Abstract
Crops, vegetables, fruit trees, flowers and other cash crops, are often harmed by a variety of harmful organisms, plant pathogens, pests, weeds and pest rats, etc. Plant diseases and insect pests often occur, which are one of the main factors which causes the damage of leaves and crop failure. Therefore, in order to stop the pest, it is extremely important to identify the pests of plants and their characteristics correctly. In this paper, an effective and scalable image recognition algorithm is proposed for disease detection. Meanwhile, MobileNets is employed for developing our method on mobile devices. Finally, a dataset consists of three apple diseases is used to demonstrate the effectiveness of our method. In the experiments, transfer learning is used to train a deep convolutional neural network for identifying two types of pest damage, apple rusts and apple Alternaria leaf spot. Our results show that the MobileNets model offer a fast, affordable, and easy-to-deploy strategy for plant disease detection.
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References
Amara, J., Bouaziz, B., Algergawy, A.: A deep learning-based approach for banana leaf diseases classification. In: Proceedings of 2nd BigDS Workshops, pp. 79–88 (2017)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications, in Arxiv (2017)
Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., Ramon, H.: Automatic detection of ‘Yellow Rust’ in wheat using reflectance measurements and neural networks. Comput. Electron. Agric. 44(3), 173–188 (2004)
Wang, X., Zhang, M., Zhu, J., Geng, S.: Spectral prediction of phytophthora infestans infection on tomatoes using artificial neural network (ANN). Int. J. Remote Sens. 29(6), 1693–1706 (2008)
Rumpf, T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W., Plümer, L.: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 74(1), 91–99 (2010)
Sannakki, S.S., Rajpurohit, V.S., Nargund, V., Kumar, A., Yallur, P.S.: Leaf disease grading by machine vision and fuzzy logic. In: Proceedings of 2nd International Conference on Signal Processing and Integrated Networks, pp. 1709–1716 (2011)
Xu, P., Wu, G., Guo, Y., Chen, X., Yang, H., Zhang, R.: Automatic wheat leaf rust detection and grading diagnosis via embedded image processing system. Procedia Comput. Sci. 107, 836–841 (2017)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep Neural Networks based Recognition of Plant Diseases by Leaf Image Classification. Comput. Intell. Neurosci. 2016(3289801), 1–11 (2016)
Fujita, E., Kawasaki, Y., Uga, H., Kagiwada, S., Iyatomi, H.: Basic investigation on a robust and practical plant diagnostic system. In: Proceedings of 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 989–992 (2016)
Picon, A., Alvarez-Gila, A., Seitz, M., Seitz, M., Ortiz-Barredo, A., Echazarra, J., Johannes, A.: Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput. Electron. Agric. 161, 280–290 (2018)
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This work was partly supported by the National Natural Science Foundation of China under Grant No. 61702360.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Duan, Y., Li, D., Bi, C. (2020). Deep Learning Based Pest Identification on Mobile. In: Deng, DJ., Pang, AC., Lin, CC. (eds) Smart Grid and Internet of Things. SGIoT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 324. Springer, Cham. https://doi.org/10.1007/978-3-030-49610-4_10
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DOI: https://doi.org/10.1007/978-3-030-49610-4_10
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