CLASSIFICATION OF PADDY GROWTH AGE DETECTION THROUGH AERIAL PHOTOGRAPH DRONE DEVICES USING SUPPORT VECTOR MACHINE AND HISTOGRAM METHODS, CASE STUDY OF MERAUKE REGENCY

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 selfsufficiency 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 separates two classes in the feature space supported by the SRM strategy. Histograms in graphical form that describe the spread of pixel intensity values of an image. With this research, it can be known the age of paddy plants through the histogram value taken on the image by the drone device, so that the growth phase parameters from one week to the harvest can be known with 89 percent accuracy.