Reliable decision support in digital agriculture requires not only accurate predictions but also well-calibrated uncertainty estimates, particularly for dense prediction tasks such as semantic segmentation. Ensembles provide strong uncertainty quantification but are computationally and memory demanding, while single-model approximations often sacrifice uncertainty quality. We propose ST-LoRA, a parameter-efficient ensemble that builds diverse members from a single training trajectory by combining Low-Rank Adaptation (LoRA) with snapshot ensembling. All members share a frozen pretrained backbone and differ only in lightweight low-rank adapters, which sharply reduces trainable parameters, checkpoint storage, and I/O overhead. We evaluate SegFormer, Mask2Former, and EoMT on GrowliFlower-L (cauliflower, open field) and BUP20 (sweet pepper, glasshouse), covering in-distribution performance, calibration under covariate shift, and near- and far-out-of-distribution (OoD) detection, with BUTom21 (tomato) as near-OoD data. Extensive ablations show that feed-forward layers, not attention projections, are the critical LoRA target for dense prediction, and that the scaling ratio $α/r$ governs an accuracy--calibration trade-off. Against full-rank snapshot ensembles, ST-LoRA is competitive in segmentation quality, with architecture-dependent training time and energy savings. Against MC Dropout, DDU, and six post-hoc calibrators, it achieves the strongest far-OoD image-level detection and near-OoD pixel-level localization with low cross-seed variance, although full-rank ensembles remain better calibrated. These results show that LoRA-based ensembling offers a compelling efficiency--performance trade-off for agricultural vision systems.

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