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.
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Classification of paddy growth age detection through aerial photograph drone devices using support vector machine and histogram methods, case study of Merauke regencyInternational Journal of Mechanical Engineering and Technology (IJMET)Volume 10, Issue 03, March 2019, pp. 1850-1859. Article ID: IJMET_10_03_187Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3ISSN 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 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. Keywords: Support Vector Machines, Histogram, Image classification, Structural Risk Minimization Cite this Article Marsujitullah, Fransiskus X. Manggau and Rachmat, 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, 10(3), 2019, pp. 1850-1859. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3 http://www.iaeme.com/IJMET/index.asp 1850 editor@iaeme.com Students’ Perceptions towards the Grammar Teaching at English Literature Department of Musamus University1. INTRODUCTIONThe purpose of remote sensing is to analyze or measure the physical number of drivers withoutdirect physical contact, the benefits obtained in using remote sensing, especially in using adrone device, are observed in a large area, in terms of financing more affordable compared toland surveys . In its use, drone devices can be used anywhere on agricultural land, considering thatIndonesia is not only well-known as an archipelagic country, it is also known by the worldcommunity as an agricultural country, where most of its land area is still used for agriculturaland plantation purposes. As a country that has been involved in agriculture and plantations fora long time, of course it has often faced various inhibiting factors which can reduce the level ofagricultural productivity. Various steps are taken to map agricultural productivity, from simplemethods to the use of advanced technologies that exist today. One of the main applications forremote sensing is for agricultural monitoring or management. For example, long distancesensing techniques are used to calculate the number of oil palm trees [1]. In [2], satellite imageryis used to estimate the biomass of secondary forest above land in Brazil. By using remotesensing, monitoring large quantities of agriculture is possible and cost effective. In [3],multispectral satellite imagery, FORMOSAT-2, is used to map cultivation areas and monitorcrop status on a regional scale. Furthermore far sensing can also be used to detect plantationareas [4] and crop yields prediction [5]. By way of implanting sensors in agricultural areas asin research [6,7] then processing the sensing data from different places. For each data retrievalmethod there are advantages and disadvantages. In taking data directly in the field, a lot of timewill be wasted because each farm must be visited one by one to get the expected data. However,this will be very different if you use the remote sensing method, where the desired data can beobtained on such a wide scale and in a relatively short time. What is an obstacle in remotesensing is that the earths atmosphere is not always clean of clouds. Though clouds or otherobjects in the atmosphere can interfere with or make satellites unable to record events that occuron the surface of the earth. In the data collection method that utilizes sensors planted in each agricultural area the resultscan be far better when compared to the first and second data collection methods because thetools have been installed on the site so that the accuracy of the reading can be better and do notneed to check the farm area one by ...