Abstract
Apple tree disease is a main threat factor to apple quality and yield. This paper proposed an improved convolutional neural network model to classify apple tree diseases. It took the advantages of neural network to extract the deep characteristics of disease parts, and used deep learning to classify target disease areas. In order to improve the classification accuracy and speed up the convergence of the network model, the center loss and focal loss functions were fused, instead of the traditional softmax loss function, which was especially important for our classification network model. Experimental results on our apple trunk dataset showed that our model achieved an accuracy of 94.5%. Therefore our method is feasible and effective for apple tree disease identification.
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Acknowledgement
This work was financially supported by the National Natural Science Foundation of China (Nos. 61672035, 61872004, 61472282 and 31401293), Anhui Province Funds for Excellent Youth Scholars in Colleges (gxyqZD2016068) and Anhui Scientific Research Foundation for Returned Scholars.
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Hang, J., Zhang, D., Chen, P., Zhang, J., Wang, B. (2019). Identification of Apple Tree Trunk Diseases Based on Improved Convolutional Neural Network with Fused Loss Functions. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_26
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