Classification of olive leaf diseases using deep learning techniques

Convolutional neural network models are known to be very successful in diagnosing diseases and pests on plant leaf images. In this thesis, three different leaf types belonging to olive plant were classified with convolutional neural network model. The data set consists of 3400 olive leaf images. There are 3 different classes Olive Peacock Spot disease, Aculus Olearius and healthy leaves. The effect of different optimization algorithms and iteration numbers on the classification success was observed with the help of Convolutional Neural Networks (CNN).CNN models used in the thesis were trained with Tesla GPUs in Google Colab cloud service. Anaconda (Spyder) is written in python programming language on the IDE platform. The web model of the trained model was created with the help of the Flask framework. The proposed CNN model achieved an accuracy rate of 84% with the Adam optimization algorithm over 100 iterations. Using the transfer learning method, advanced CNN models such as VGG16 and VGG19 were trained with the generated data set. According to comparative test results, VGG16 model gave the best result with 88% accuracy. The CNN model, which was proposed in the thesis study, was applied to the dataaugmentation process with ImageDataGenarator class of Keras framework. This model, which is applied dataaugmentation method, was trained in 100 iterations by using Adam optimization algorithm. As a result of the dataaugmentation process, the proposed CNN model achieved the best classification success with 94% accuracy rate.