IDENTIFICATION OF DISEASE ON LEAVES SOYBEAN USING MODIFIED OTSU AND LEARNING VECTOR QUANTIZATION NEURAL NETWORKS

  • Candra Dewi Faculty of Computer Science, University of Brawijaya; Essential Oil Institute, University of Brawijaya
  • Muhammad Sa’idul Umam Informatics Department, University of Brawijaya
  • Imam Cholissodin Informatics Department, University of Brawijaya

Abstract

Disease of the soybean crop is one of the obstacles to increase soybean production in Indonesia. Some of these diseases usually are found in the leaves and resulted to the crop become unhealthy. This study aims to identify disease on soybean leaf through leaves image by applying the Learning Vector Quantization (LVQ) algorithm. The identification begins with preprocessing using modified Otsu method to get part of the diseases on the leaves with a certain threshold value. The next process is to identify the type of disease using LVQ. This process uses the minimum value, the maximum value and the average value of the red, green and blue color of the image. The testing conducted in this study is to identify two diseases called Peronospora manshurica (Downy Mildew) and phakopsora pachyrhizi (Karat). The result of testing by using 60 training data and the value of all recommendations parameters obtained the highest accuracy of identification is 95% %, but the more stable accuracy is 90%. This result shows that the method perform quite well identification of two mentioned disease.

Downloads

Download data is not yet available.
Published
2018-07-10
How to Cite
DEWI, Candra; UMAM, Muhammad Sa’idul; CHOLISSODIN, Imam. IDENTIFICATION OF DISEASE ON LEAVES SOYBEAN USING MODIFIED OTSU AND LEARNING VECTOR QUANTIZATION NEURAL NETWORKS. Jurnal Ilmiah Kursor, [S.l.], v. 9, n. 3, july 2018. ISSN 2301-6914. Available at: <http://kursorjournal.org/index.php/kursor/article/view/158>. Date accessed: 29 nov. 2021. doi: https://doi.org/10.28961/kursor.v9i3.158.
Section
Articles