Monocular Metric-Depth Adaptation for Agricultural Perception: A CitrusFarm Case Study with Embedded Inference

Adapting monocular metric depth to agricultural imagery requires balancing target-domain accuracy against the scope of model updates and embedded execution cost. We evaluate SARE-Depth on CitrusFarm using a frozen teacher-guided Depth Anything V2 Small base. Depth-Preserving Residual Head Refinement (DP-RHR) learns image-level log-scale and gated local corrections, followed by fixed depth-conditioned Range-Expert (RE) refinement. Before adaptation, fixed-checkpoint evaluations show persistent overestimation under both square and aspect-preserving input. On 750 internal-test images with common FP32 references and a 30 m cap, scalar calibration reduces absolute relative error (AbsRel) from 4.782 to 0.476. DP-RHR reaches 0.325 with 93,123 updated parameters; complete metric-head fine-tuning achieves 0.220 ± 0.006 across three runs with 2,728,513 updated parameters. Subsequent RE refinement provides smaller, domain-dependent gains with reduced threshold accuracy, while separate Dingxi examples reveal object-distance underestimation. Cumulative Jetson Orin NX latency increases from 26.87 ms for the base to 31.49 ms with DP-RHR and 35.08 ms for the complete path. These comparisons relate adaptation scope to accuracy and measured execution cost in this agricultural case study.