The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic Algorithm

Abstract

Rice is an agricultural sector that is very important for Indonesia's economy. The main problem with rice plants is pest and disease control which has a hazardous impact and economic losses for farmers. The apparent characteristics of rice leaves have a greater area than other plant structures; rice leaves can be applied for the early diagnosis of rice plant diseases. The approaches employed are fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means (GA-FCM). The center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research is Teaching Sawah Farm IPB, and the second dataset is UCI Rice Leaf Diseases. According to the comparison results, the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the silhouette coefficient value compared to just 60% for FCM. This research shows that the proposed method can add 5% accuracy to the clustering results in correctly identifying rice plant diseases.

Keywords

• clustering
• fuzzy c-means
• genetic algorithm
• image processing
• rice plants diseases