Artificial Intelligence-Based Plant Diseases Classification

Machine learning techniques are used for classifying plant diseases. Recently, deep learning (DL) is applied in the classification process of image processing. In this chapter, convolutional neural network (CNN) is used to classify plant diseases images. However, CNN suffers from the hyper parameters problem which can affect the proposed model. Therefore, Gaussian optimization method is used to overcome the hyper parameters problem in CNN. This chapter 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.