6th International Conference on Inventive Computation Technologies ICICT 2023, Lalitpur, Nepal, 26 - 28 Nisan 2023, ss.25-32
Today, effective production is interrupted unless land ownership
disputes are resolved. The state cannot make the necessary investments
due to these disputes not being concluded, and the borders of the fields
remain unclear. Artificial intelligence-based methods can be suggested
to eliminate disagreements and uncertainty. By using convolutional
neural network (CNN) based deep learning networks in which image data
are meaningful, areas with primary importance in crop production have
been identified in this study. With the CNN networks used by computer
vision technology, meaningful information can be extracted from the
image. Field detection processes were carried out in this study by using
deep learning networks that learn from data. As remote sensing studies
gain speed, the number of deep learning studies also increases. For this
purpose, satellite images were first collected from the Google Earth
website, and then these collected images were used in Faster R-CNN and
SSD training, which gained a reputation for accuracy and speed. It is
aimed to provide more efficient production and resolve disputes by
detecting the fields from satellite images. From two different networks
running, SSD outperformed Faster R-CNN in terms of both accuracy and run
time. With an f1 score of %97.32, SSD gave Faster R-CNN %3.18
superiority. In the field object results in the test images, the SSD
outperformed by detecting 12 more fields. In terms of run times, the SSD
performed faster detections with a difference of 285.5 ms in the
experiments tried in one-third of the test images.