Abstract
Fruit tree disease detection system is very essential to reduce the production pressure of fruit farmers. The disease of fruit trees can be detected through the appearance recognition of leaves. CNN is widely used for object detection and recognition. In order to save cost, we proposed a fruit tree disease detection scheme based on CNN. By cutting and compressing the traditional neural network model, we designed a lightweight neural network model, which can be run on Raspberry PI and can be used to detect fruit tree diseases in an offline environment. The Plant Village dataset was used in this article to verify its validity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, Z.Q., Zeng, S.M.: Wheat Rusts in China, pp. 1–379. China Agriculture Press, Beijing (2002)
Li, H.Y., Chen, Z.Y., Zhou, J.C., Zhang, J.X.: Occurrence and damage of the main diseases of grape in greenhouse during growthperiod and shelf life. Mod. Agric. Sci. Technol. 39, 187–189 (2010)
Wang, H.G., Ma, Z.H., Zhang, M.R., Shi, S.D.: Application of computer technology in plant pathology. Agric. Netw. Inform. 19, 31–34 (2004)
Wang, N., Wang, K.R., Xie, R.Z., Lai, J.C., Ming, B., Li, S.K.: Maize leaf disease identification based on fisher discrimination
Ma, X.D., Qi, G.Y.: Investigation and recognition on diseased spots of soybean laminae based on neural network. J. Heilongjiang Aug. First Land Reclamat. Univ. 18, 84–87 (2006)
Song, K., Sun, X.Y., Ji, J.W.: Corn leaf disease recognition based on support vector machine method. Trans. CSAE 23, 155–157 (2007)
Bock, C.H., Poole, G.H., Parker, P.E., Gottwald, T.R.: Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit. Rev. Plant Sci. 29, 59–107 (2010)
Ahmad, I.S., Reid, J.F., Paulsen, M.R., Sinclair, J.B.: Color classifier for symptomatic soybean seeds using image processing. Plant Dis. 83, 320–327 (1999)
Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., ALRahamneh, Z.: Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17, 31–38 (2011)
Li, Z.R., He, D.J.: Research on identify technologies of apple’s disease based on mobile photograph image analysis. Comput. Eng. Design 31, 3051–3053 (2010)
Wang, Y.P., Dai, X.P., Huang, H., Zhang, R.: Identification of tomatoes with diseases based on mathematical morphology and neural network. J. Hum. Agric. Univ. (Nat. Sci.) 32, 344–346 (2006)
Tian, Y.W., Li, T.L., Li, C.H., Piao, Z.L., Sun, G.K., Wang, B.: Method for recognition of grape disease based on support vectormachine. Trans. CSAE 23, 175–180 (2007)
Ren, D., Yu, H.Y., Wang, J.H.: Research on plant disease recognition based on linear combination of the kernel function support vector machine. J. Agric. Mech. Res. 29, 41–43 (2007)
Camargo, A., Smith, J.S.: Image pattern classification for the identification of disease causing agents in plants. Comput. Electron. Agric. 66, 121–125 (2009)
Li, G.L., Ma, Z.H., Huang, C., Chi, Y.W., Wang, H.G.: Segmentation of color images of grape diseases using K_means clustering algorithm. Trans. CSAE 26, 32–37 (2010)
Li, G.L.: Preliminary study on automatic diagnosis and classification method of plant diseases based on image recognition technology, pp. 1–64. China Agricultural University, Beijing (2011)
Strange, R.N., Scott, P.R.: Plant disease: a threat to global food security. Annu. Rev. Phytopathol. 43, 83–116 (2005)
Harvey, C.A., et al.: Extreme vulnerability of smallholder farmers to agricultural risks and climate change in madagascar. Philos. Trans. Roy. Soc. London B: Biol. Sci. 369, 20130089 (2014)
Ehler, L.E.: Integrated pest management (IPM): definition, historical development and implementation, and the other IPM. Pest Manag. Sci. 62(9), 787–789 (2006)
Hughes, D.P., Salathé, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. CoRR abs/1511.08060 (2015)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, F., Li, Z., Yan, L. (2020). CNN and Raspberry PI for Fruit Tree Disease Detection. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-30465-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30464-5
Online ISBN: 978-3-030-30465-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)