Abstract:
Agriculture plays a pivotal role in the growth of any nation.
Nowadays, with the advancements of information
technologies, big data are generated and executed using
faster computing techniques. machine learning techniques
have been used to automate and improve agricultural activities
for a long time. This paper presents a review of
existing applications of machine learning in agriculture
with a focus on the applications of Deep Reinforcement
Learning techniques in agriculture. The conventional solutions
for agricultural machine learning decision-making
problems are using supervised approaches. In supervised
learning, the machine needs to be trained on samples of
inputs and outputs to support decision making. While, in
reinforcement learning, sequential decision making happens
and the next input depends on the decision of the
machine. In this paper, we perform a survey of 49 publications
of which 10 were secondary research e orts that
discussed a variety of machine learning approaches applications
in agriculture and 39 research e orts that used
machine learning approaches to support the automation
of agricultural activities.