A New Hybrid Method for Classification of Rice Leaf Diseases: SVM+NCA+Resnet50

Rice is extremely important for individuals and countries, both in terms of nutritional value and financial value. It is necessary to protect such an important plant from diseases and increase the yield. However, early detection of diseases on plant leaves can prevent the spread of this disease and is also very important in terms of treating the plant. Artificial intelligence has become very popular in recent years thanks to its success in terms of disease classification. CNN architectures used in image classification perform very successful work. Within the scope of this study, it is recommended that the diseases on rice leaves be classified using artificial intelligence techniques, without mixing them with each other, with very high accuracy values, and without any problems caused by humans. With this proposed model, a support vector machine-based model is proposed that classifies five (5) of the most common rice diseases with a very high accuracy of %98.