SprayCraft: Graph-Based Route Optimization for Variable Rate Precision Spraying
Kiran K. Kethineni, Saraju P. Mohanty, Elias Kougianos, Sanjukta Bhowmick, Laavanya Rachakonda
TL;DR
SprayCraft tackles efficient, targeted pesticide application in large-scale agriculture by marrying graph-based hotspot detection with route optimization. It represents diseased locations as a graph and uses message passing to estimate hotspot probabilities, guiding variable-rate spraying. A Christofides-based near-optimal TSP tour is combined with a Boustrophedon spray pattern, with separate handling for variable-rate and constant-rate sprayers, and GPS compatibility. Validated on synthetic farmland data, SprayCraft demonstrates improved pesticide utilization and precise hotspot coverage, with clear pathways for extending to multi-drone and wind-aware scenarios.
Abstract
To efficiently manage plant diseases, Agriculture Cyber-Physical Systems (A-CPS) have been developed to detect and localize disease infestations by integrating the Internet of Agro-Things (IoAT). By the nature of plant and pathogen interactions, the spread of a disease appears as a focus with density of infected plants and intensity of infection diminishing outwards. This gradient of infection needs variable rate and precision pesticide spraying to efficiently utilize resources and effectively handle the diseases. This article, SprayCraft presents a graph based method for disease management A-CPS to identify disease hotspots and compute near optimal path for a spraying drone to perform variable rate precision spraying. It uses graph to represent the diseased locations and their spatial relation, Message Passing is performed over the graph to compute the probability of a location to be a disease hotspot. These probabilities also serve as disease intensity measures and are used for variable rate spraying at each location. Whereas, the graph is utilized to compute tour path by considering it as Traveling Salesman Problem (TSP) for precision spraying by the drone. Proposed method has been validated on synthetic data of locations of diseased locations in a farmland.
