Detecting Wheat Leaf Diseases: A Deep Feature-Based Approach with Machine Learning Classification

Abstract

Wheat is a rich storehouse of nutrients with many different vitamins and minerals. Wheat is one of the main cereals that meet the nutritional needs of humans and other living things and is used in the production of other foods. It can be grown in almost all regions of the world. It requires less irrigation compared to other plants. One of the most important problems in wheat cultivation is the fight against diseases. Wheat diseases cause yield losses and quality decreases as in other agricultural products. Timely and accurate diagnosis of these diseases; It is clear that it will lead to an increase in yield and quality. Detection of these diseases with the naked eye can be difficult and laborious. In this study, diseases on wheat leaves were detected using image processing techniques. The features of septoria and stripe rust diseases on wheat leaves were extracted using pre-trained networks VGG-16, VGG-19 and then classified with machine learning algorithms support vector machines (SVM), multi-layer perceptron (MLP), k-nearest neighbor (KNN). The results obtained were evaluated with performance criteria such as accuracy, sensitivity, specificity, precision and F1-Score. In the analysis, the features extracted with VGG-19 were classified with SVM method and the highest classification accuracy of 98.63% was achieved.