Abstract
There are many applications of smart agriculture based on Artificial Intelligence (AI). In this paper, we propose an AI-based vegetable classification system called VegeCare. We present the performance evaluation of the VegeCare tool for potato disease classification. We collect the main leaf diseases of potato crops. The dataset belongs to 3 classes. To evaluate the accuracy, we consider different epochs by training and validation stages. We found that VegeCare tool has good performance. The accuracy is more than 96% for potato disease classification.
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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)
Geetharamani, G., Arun Pandian, J.: Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput. Electr. Eng. 76, 323–338 (2019)
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, N.: HARC brochure. http://www.naro.affrc.go.jp/publicity_report/publication/files/2017NARO_english_1.pdf (2017)
Hughes, D.P., Salath’e, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. Computing Research Repository (CoRR) (2015)
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 Utility Comput. 6(3/4), 143–149 (2015)
Mattihalli, C., Gedefaye, E., Endalamaw, F., Necho, A.: Plant leaf diseases detection and auto-medicine. Internet Things 1–2, 67–73 (2018)
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)
Petrakis, E.G.M., Sotiriadis, S., Soultanopoulos, T., Renta, P.T., Buyya, R., Bessis, N.: Internet of things as a service (iTaaS): challenges and solutions for management of sensor data on the cloud and the fog. Internet Things 3–4, 156–174 (2018)
Ruedeeniraman, N., Ikeda, M., Barolli, L.: Performance evaluation of vegecare tool for tomato disease classification. In: Proceedings of the 22nd International Conference on Network-Based Information Systems (NBiS-2019), pp. 595–603, September 2019
Ruedeeniraman, N., Ikeda, M., Barolli, L.: Performance evaluation of vegecare tool for insect pest classification with different life cycles. In: Proceedings of the 8th International Conference on Emerging Internet, Data and Web Technologies (EIDWT-2020), pp. 171–180, February 2020
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
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)
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Ruedeeniraman, N., Ikeda, M., Barolli, L. (2021). Performance Evaluation of VegeCare Tool for Potato Disease Classification. In: Barolli, L., Li, K., Enokido, T., Takizawa, M. (eds) Advances in Networked-Based Information Systems. NBiS 2020. Advances in Intelligent Systems and Computing, vol 1264. Springer, Cham. https://doi.org/10.1007/978-3-030-57811-4_47
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DOI: https://doi.org/10.1007/978-3-030-57811-4_47
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