Dynamic scheduling of robotic mildew treatment by UV-c in horticulture

Thanks to new technologies, it is possible to make an automatic robotic
treatment of plants for the mildew in greenhouses. The optimization of the scheduling of this robotic treatment presents a real challenge due to the continues evolution
of disease level. The conventional optimization methods can not provide an accurate
scheduling able to eliminate the disease from the greenhouse. This paper proposes
a solution to provide a dynamic scheduling problem of evolutionary tasks in horticulture. We first developed a genetic algorithm (GA) for a static model. Then we
improved it for the dynamic case where a dynamic genetic algorithm (DGA) based
on the prediction of the task amount is developed. To test the performance of our
algorithms, especially for the dynamic case, we integrated our algorithms in a simulator.