To address the prominent issues in current potato peeling processes (such as high labor intensity, excessive flesh loss, hard-to-remove peel from bud eyes/concaves), a non-contact waterjet method was proposed. Based on the computational fluid dynamics (CFD) method, the Fluent software was used to simulate and analyze the flow field of fan-shaped nozzle models with different slot angles. The simulation results indicated that the 25° scattering angle nozzle had excellent performance: it ensured effective potato surface coverage and minimized jet energy loss, fitting peeling needs. A one-way fluid–structure interaction (FSI) model of the nozzle–potato system was built to study waterjet–potato mechanical interactions. Surface stress distribution under waterjet impact was analyzed, and jet dynamic pressure was mapped to solid stress via FSI interface load transfer. Simulations revealed that with a 25° scattering angle, 200 mm standoff distance, and 5 MPa pressure, the maximum shear stress at potato surface characteristic points was 0.032 MPa—within the 0.025–0.04 MPa target range and matching potato skin–substrate peeling strength threshold. This confirmed the energy–mechanical response coordination, validated by experiments. The research results can provide an effective technical reference for potato peeling processing.

In pigeon farming, manure cleaning is predominantly manual, a method that is both slow and costly, and exposes workers to harsh conditions. Addressing these challenges, this paper introduces a cleaning robot for pigeon farms utilizing magnetic strip navigation combined with RFID signal recognition and derives the magnetic-navigation control model. This method can improve operational stability and accuracy. Given the farm’s unstable environment, a control algorithm based on fuzzy PID with Kalman filtering was developed. This algorithm mitigates input disturbances and measurement noise by integrating Kalman filtering into the fuzzy PID feedback loop, thereby refining signal accuracy. Numerical simulations conducted in Matlab/Simulink demonstrate that the inclusion of Kalman filtering reduces the time of target signal tracking by nearly 1 s compared to fuzzy PID and by almost 2 s relative to standard PID under identical disturbances. Experimental tests confirm that this algorithm significantly improves the robot’s operational stability and reduces magnetic-navigation deviation, underscoring its advancement and practicality over traditional PID and fuzzy PID methods.

Peeling is essential in potato processing, yet conventional assessment methods face issues like inefficiency and environmental concerns. This study proposed a hyperspectral imaging approach combined with dual-threshold segmentation to quantify potato peeling rates. This method was in the preliminary research stage. The objectives of this work were to experimentally validate the feasibility and accuracy of this method. A hyperspectral system captured images of potatoes after water-jet peeling. By analyzing spectral data from peel and flesh regions and applying principal component analysis, the key wavelength of 592 ± 20 nm was identified, where the reflectance difference between flesh and peel was most pronounced. Grayscale images derived from this band were processed via median filtering and dual-threshold segmentation to differentiate flesh from peel. The peeling rate was calculated as the pixel ratio of flesh to total potato area. In validation tests, the calculated peeling rates showed an average absolute error of 0.69% compared to manual measurement, confirming the potential and feasibility of this technique. The proposed method offers a promising, non-destructive, and eco-friendly alternative for monitoring peeling quality in agricultural processing, though further research is warranted.