Application of Unmanned Aerial Vehicles for Micro-Small and Medium Enterprises Agricultural in West Java - Sawita, I Kadek Agus Sara; Evita, Maria; Kane, Hansel; Rausyanfikr, Fadhil; Djamal, Mitra (Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura, 2022)
Agricultural Micro-Small and Medium Enterprises (MSMEs) play a vital role in maintaining national food security. In terms of sources of economic growth, the agricultural sector is the largest contributor to West Java's economic growth in the first quarter of 2021. The agricultural business sector contributed as much as 0.94%, greater than its contribution in the fourth quarter of 2020 which amounted to 0.39%. MSMEs still have external and internal constraints, especially in terms of financing, product marketing, and lack of access to information. These constraints often hinder MSMEs in developing their business and expanding their market share. From various cases of marketing agricultural MSME products in West Java, it is necessary to implement technology to market agricultural commodities. One of them is a map application, which is useful in providing the location of agricultural MSMEs and how to reach that location from the customer's current location. So that sellers/farmers and buyers can do direct transactions. Therefore, map applications using Unmanned Aerial Vehicles (UAV) can be used as the alternative technology to solve that problem. While, this research only focuses on generating the map. UAV - DJI Phantom 4 Pro has been used in this research to take images at each location of the sample locations. To control the UAV automatically for each mission, it used Pix4D capture flight plans. The data were processed by Agisoft Metashape Professional software. The location of image data collection was carried out in various areas: a building, an open space area and a real small and medium agriculture enterprises location. Two-dimensional maps and 3D maps of these areas have been successfully created. The average RMS error is 0.17 (2.88 pixels) indicating under 1% of the average error.