TAILIEUCHUNG - Classification of paddy growth age detection through aerial photograph drone devices using support vector machine and histogram methods, case study of Merauke regency

In this paper we present an approach to estimate the age of paddy in drone images using the Support Vector Machines - SVM and Histogram method. | Classification of paddy growth age detection through aerial photograph drone devices using support vector machine and histogram methods case study of Merauke regency International Journal of Mechanical Engineering and Technology IJMET Volume 10 Issue 03 March 2019 pp. 1850-1859. Article ID IJMET_10_03_187 Available online at http ijmet JType IJMET amp VType 10 amp IType 3 ISSN Print 0976-6340 and ISSN Online 0976-6359 IAEME Publication Scopus Indexed CLASSIFICATION OF PADDY GROWTH AGE DETECTION THROUGH AERIAL PHOTOGRAPH DRONE DEVICES USING SUPPORT VECTOR MACHINE AND HISTOGRAM METHODS CASE STUDY OF MERAUKE REGENCY Marsujitullah Fransiskus X. Manggau and Rachmat Informatics Engineering Universitas Musamus Merauke Indonesia ABSTRACT Farming is one of the spearheads of national development which has an important role especially Merauke Regency which is planned as an area of national food self- sufficiency in the field of agribusiness. Agriculture in Indonesia has a lot of food land that is widely spread and various types of paddy fields from several types of food management especially in agriculture however there is no system that visualizes the progress of food crop growth in particular areas by looking at the condition of the land in an approach visual. The estimated age of paddy growth is aimed at managing and monitoring paddy plants as information needs in assisting the government especially in monitoring the area planted by utilizing image images taken through aerial photographs using Drone devices. In this paper we present an approach to estimate the age of paddy in drone images using the Support Vector Machines - SVM and Histogram method. SVM is a learning machine method that works on the principle of Structural Risk Minimization SRM with the aim of finding the best hyperplane that separates two classes in input space. Input data are images from drone devices to support vector machines in their ability to find the best hyperplane that

• Nông nghiệp
• Age of paddy
• Support Vector Machines
• SVM and Histogram method
• Structural Risk Minimization
• Spearheads of national development