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.

翻译：大规模果园生产需要及时精确的病害监测，但常规人工巡查在现代运营规模下劳动强度大且经济上不可行。因此，病害爆发往往被延迟发现，并以果园地块级低空间分辨率进行跟踪。我们提出了一种自主移动主动感知系统，用于休眠苹果树的目标病害检测与定位，并以当前苹果最具破坏性病害之一——火疫病为例进行验证。该系统集成了闪光照明立体RGB传感、实时深度估计、实例级分割以及置信度感知的语义三维建图，实现了病害症状的精准定位。语义预测被融合到体积占用地图表示中，可同时跟踪占用率和逐体素语义置信度，为种植者构建可操作的空间地图。为在复杂树冠内主动优化观测，我们在统一的感知-行动循环框架内评估了三种视点规划策略：确定性几何基线、最大化未知空间缩减的体积型下一个最佳视点规划器，以及优先处理低置信度症状区域的语义型下一个最佳视点规划器。在实验室内人工树模型和五棵模拟症状树上的实验表明，作为现场评估的前期验证，该系统实现了可靠的症状定位与建图。模拟实验中，语义规划器在30个视点后达到最高F1分数（0.6106），而体积型规划器达到最高ROI覆盖率（85.82%）。在实验室环境中，语义规划器取得最高最终F1分数（0.9058），两种下一个最佳视点规划器的覆盖率均显著优于基线方法。