A practical, reproducible pipeline for olive yield estimation is presented, combining close-range UAV still imagery, automated fruit detection, and a simple harvest-based calibration step. For a chosen set of six test trees, one high-resolution canopy image was captured at multiple dates, a fine-tuned object detector was applied to obtain digital fruit counts, and the median of per-tree detections was used as a robust representative digital count. Harvest weights collected at the end of the season were converted to estimated fruit counts using per-cultivar mean fruit mass, while calibration coefficients were computed as the ratio of harvest-derived counts to the representative digital counts. Calibration coefficients are proposed for two observed olive cultivars, enabling estimation of total per-cultivar yield in the observed orchard. The workflow is evaluated and the primary sources of uncertainty -- viewpoint-dependent coverage, occlusion, illumination variation, and detector errors -- are analysed. The results demonstrate the feasibility of translating single-view digital counts into calibrated yield estimates, while also highlighting substantial variability that motivates pragmatic mitigations: further in-domain labelling and retraining, multispectral foliage masking, scale-aware architectures, and multi-view sampling. The proposed approach is lightweight and suitable for operational orchard monitoring; recommendations and a roadmap to reduce uncertainty and improve robustness are provided.