  Agriculture has always remained an integral part of the world. As the human
population keeps on rising, the demand for food also increases, and so is the
dependency on the agriculture industry. But in today's scenario, because of low
yield, less rainfall, etc., a dearth of manpower is created in this
agricultural sector, and people are moving to live in the cities, and villages
are becoming more and more urbanized. On the other hand, the field of robotics
has seen tremendous development in the past few years. The concepts like Deep
Learning (DL), Artificial Intelligence (AI), and Machine Learning (ML) are
being incorporated with robotics to create autonomous systems for various
sectors like automotive, agriculture, assembly line management, etc. Deploying
such autonomous systems in the agricultural sector help in many aspects like
reducing manpower, better yield, and nutritional quality of crops. So, in this
paper, the system design of an autonomous agricultural robot which primarily
focuses on weed detection is described. A modified deep-learning model for the
purpose of weed detection is also proposed. The primary objective of this robot
is the detection of weed on a real-time basis without any human involvement,
but it can also be extended to design robots in various other applications
involved in farming like weed removal, plowing, harvesting, etc., in turn
making the farming industry more efficient. Source code and other details can
be found at https://github.com/Dhruv2012/Autonomous-Farm-Robot
