Active Robotic Perception for Disease Detection and Mapping in Apple Trees
This addresses the need for timely and precise disease detection in large-scale orchard production, offering a practical solution for growers, though it is incremental as it builds on existing perception and planning methods.
The paper tackles the problem of labor-intensive disease monitoring in apple orchards by developing an autonomous mobile active perception system for detecting and mapping fire blight symptoms, achieving an F1 score of 0.9058 in lab tests and up to 85.82% coverage in simulations.
Large-scale orchard production requires timely and precise disease monitoring, yet routine manual scouting is labor-intensive and financially impractical at the scale of modern operations. As a result, disease outbreaks are often detected late and tracked at coarse spatial resolutions, typically at the orchard-block level. We present an autonomous mobile active perception system for targeted disease detection and mapping in dormant apple trees, demonstrated on one of the most devastating diseases affecting apple today -- fire blight. The system integrates flash-illuminated stereo RGB sensing, real-time depth estimation, instance-level segmentation, and confidence-aware semantic 3D mapping to achieve precise localization of disease symptoms. Semantic predictions are fused into the volumetric occupancy map representation enabling the tracking of both occupancy and per-voxel semantic confidence, building actionable spatial maps for growers. To actively refine observations within complex canopies, we evaluate three viewpoint planning strategies within a unified perception-action loop: a deterministic geometric baseline, a volumetric next-best-view planner that maximizes unknown-space reduction, and a semantic next-best-view planner that prioritizes low-confidence symptomatic regions. Experiments on a fabricated lab tree and five simulated symptomatic trees demonstrate reliable symptom localization and mapping as a precursor to a field evaluation. In simulation, the semantic planner achieves the highest F1 score (0.6106) after 30 viewpoints, while the volumetric planner achieves the highest ROI coverage (85.82\%). In the lab setting, the semantic planner attains the highest final F1 (0.9058), with both next-best-view planners substantially improving coverage over the baseline.