Pamuk yetiştiriciliğindeki hastalıkların derin öğrenme yaklaşımı ile tahmin edilmesi

In this thesis, a study on the detection and prediction of cotton diseases, which is a sub-title of environmental factors that are effective in the cultivation of cotton plants, with the help of image processing and deep learning methods is presented. In the first stage, the images of the cotton plant were preprocessed in order to minimize the problems that may be encountered during the application of the preferred deep learning methods. These data obtained as a result of the preprocessing were used as input data for the optimization of the applied deep learning models. With the help of this input data, the hyper-parameters of Convolutional Neural Networks, Long Short-Term Memory Networks and Convolutional Long Short-Term Memory Networks models are decided. In the last phase, the success rates of the predictions made on random images given as input to these optimized models were evaluated. The results obtained as a result of the study were analyzed and compared with the studies in the literature.