Estimating the mass of Oudemansiella raphanipies quickly and accurately is indispensable in optimizing post-harvest packaging processes. Traditional methods typically involve manual grading followed by weighing with a balance, which is inefficient and labor-intensive. To address the challenges encountered in actual production scenarios, in this work, we developed a novel pipeline for estimating the mass of multiple Oudemansiella raphanipies. To achieve this goal, an enhanced deep learning (DL) algorithm for instance segmentation and a machine learning (ML) model for mass prediction were introduced. On one hand, to segment multiple samples in the same image, a novel instance segmentation network named FinePoint-ORSeg was applied to obtain the finer edges of samples, by integrating an edge attention module to improve the fineness of the edges. On the other hand, for individual samples, a novel cap–stem segmentation approach was applied and 18 phenotypic parameters were obtained. Furthermore, principal component analysis (PCA) was utilized to reduce the redundancy among features. Combining the two aspects mentioned above, the mass was computed by an exponential GPR model with seven principal components. In terms of segmentation performance, our model outperforms the original Mask R-CNN; the $\mathrm{A}\mathrm{P}$, ${\mathrm{A}\mathrm{P}}_{50}$, ${\mathrm{A}\mathrm{P}}_{75}$, and ${\mathrm{A}\mathrm{P}}_{\mathrm{s}}$ are improved by 2%, 0.7%, 1.9%, and 0.3%, respectively. Additionally, our model outperforms other networks such as YOLACT, SOLOV2, and Mask R-CNN with Swin. As for mass estimation, the results show that the average coefficient of variation (CV) of a single sample mass in different attitudes is 6.81%. Moreover, the average mean absolute percentage error (MAPE) for multiple samples is 8.53%. Overall, the experimental results indicate that the proposed method is time-saving, non-destructive, and accurate. This can provide a reference for research on post-harvest packaging technology for Oudemansiella raphanipies.

Estimating the mass of Oudemansiella raphanipies quickly and accurately is indispensable for optimizing post-harvest packaging processes. The traditional methods typically involve manual grading followed by weighing with a balance, which is inefficient and labor-intensive. To address the challenges encountered in actual production scenarios, in this work, we proposed a novel pipeline for estimating the mass of multiple Oudemansiella raphanipies. To achieve this goal, an enhanced deep learning (DL) algorithm for instance segmentation and a machine learning (ML) model for mass prediction were introduced. On one hand, to segment the multiple samples in the same image, a novel instance segmentation network named FinePoint-ORSeg was presented to obtain the finer edges of samples, which integrated the edge attention module for improving the fineness of the edges. On the other hand, for individual samples, a novel cap-stem segmentation approach was applied and 18 phenotypic parameters were obtained. Furthermore, the Principal Component Analysis (PCA) was utilized to reduce the redundancy among features. Combining the two aspects mentioned above, the mass was computed by Exponential GPR model with 7 principal components. In terms of segmentation performance, our model outperforms the original Mask R-CNN, the AP, the AP50, the AP75 and the APs are improved by 2%, 0.7%, 1.9%, and 0.3%, respectively. Additionally, our model outperforms other networks such as YOLACT, SOLOV2 and Mask R-CNN with swin. As for mass estimation, the results showed the average Coefficient of Variation (CV) of single sample mass in different attitude are 6.81%. Moreover, an average mean absolute percentage error (MAPE) of multiple samples is 8.53%. Overall, the experimental results indicated that the proposed method is time-saving, non-destructive and accurate. This can provide a reference for the research on post-harvest packaging technology of Oudemansiella raphanipies.

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.