Abstract
Maize (Zea mays L.) is one of the essential agricultural products in Papua Province of Indonesia, specifically in the three largest maize producing regions, namely Nabire Regency, Biak Numfor Regency and Merauke Regency, with the number of productions of 991 tons, 764 tons, and 751 tons respectively in 2015. Unfortunately, since 2016 the secondary data on food crops productivity, including maize, has not been provided yet in the provinces statistical report, due to manual estimation methods, i.e., visual estimation. On the other side, the number of populations in this Province has a slight increase, from 2.97 million people in 2012 to 3.38 million in 2019. Further, approximately 1.20 million people are employed in the agricultural sector. Considerable population growth will intensify the demand for food stock and other utilization of food crops in this region; hence, relevant research in food crops needs to be considered. One of the dominant factors in the yield potential of maize is plant height, since it is associated with fertilizer, seed, and soil treatment and predicts yield area. Therefore, this study aims to analyse the plant height, particularly maize plant based on a digital surface model (DSM) derived from Unmanned Aerial Vehicle (UAV) Red Green Blue (RGB) images. The crop was monitored during the second and third week of January 2022 and then, processed using pix4d Mapper software to produce the DSM, Digital Terrain Model (DTM), and orthomosaic. Then, the Geographical Information System (GIS) software, and an open-source software, namely Python were used to estimate the plant height. Next, the results were assessed statistically to examine the validation, the strong correlation coefficient of the estimation to the actual height that obtained from UAV and ground-based plant height data. The findings will help to support the prior decision support on estimation of maize production in Papua Province.
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