Yield estimation of corn plant with UAV-based NDVI

In recent times, remote sensing technology allows the Earth's surface to be observed simultaneously in different spectral regions, and the data obtained are used in many application areas. One of these applications is monitoring urban development. The observation of urban change over time using multi-temporal satellite image data enables the determination of changes in land cover and use as a result of urban development.This study aims to assess the health and productivity of vegetation through the use of spectral indices. The research, conducted on corn fields in the Çamlıdere neighborhood of Haliliye district in Şanlıurfa province in the Southeastern Anatolia Region, is carried out based on data collected using Unmanned Aerial Vehicles (UAVs) and multispectral cameras. The study specifically focuses on corn plants grown as a second crop for grain and silage.In the research, images collected with the DJI Mavic 3 Multispectral UAV were processed using PIX4D Field software, and various spectral indices (such as NDVI, GNDVI, RENDVI) were calculated to analyze the health status and estimated productivity of the plants. The effectiveness of spectral indices in predicting plant health and yield was evaluated by comparing the spectral index values with actual field data. The findings of the study demonstrate that spectral index-based analyses can be a powerful tool to increase productivity in agricultural production and support management decisions. Furthermore, the effects of irrigation methods and different agricultural practices on plant health could be successfully monitored through spectral indices. This study highlights the importance of using remote sensing techniques and spectral indices in agricultural research and lays the groundwork for expanding these methods.