Poor shelling quality degrades the performance and profit of the peanut industry. The Hertz theory and the wear resistance experimental method were applied to identify a highly wear-resistant material guaranteeing a low mechanical damage rate (MDR) of peanut kernels. The Box–Behnken design method was applied in the experiment to illustrate the influence of the material’s elastic modulus (X₁), radius of curvature of the key parts (X₂), rotating speed of the shelling drum (X₃), and clearance between the shelling drum and concave screen (X₄) on MDR and shelling efficiency (SE). Depending on the analysis of variance, the weights of the influential factors were observed as X₁ > X₃ > X₄ > X₂. The mathematical models of MDR and SE were derived from the least squares’ method, and four-dimensional slice diagrams of the three most significant factors were used to illustrate the trends of MDR and SE. A multi-objective analysis provided the optimal combination of parameters as: X₁ = 10 MPa, X₂ = 12.77 mm, X₃ = 277.48 r/min, and X₄ = 24.24 mm, yielding MDR = 4.89% and SE = 97.91%. The results were further verified by a production trial test, proving that the proposed solution with the selected material, machine design, and working parameters were effective in improving peanut shelling quality.

The quality of walnuts deteriorates owing to the poor quality of the shelling equipment. The improvement of shelling quality is urgently required for walnut processing. In this study, systematic research was carried out on the changes in walnut mechanical properties, mechanical model of walnut shelling, and the key parameters of the equipment. The key parameters were determined as the angles of the shelling conic roller (X₁), speeds of the shelling conic roller (X₂), clearance between the shelling conic roller and the static roller (X₃), and the moisture content of walnuts (X₄). The Box–Behnken design method was used for the experimental design, an analysis of variance was applied to determine the graded significance of each variable on the rate of high-quality kernel (RHQK) and rate of shell rushing (RSC), and the multi-objective optimization method was used to obtain the optimal parameters for RHQK and RSC. The ranking of factors affecting RHQK and RSC were: (X₃) > (X₄) > (X₂) > (X₁) for RHQK, and (X₂) > (X₃) > (X₄) > (X₁) for RSC. The ranks of significant interactive effects among the factors were as follows: (X₁ X₂) > (X₂ X₃) for RHQK and (X₂ X₃) > (X₃ X₄) > (X₂ X₄) for RSC. The multi-objective optimization results showed that the optimal combination was $X_1$ = 15.83°, $X_2$ = 17.93 rpm, $X_3$ = 45 mm, and $X_4$ = 9.5%, yielding RHQK = 84.54%, and RSC = 99.15%. The verification test of the optimal results further illustrates the accuracy of the optimization. The obtained results showed that the quality of walnut shelling can be improved by adjusting the moisture content of walnuts and optimizing key parameters of the equipment. This method also represents a potential solution for improving the shelling quality of other nuts.

This study proposes a YOLOv5 inspection model based on polariser filtering (PF) to improve the recognition accuracy of the machine vision inspection model for tea leaf shoots when operating under intense outdoor light. To study the influence of the polariser parameters on the quality of the tea shoot image datasets, we improved the YOLOv5 algorithm module, inputted the results obtained from the spatial pyramid pooling structure in the backbone module into the neck module, set the up-sampling link of the neck module as a low-level feature alignment (LFA) structure, and used a bounding box similarity comparison metric based on the minimum point distance (mpdiou) to improve the accuracy of the YOLOv5 detection model. The mpdiou loss function is used to replace the original loss function. Experimental results show that the proposed method can effectively address the impact of intense outdoor light on tea identification, effectively solving the problem of poor detection accuracy of tea buds in the top view state. In the same identification environment, the model mAP50 value increased by 3.3% compared to that of the existing best mainstream detection model, and the mAP50-90 increased by 3.1%. Under an environment of light intensity greater than $5\times {10}^4 \mathrm{L}\mathrm{u}\mathrm{x}$, the proposed YOLOv5s+LFA+mpdiou+PF model reduced the leakage detection rate by 35% and false detection rate by 10% compared to that with YOLOv5s alone.