Currently, the cultivation of flowers is becoming popular. However, during the cultivationrnprocess there may be a number of challenges that affect it, one of which is flower disease.rnMost flower diseases are caused by insects, fungi, and bacteria. Identification of theserndiseases need experienced experts in this area. Thus, developing a system thatrnautomatically identifies flower diseases can help to support the experienced experts.rnIn view of this, an image processing based system for automatic identification of flowerrndisease is proposed. The proposed system consists of two main phases. In the first phasernnormal and diseased flower image are used to create a knowledge base. During the creationrnof the knowledge base, images are pre-processed and segmented to identify the region ofrninterest. Then, seven different texture features of images are extracted using Gabor texturernfeature extraction. Finally, an artificial neural network is trained using seven input featuresrnextracted from the individual image and eight output vectors that represent eight differentrnclasses of disease to represent the knowledge base. In the second phase, the knowledgernbase is used to identify the disease of a flower.rnIn order to create the knowledge base and to test the effectiveness of the developed system,rnwe have used 40 flower images for each of the eight different classes of flower disease andrnwe have a total of 320 flower images. From those images 85% of the Dataset is used forrntraining and 15% of the data set is used for testing. The experimental result demonstratesrnthat the proposed technique is effective technique for the identification of flower disease.rnThe developed system can successfully identify the examined flower with an accuracy ofrn83.3%.rnKeywords: Gabor Feature Extraction, Artificial Neural Network, Texture Feature