Optimal Pesticide Scheduling in Precision Agriculture

Abstract

Agricultural automation presents challenges typically encountered in the realm of cyber-physical systems, such as incomplete information (plant health indicators), external disturbances (weather), limited control authority (fertilizers cannot make a plant mature arbitrarily fast), and a combination of discrete events and continuous plant dynamics. In this paper, we investigate the problem of optimal pesticide spray scheduling. Regulations impose strict requirements on scheduling, e.g., individual pesticides are only effective during certain seasons and pesticides cannot be sprayed too close to harvest time. We show how to translate these requirements to a metric temporal logic formula over the space of schedules. We next use the theory of optimal mode scheduling to generate a schedule that minimizes the risk of various infestations over time while guaranteeing the satisfaction of the constraints. We demonstrate this methodology via simulation with scheduling constraints based on recommendations and regulations from agricultural experts. Our case study considers blueberries, a crop whose cultivation currently involves little automation.