A Pipeline for Mushroom Mass Estimation Based upon Phenotypic Parameter: Multiple <em>Oudemansiella raphanipies</em> Model

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.