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.
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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
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DOI: https://doi.org/10.1007/978-3-030-30465-2_1
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