We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic mapping, plant scene-graph reasoning, and semantics-aware active view-motion planning. Beyond inspecting uncertain observed regions, the scene graph explicitly hypothesizes unobserved pepper--peduncle attachments and directs close-range sensing toward them. Candidate views are selected according to expected information gain, while class-dependent motion costs distinguish protected peppers, peduncles, and stems from conditionally traversable foliage. On pepper data, the perception network achieves
55.27% semantic mIoU,
38.67% PQ, and
40.62mm depth RMSE, while input-conditioned uncertainty improves NYUv2 NLL from
−1.6518 to
−1.6925 and AUSE from
0.0102 to
0.0087.