  With the increase in world population, food resources have to be modified to
be more productive, resistive, and reliable. Wheat is one of the most important
food resources in the world, mainly because of the variety of wheat-based
products. Wheat crops are threatened by three main types of diseases which
cause large amounts of annual damage in crop yield. These diseases can be
eliminated by using pesticides at the right time. While the task of manually
spraying pesticides is burdensome and expensive, agricultural robotics can aid
farmers by increasing the speed and decreasing the amount of chemicals. In this
work, a smart autonomous system has been implemented on an unmanned aerial
vehicle to automate the task of monitoring wheat fields. First, an image-based
deep learning approach is used to detect and classify disease-infected wheat
plants. To find the most optimal method, different approaches have been
studied. Because of the lack of a public wheat-disease dataset, a custom
dataset has been created and labeled. Second, an efficient mapping and
navigation system is presented using a simulation in the robot operating system
and Gazebo environments. A 2D simultaneous localization and mapping algorithm
is used for mapping the workspace autonomously with the help of a
frontier-based exploration method.
