Abstract:
Pakistan's major economy depends on agriculture, but we have seen many problems with
automation and technology due to a lack of information and lost a lot of crops. In order to get
high yield and more production along with the savage of extra labor time and money, we need
automation in this era. Automated and high-resolution imagery (temporal and spatial) acquired
from low-cost drones provides an opportunity to push the horizon of precision agriculture
especially in weed detection, pest attack detection, and yield estimation. The state of the art
approaches involving drones for precision agriculture does not include active inspection of the
area from drones. This requires making drones autonomous. This thesis aims at applying deep
learning at drone imagery acquired in an inquisitive manner. A lot of methods proposed for the
localization in agriculture, in that research we want to find out the more suitable in accordance
with our current economic condition. First, we tried to detect weed and localization of agriculture
using deep learning techniques, but faced a lot of issues, like dealing with intercropping and a lot
of more. Then we moved to some computer vision techniques, like SLAM, which helps to cope
with the problems that we faced during deep learning techniques. Then we move to one of the
simplest methods of localization in agriculture which requires less change in the field and will
give more useful and accurate results of localization. At the last, we did compression and
observation on all of the above methods of localization and will tell about their advantages and
disadvantages
