Peanut is an important oil crop and cash crop, with a wide range of applications in many fields such as the food industry, light industry, and chemical industry. Mechanized shelling is a necessary part of the post-production processing of peanuts, and it is also the key to determining the quality of peanut products. Reducing shelling damage is an effective way to improve the quality and comprehensive benefits of peanut products. Consequently, it is of great significance to strengthen the research on damage reduction in mechanized peanut-shelling. China is a large peanut producer, but the research on mechanized shelling started relatively late, and the existing technology is not compatible with the high-quality shelling requirements of farmers. This paper reviews the status of mechanized peanut-shelling technology, compares the technical characteristics and equipment development of the world’s important peanut producing countries, it summarizes and proposes the suggestions to reduce loss from the aspects of varieties, agronomy, technology, and technical equipment; further deepen innovative research; and strengthen the construction of peanut-shelling socialized service systems. It is expected to provide reference for effectively reducing damage and improving quality of China’s mechanized shelling, and promoting the sustainable development of peanut shelling industry.

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.

In order to address the problems of high mechanical damage rate (MDR) and poor variety adaptability in mechanical peanut shelling, this paper improves a small, flexible arc-plates drum–circular grid bar concave screen-type peanut-shelling device. Firstly, by combining the Hertz theory and the Weibull distribution model, the shelling and separation models of drums of rigid rods and flexible arc-plates were established. Through comparative analysis, it was verified that the latter has a lower MDR and energy consumption and has excellent shelling performance. Then, through single-factor experiments and an Analysis of Variance (ANOVA), the influence laws of peanut moisture content, drum speed, shelling spacing, and hardness of flexible material (silicone) on the MDR and shelling efficiency (SE) were explored. Subsequently, Box–Behnken’s four-factor three-level regression experiments were carried out, and the optimal shelling operation parameters were obtained by using the response surface multi-objective optimization method (RSM) and verified experiments. The results show that when moisture content is 11%, drum speed is 227 rpm, shelling spacing is 24 mm, and silicone hardness is 40 HA, the kernel’s MDR after shelling is 4.73%, which is reduced by 5.51% and the SE is 95.21%, which is increased by 3%. The R² and the Root Mean Square Error (RMSE) of the actual value versus the predicted value of the model were 0.9921, 0.9624, 7.99 × 10⁻², and 3.1 × 10⁻³, respectively. The relevant research provides references for reducing losses, improving quality, and applying new materials for components in mechanical peanut shelling.

In order to enable accurate and efficient rice quality evaluation through full-surface defect detection of brown rice, a detection system based on convolutional neural network (CNN) was developed. A dataset of images of five categories—unhulled, normal, broken, cracked, and insect-bitten brown rice—was collected. Three CNN models, YOLOv5s, YOLOv7, and Faster R-CNN, were evaluated and compared with traditional algorithms including support vector machine (SVM) and back propagation (BP) neural networks. Experimental results showed that CNN-based methods in the present database significantly outperformed traditional approaches, with the YOLOv5s model achieving the best comprehensive performance: 95.80% detection accuracy, 10.90 ms inference time per image, and 92 frames/s processing speed. An improved Rice-YOLOv5s algorithm was further proposed and validated through batch detection experiments, achieving an average recognition accuracy of 96.44% and a processing time of 9.2 ms per image, which is equivalent to approximately 108.7 FPS. This study demonstrates the feasibility of CNN-based brown rice defect detection, with future work directed toward lightweight deployment and multimodal fusion for production-line application.