. Amrani, O. E. K. Abra, M. Youssfi, O. Bouattane, “A novel deep learning approach for semantic interoperability between heterogeneous multi-agent systems”, Engineering, Technology & Applied Science Research, Vol. 9, No. 4, pp. 4566–4573, 2019 DOI: https://doi.org/10.48084/etasr.2841

H. A. Pierson, M. S. Gashler, “Deep learning in robotics: A review of recent research”, Advanced Robotics, Vol. 31, No. 16, pp. 821–835, 2017 DOI: https://doi.org/10.1080/01691864.2017.1365009

S. P. Mohanty, D. P. Hughes, M. Salathe, “Using deep learning for image-based plant disease detection”, Frontiers in Plant Science, Vol. 7, Article ID 1419, 2016 DOI: https://doi.org/10.3389/fpls.2016.01419

Y. Deng, “Deep learning on mobile devices: A review”, Proceedings of the SPIE, Vol. 10993, Article ID 109930A, 2019 DOI: https://doi.org/10.1117/12.2518469

K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis”, Computers and Electronics in Agriculture, Vol. 145, pp. 311–318, 2018 DOI: https://doi.org/10.1016/j.compag.2018.01.009

K. Ramadhani, D. Machuve, K. Jomanga, “Identification and analysis of factors in management of banana fungal diseases: Case of Sigatoka (mycosphaerella fijiensis. mulder) and fusarium (fusarium oxysporum f. sp. cubense (Foc) diseases in arumeru district”, Journal of Biodiversity and Environmental Sciences, Vol. 11, No. 1, pp. 69–75, 2017

A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, D. Hughes, “Using transfer learning for image-based cassava disease detection”, Frontiers in Plant Science, Vol. 8, Article ID 1852, 2017 DOI: https://doi.org/10.3389/fpls.2017.01852

N. C. Eli-Chukwu, “Applications of artificial intelligence in agriculture : A review”, Engineering, Technology & Applied Science Research, Vol. 9, No. 4, pp. 4377-4383, 2019 DOI: https://doi.org/10.48084/etasr.2756

H. A. Hiary, S. B. Ahmad, M. Reyalat, M. Braik, Z. ALRahamneh, “Fast and accurate detection and classification of plant diseases”, International Journal of Computer Applications, Vol. 17, No. 1, pp. 31–38, 2011 DOI: https://doi.org/10.5120/2183-2754

C. Szegedy, S. Ioffe, V. Vanhoucke, A. A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning”, Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, USA, February 4-9, 2017

C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, C. Liu, “A survey on deep transfer learning”, in: Artificial Neural Networks and Machine Learning-ICANN 2018, Vol. 11141, Springer, 2018 DOI: https://doi.org/10.1007/978-3-030-01424-7_27

K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, A. Agrawal, “Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection”, Construction and Building Materials, Vol. 157, pp. 322–330, 2017 DOI: https://doi.org/10.1016/j.conbuildmat.2017.09.110

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, F. F. Li, “Large-scale video classification with convolutional neural networks”, IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, June 23-28, 2014 DOI: https://doi.org/10.1109/CVPR.2014.223

J. Shijie, J. Peiyi, H. Siping, S. Haibo, “Automatic detection of tomato diseases and pests based on leaf images”, Chinese Automation Congress, Jinan, China, October 20-22, 2017 DOI: https://doi.org/10.1109/CAC.2017.8243388

K. Divya, S. V. Krishnakumar, “Comparative analysis of smart phone operating systems Android, Apple IOS and Windows”, International Journal of Scientific Engineering and Applied Science, Vol. 2, No. 2, pp. 432-438, 2016

K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition”, IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, June 27-30, 2016 DOI: https://doi.org/10.1109/CVPR.2016.90

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, “TensorFlow : A system for large-scale machine learning”, 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, USA, November 2-4, 2016

C. Meng, M. Sun, J. Yang, M. Qiu, Y. Gu, “Training deeper models by GPU memory optimization on TensorFlow”, 31st Conference on Neural Information Processing Systems, Long Beach, USA, December 4-9, 2017

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous distributed systems”, available at: https://arxiv.org/pdf/1603.04467.pdf, 2016

Metrics

Abstract Views: 295
PDF Downloads: 186

Metrics Information
Bookmark and Share

Most read articles by the same author(s)