Abstract
The aged population in Japan is increased and some people start to work in agriculture after the retirement. Therefore, it is important to teach the skills to these agricultural beginners. Also, to improve the productivity of vegetables, advanced approaches based on Artificial Intelligence (AI) and sensing technology are needed. We focus on vegetable recognition using Deep Neural Network (DNN) as a method to visualize the knowledge in vegetable production. In this paper, we present the performance evaluation of VegeCare tool for tomato disease classification. We used 6 kinds of tomato diseases. The plant disease classification is one of the functions of the proposed VegeCare tool, which helps the growth of vegetables for farmers. The evaluation results show that the learning accuracy was more than 90%. We found that the tomato disease classification results was selected correctly for the tomato mosaic virus.
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References
Arridha, R., Sukaridhoto, S., Pramadihanto, D., Funabiki, N.: Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system. Int. J. Space-Based Situated Comput. 7(2), 82–93 (2017)
Faria, F.A., dos Santos, J.A., Rocha, A., da S. Torres, R.: Automatic classifier fusion for produce recognition. In: Proceedings of the 25th International Conference on Graphics, Patterns and Images (SIBGRAPI-2012), pp. 252–259 (2012)
Gentile, A., Santangelo, A., Sorce, S., Vitabile, S.: Human-to-human interfaces: emerging trends and challenges. Int. J. Space-Based Situated Comput. 1(1), 3–17 (2011)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Hokkaido Agricultural Research Center, NARO: HARC brochure (2017). http://www.naro.affrc.go.jp/publicity_report/publication/files/2017NARO_english_1.pdf
Kang, L., Kumar, J., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for document image classification. In: Proceedings of 22nd International Conference on Pattern Recognition 2014 (ICPR-2014), pp. 3168–3172, August 2014
Le, Q.V.: Building high-level features using large scale unsupervised learning. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2013 (ICASSP-2013), pp. 8595–8598, May 2013
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616, June 2009
Mahesha, P., Vinod, D.: Support vector machine-based stuttering dysfluency classification using GMM supervectors. Int. J. Grid Util. Comput. 6(3/4), 143–149 (2015)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Okamoto, K., Yanai, K.: Real-time eating action recognition system on a smartphone. In: Proceedings of the IEEE International Conference on Multimedia and Expo Workshops (ICMEW-2014), pp. 1–6 (2014)
Ruedeeniraman, N., Ikeda, M., Barolli, L.: TensorFlow: a vegetable classification system and its performance evaluation. In: The Proceedings of the 13th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2019), July 2019
Sakai, Y., Oda, T., Ikeda, M., Barolli, L.: VegeShop tool: a tool for vegetable recognition using DNN. In: Proceedings of the 11th International Conference on Broad-Band Wireless Computing, Communication and Applications (BWCCA-2016), pp. 683–691, November 2016
Sardogan, M., Tuncer, A., Ozen, Y.: Plant leaf disease detection and classification based on CNN with LVQ algorithm. In: Proceedings of the 3rd International Conference on Computer Science and Engineering (UBMK-2018), pp. 382–385, September 2018
Shoji, S., Koyama, A.: A fast search and classification method of isomorphic polygons in LSI design data using geometric invariant feature value. Int. J. Space-Based Situated Comput. 6(4), 199–208 (2016)
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., Hassabis, D.: Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017)
Singh, V., Misra, A.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2017)
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Ruedeeniraman, N., Ikeda, M., Barolli, L. (2020). Performance Evaluation of VegeCare Tool for Tomato Disease Classification. In: Barolli, L., Nishino, H., Enokido, T., Takizawa, M. (eds) Advances in Networked-based Information Systems. NBiS - 2019 2019. Advances in Intelligent Systems and Computing, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-29029-0_59
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DOI: https://doi.org/10.1007/978-3-030-29029-0_59
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