Winter wheat Segmentation using AI

In this research, a newly modified UNet (Fast-UNet) was implemented to segment winter wheat from time series Sentinel-2 images for the years 2021 and 2023. These images were converted to NDVI and utilized to identify wheat fields by tracking the wheat phenology from sowing to harvesting. The main satellite image that was used in this research was Sentinel-2.  It is considered important, and free optical remote sensing satellite data is provided by the European Space Agency (ESA). Sentinel-2A and Sentinel-2B were launched in June 2015 and March 2017, respectively. They have a spatial resolution that varies between 10 m and 60 m depending on the wavelength. Sentinel-2A has a temporal resolution of 10 days, which can become 5 days with the combination of Sentinel-2B. The clipped image has a size of 6764 × 9018 pixels and comprises bands 3, 4, and 8, which correspond to green, red, and near-infrared. The bands were chosen for their high spatial resolution (10 meters) and their representation of the crop’s photosynthesis process.  Sentinel-2 images were selected from March to July 2021 and 2023 for two reasons: to reduce the cloud cover effect (less than 5% of the image size) and to separate wheat from other vegetation based on the phenology progress of these crops. Normally, farmers prefer to make a turnaround between planting winter wheat and potatoes for this reason recent odd years were selected.