Classification of tea leaves diseases by developed CNN, feature fusion, and classifier based model

Abstract

Due to the increase in the world population day by day, the amount of food needed is also increasing day by day. Diseases that occur in plants reduce the amount and quality of the product obtained. In this study, a computer-aided model was developed to detect diseases in tea leaves. Because plant diseases can be difficult and misleading to detect with the naked eye by farmers or experts. It is very important to detect diseases in tea leaves using artificial intelligence methods. Three Convolutional Neural Network (CNN) architectures accepted in the literature were used as the basis for the classification of diseases in tea leaves. With these three CNN architectures, feature maps of the images in the data set were obtained. After combining the feature maps obtained in each architecture, they were classified in the Linear Discriminant classifier. In addition, the performance of the proposed model was compared with seven CNN architectures accepted in the literature. The performance of the models used in the study was evaluated using different performance measurement metrics. The obtained results showed that the proposed model can be used to classify diseases in tea leaves.