Estimation of Rice Production on Paddy Fields by Vegetation Indices using Drone-Based and Sentinel-2 Imageries at Maturation Stage Mutmainna (a), Dr. Ir. Daniel Useng, M.Eng.Sc., (b*) and Husnul Mubarak, S.TP, M.Si. (b)
a) Student of Agriculture Engineering Study Program, Universitas Hasanuddin, Makassar, 90245, Indonesia
b) Lecturer of Agriculture Engineering Study Program, Universitas Hasanuddin, Makassar, 90245, Indonesia
Abstract
The Paddy plant is a rice-producing plant used as a staple food for the community. Based on data from the Central Statistics Agency in 2019, rice production in South Sulawesi is unstable. Estimating rice production will make it easier for the government and farmers to optimize planting planning and increase rice production. Remote sensing technology based on satellites and drones is one of the most widely used options for estimating rice production. Estimation of rice production uses vegetation indices such as NDVI (Normalized Vegetation Index), EVI (Enhancement Vegetation Index), GNDVI (Green Normalized Vegetation Index), and VARIGreen (Visible Athmosphericely Resistance Index). This study aims to determine the results and accuracy of estimating rice production using the drone and sentinel-2 imagery based on the vegetation indices when rice enters the maturation phase. In this study, the classification of the production level of paddy fields was carried out. A linear regression analysis was performed between the vegetation index values obtained from a drone and sentinel-2 images and rice production. The equation obtained from the regression analysis was used to estimate the production of rice plants in the paddy fields that were used as test samples. The results of this study, when the rice plant enters the maturation phase, the production estimation has a poor accuracy level. The guided classification that has been carried out has an Overall Accuracy value of 51,35%. The result of the estimation rice production at 73 DAP has an accuracy of 49,37% dan 31,43%.
Keywords: Estimation, Production, Vegetation Index, Sentinel-2 and drone
