Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach

Abstract:

Bacterial Wilt disease is the most determinant factor as it results in a serious reduction in the quality and quantity of food produced by Enset crop. Therefore, early detection of Bacterial Wilt disease is important to diagnose and fight the disease. To this end, a deep learning approach that can detect the disease by using healthy and infected leave images of the crop is proposed. In particular, a convolutional neural network architecture is designed to classify the images collected from different farms as diseased or healthy. A total of 4896 images that were captured directly from the farm with the help of experts in the field of agriculture was used to train the proposed model. The proposed model was trained using these images and data augmentation techniques was applied to generate more images. Besides training the proposed model, a pre-trained model namely VGG16 is trained by using our dataset. The proposed model achieved a mean accuracy of 98.5% and the VGG16 pre-trained model achieved a mean accuracy of 96.6% by using a mini-batch size of 32 and a learning rate of 0.001. The preliminary results demonstrated that the effectiveness of the proposed approach under challenging conditions such as illumination, complex background, different resolutions, variable scale, rotation, and orientation of the real scene images.

• Periodical: International Journal of Engineering Research in Africa (Volume 51)
• Pages: 131-146
• DOI: https://doi.org/10.4028/www.scientific.net/JERA.51.131
• Online since: November 2020
• Authors: Yidnekachew Kibru Afework, Taye Girma Debelee*
• Keywords: Bacterial Wilt, Convolutional Neural Network, Disease Detection, Enset Crop, Image Classification

References

[1] C. S. Agency, Agricultural in figures key findings of 2008/09-2010/11 agricultural samples survey for all sectors and seasons,, pp.21-30, (2012).

[2] Z. Yemataw, A. Mekonen, A. Chala, K. Tesfaye, K. Mekonen, D. Studholme, and K. Sharma, Farmers' knowledge and perception of enset xanthomonas wilt in southern ethiopia,, Agricul­ ture & food security, vol. 6, no. 1, p.62, (2017).

DOI: 10.1186/s40066-017-0146-0

[3] S. W. Fanta and S. Neela, A review on nutritional profile of the food from enset: A staple diet for more than 25 per cent population in ethiopia,, Nutrition & Food Science, vol. 49, no. 5, pp.824-843, (2019).

DOI: 10.1108/nfs-11-2018-0306

[4] A. Spring, M. Diro, S. A Brandt, E. Tabogie, G. Wolde­Michael, J. T. McCabe, M. Shigeta, C. Hiebsch, S. Tesfaye, and G. Yntiso, Tree against hunger: Enset­based agricultural systems in ethiopia., American Association for the Advancement of science, (1997).

[5] G. Welde­Michael, K. Bobosha, G. Blomme, T. Addis, T. Mengesha, and S. Mekonnen, Eval­ uation of enset clones against enset bacterial wilt,, African Crop Science Journal, vol. 16, no. 1, (2008).

DOI: 10.4314/acsj.v16i1.54348

[6] D. Yirgou, J. Bradbury et al., Bacterial wilt of enset (ensete ventricosum) incited by xan­ thomonas musacearum sp. n., Phytopathology, vol. 58, pp.111-12, (1968).

[7] J. Schmidhuber, Deep learning in neural networks: An overview,, Neural networks, vol. 61, pp.85-117, (2015).

DOI: 10.1016/j.neunet.2014.09.003

[8] Y. Rikiya, N. Mizuho, K. G. Richard, and K. Togashi, Convolutional neural networks: an overview and application in radiology,, Insights Imaging, vol. 9, pp.611-629, (2018).

DOI: 10.1007/s13244-018-0639-9

[9] S. Rahimeto, T. Girma, D. Yohannes, and F. Scwenker, Automatic pectoral muscle removal in mammograms," Under review at Evolving Systems with manuscript number EVOS­D­19­00111, 2019.[10] T. G. Debelee, F. Schwenker, S. Rahimeto, and D. Yohannes, "Evaluation of modified adaptive k­means segmentation algorithm,, Computational Visual Media, vol. 5, no. 4, pp.347-361, jul (2019).

DOI: 10.1007/s41095-019-0151-2

[11] K. P. Ferentinos, Deep learning models for plant disease detection and diagnosis,, Computers and Electronics in Agriculture, vol. 145, pp.311-318, (2018).

DOI: 10.1016/j.compag.2018.01.009

[12] G. Ian, B. Yoshua, and C. Aaron, Deep learning., MIT Press, (2016).

[13] F. Li, J. Justin, and Y. Serena. (2018) Cs231n: Convolutional neural networks for visual recognition. [Online]. Available: http://cs231n.stanford.edu/index.html.

[14] P. Josh and G. Adam, Deep learning a practitioner's approach." O,Reilly Media, (2017).

[15] A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks,, in Advances in neural information processing systems, 2012, pp.1097-1105.

DOI: 10.1145/3065386

[16] T. G. Debelee, M. Amirian, A. Ibenthal, G. Palm, and F. Schwenker, Classification of mam­ mograms using convolutional neural network based feature extraction,, LNICST, vol. 244, pp.89-98, (2018).

DOI: 10.1007/978-3-319-95153-9_9

[17] T. G. Debelee, A. Gebreselasie, F. Schwenker, M. Amirian, and D. Yohannes, Classification of mammograms using texture and cnn based extracted features,, Journal of Biomimetics, Bioma­ terials and Biomedical Engineering, vol. 42, pp.79-97, (2019).

DOI: 10.4028/www.scientific.net/jbbbe.42.79

[18] T. G. Debelee, F. Schwenker, A. Ibenthal, and D. Yohannes, Survey of deep learning in breast cancer image analysis,, Evolving Systems, Aug. (2019).

DOI: 10.1007/s12530-019-09297-2

[19] C. François, Deep learning with python., Manning Publications, (2017).

[20] H. Al­Hiary, S. Bani­Ahmad, M. Reyalat, M. Braik, and Z. Alrahamneh, Fast and accurate de­ tection and classification of plant diseases,, International Journal of Computer Applications, vol. 17, no. 1, pp.31-38, (2011).

DOI: 10.5120/2183-2754

[21] D. Al Bashish, M. Braik, and S. Bani­Ahmad, Detection and classification of leaf diseases us­ ing k­means­based segmentation and,, Information Technology Journal, vol. 10, no. 2, pp.267-275, (2011).

DOI: 10.3923/itj.2011.267.275

[22] J. Amara, B. Bouaziz, A. Algergawy et al., A deep learning­based approach for banana leaf diseases classification., in BTW (Workshops), 2017, pp.79-88.

[23] G. Owomugisha, J. A. Quinn, E. Mwebaze, and J. Lwasa, Automated vision­based diagnosis of banana bacterial wilt disease and black sigatoka disease,, in International Conference on the Use of Mobile ICT in Africa, (2014).

[24] B. Tigadi and B. Sharma, Banana plant disease detection and grading using image processing,, Int J Eng Sci, vol. 6512, (2016).

[25] S. Bhamare and S. Kulkarni, Detection of black sigatoka on banana tree using image processing techniques,, IOSR Journal of Electronics and Communication Engineering, pp.60-65, (2013).

[26] K. Simonyan and A. Zisserman, Very deep convolutional networks for large­scale image recog­ nition,, arXiv preprint arXiv:1409.1556, (2014).

[27] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions,, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp.1-9.

DOI: 10.1109/cvpr.2015.7298594