Accurate and non-destructive volume estimation of agricultural products is essential for precision agriculture, yet remains challenging when transitioning from controlled laboratory conditions to complex orchard environments. Although 2D image-based volume estimation methods provide a cost-effective and scalable solution, existing studies are fragmented and lack a unified perspective on their real-world applicability. This review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition. We categorized existing volume estimation approaches according to the sensing modality into monocular RGB-based approaches and depth-assisted methods, and further reviewed them based on the image processing methods. A key finding is that high-precision geometric estimation can be achieved in laboratory environments, whereas deep learning and RGB-D fusion have driven a shift from conventional geometric modeling toward data-driven and hybrid learning frameworks in orchard settings. However, 2D image-based volume estimation remains fundamentally limited by scale ambiguity, severe occlusion, and sensitivity to illumination and background variability in real orchard environment. Overall, this review provides a unified perspective for understanding volume estimation methodology across environments and offers guidance for developing robust, scalable, and field-deployable volume estimation systems for real-world agricultural applications.

Monocular RGB imaging offers a low-cost, flexible approach to morphological measurement, quality assessment, growth monitoring, and production automation for edible mushroom fruiting bodies. However, the relationships among visible phenotypes, visual methods, measurement reliability, and production requirements remain insufficiently integrated. This problem-oriented review synthesizes 76 core studies published between 2016 and the final search date in 2026 through a framework linking visible phenotypes, visual tasks, key bottlenecks, and production applications. It covers individual localization and separation, structural measurement, spatial estimation, quality and species recognition, temporal analysis, and production deployment. The reviewed studies reveal a transition from static two-dimensional detection and counting toward instance-level morphological measurement, three-dimensional parameter estimation, and spatiotemporal growth modeling, extending phenotyping from visible appearance description to spatial trait estimation and growth prediction. Quantitative results also highlight the importance of evaluation conditions: for example, MSH-YOLOv8 achieved an ${\mathrm{AP}}_{50}$ of 98.49% on the Fungi dataset, whereas ${\mathrm{AP}}_{50:95}$ and small-object AP were 75.29% and 39.73%, respectively, indicating that high ${\mathrm{AP}}_{50}$ alone does not adequately characterize detection performance under stricter localization criteria or for small targets. These study-specific results cannot be directly extrapolated to commercial production environments. Severe occlusion, projection errors, inconsistent phenotype definitions, and temporal instability continue to constrain measurement reliability. Moreover, reliable performance without extensive retraining following changes in strains, substrates, or lighting systems remains insufficiently demonstrated. Future research should prioritize standardized multi-task and temporal datasets, unified phenotype definitions, uncertainty evaluation, occlusion-robust spatial and temporal modeling, and closed-loop production validation. Commercial scaling of low-cost monocular RGB imaging in protected mushroom cultivation depends on translating its affordability into reliable performance under severe occlusion and across production conditions, thereby enabling accessible automation for small and medium-scale producers.