Abstract
In this paper, we proposed an artificial intelligence model for plants diseases classification based on convolutional neural network (CNN). The proposed model consists of three phases; (a) preprocessing phase, which augmented the data and balanced the dataset; (b) classification and evaluation phase based on pre-train CNN VGG16 and evaluate the results; (c) optimize the hyperparameters of CNN using Gaussian method. The proposed model is tested on the plant’s images dataset. The dataset consists of nine plants with thirty-three cases for diseased and healthy plant’s leaves. The experimental results before the optimization of pre-trained CNN VGG16 achieve 95.87% classification accuracy. The experimental results improved to 98.67% classification accuracy after applied the Gaussian process for optimizing hyperparameters.
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https://storage.googleapis.com/kaggle-datasets/53092/100927/plantdisease.zip
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Abou El-Maged, L.M., Darwish, A., Hassanien, A.E. (2020). Artificial Intelligence-Based Plant’s Diseases Classification. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_1
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