Automatic identification and targeted peeling of residual epidermis on lotus root based on an improved PSPNet

Lotus root is the vegetable rhizome of the lotus plant in Asian areas. Mechanical peeling is one of the most important steps during harvesting. However, the residual regions can be left after conventional mechanical peeling and currently require manual trimming, due to the complex curved surface of the lotus root and the low contrast between peel and flesh. Furthermore, the peeling rate assessment has been limited to manual inspection. This study aims to identify the residual epidermis on the lotus roots for target peeling using machine vision. An improved Pyramid Scene Parsing Network (PSPNet) was proposed for semantic segmentation. Three procedures were constructed, including visual recognition, precise localization, and automatic trimming. 1) Visual recognition. Dilated convolutions with a dilation rate of two were incorporated into Stage 4 and Stage 5 of the backbone network ResNet50. The receptive field was expanded without increasing the number of parameters after modification. Long-range contextual dependencies were captured to better distinguish the subtle textural differences between lotus root peel and flesh. The original bilinear upsampler was replaced with a DySample dynamic upsampler. The 3×3 convolutions were used to generate spatially adaptive sampling weights, according to the input feature map. As such, the decoder was used to more accurately reconstruct the fine edge structures of residual peel, especially in irregularly shaped regions. A FreqFusion module was introduced into the decoder path. The sensitivity to peel–flesh boundaries was significantly enhanced to adaptively fuse the high-frequency edge features and low-frequency semantic information from multi-scale feature maps. 2) Precise localization. Residual peel regions were localized to detect the global temporal images. A fixed industrial camera was used to capture the global video stream of the lotus root via the rotation of 360° around the axis of the clamping mechanism. The video stream was processed frame by frame to generate binary segmentation masks of residual peel. Key frames were then extracted to minimize the Euclidean distance between the centroid of a detected residual region and the midline of the predefined region of interest (ROI). The residual region was positioned directly face-to-face with the trimming cutter. The rotation angles corresponding to all residual regions were computed using frame sequence analysis. An optimal rotation path was planned after calculation. The stepper motor of the clamping mechanism was used to incrementally drive the rotation of the lotus root, sequentially aligning each residual region with the trimming cutter. The trimming mechanism subsequently performed continuous feed motion to realize the target trimming with consistent cutting depth. 3) Automatic trimming: The peeling rate of lotus root was achieved using a pixel ratio with linear scan unfolded images. A line-scan camera was used to acquire the high-resolution images of the lotus root surface during full rotation; Hikvision industrial camera client software was used to correct the distortion with the front-view images. An accurate image was obtained from the unfolded circumference of the entire lotus root surface. A precise segmentation mask was then generated to detect the residual peel regions in the unfolded image. Finally, the peeling rate was calculated to determine the pixel ratio of the residual peel to the total lotus root surface area. Experiments were conducted on a self-developed axial-feed lotus root peeling machine with a trimming detection platform. The improved PSPNet model was achieved in a mean intersection over union (mIoU) of 92.15%, precision of 95.74%, and recall of 95.85%, which was improved by 1.53, 0.92, and 0.75 percentage points, respectively, over the baseline PSPNet with ResNet50. The average angular positioning error of residual peel regions was 4.6°, and the removal rate of the regions reached 87.50% after target trimming. As a result, the overall peeling rate was improved from 73.62% to 91.16% after trimming, with an increase of 17.54 percentage points. The residual peel effectively enhanced the peeling quality with high precision. The research findings can provide important guidance for the intelligent upgrading of lotus root peeling equipment. The technical framework can be extended to the rest of the fruits and vegetables during peeling.