Mısır Ekim Alanları ve Dane Veriminin Uzaktan Algılama ve Hybrid-Maize Bitki Modeli ile Belirlenmesi

Abstract

The close relationship of agricultural production with environmental conditions and the increase in demand for agricultural products in today's world, make it necessary to monitor crop production in a reliable, fast and systematic manner. In this sense, remote sensing is a valuable technology that enables monitoring and crop area and yield estimations in agricultural studies. This research studied the area sown and grain yields of maize, a common crop on the Menemen Plain. In the research, maize fields were classified in object based classification manner by Göktürk-2 taken in 2020 with 86.7% overall accuracy and 73.3% Kappa value. The values of determination coefficient and significance level of the regression model which was obtained between measured maize grain yield and NDVI values of the satellite data were, R 2 =0.77 and P≤ 0.01, respectively. The regression model method was used to calculate the yield of wider areas in the research. In addition, potential yield of the region was estimated by a crop model, Hybrid-Maize. With this study, it has been concluded that the potential yield values calculated with the plant model can be compared with the actual yield values and can be used in the analysis of plot-based changes.