SprayCraft: Graph-Based Route Optimization for Variable Rate Precision Spraying

TL;DR

SprayCraft introduces a graph-based approach for disease hotspot detection and optimal path planning for drones to perform variable rate precision spraying in agriculture, enhancing resource efficiency and disease management.

Contribution

The paper presents a novel graph-based method combining disease hotspot detection with TSP-based path optimization for precision spraying in A-CPS.

Findings

Effective identification of disease hotspots using graph message passing.

Near-optimal spraying paths computed via TSP approximation.

Validated on synthetic farmland data, demonstrating potential for real-world application.

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.