Conventional soil-tool interaction has been upgraded to straw-soil-tool interaction due to plenty of straw remains in the field after harvesting. Understanding the straw-soil-tool interaction relationship and quantifying the straw movement and distribution characteristics at various tillage operation parameters is critical for straw management and the design of tillage tools. Here, in order to investigate the interactive effects of key operation parameters on the displacement and burial of straw, a specific field test rig was developed to perform straw movement test. According to the singe-factor test and multifactor interactive experiment, we investigated the effect of straw length, tillage depth and rotary speed on straw movement, and established a mathematical model between operation parameters and straw movement. The results showed that the significant order of the influence on the displacement and burial of straw was as follows: the tillage depth, the straw length, the rotary speed. As determined by response surface analysis, the optimal combination of parameters for straw incorporation was straw length of 5 cm, tillage depth of 13 cm, and rotary speed of 320 rpm, and the corresponding straw burial rate and straw displacement were 95.5% and 27.6 cm, respectively. The relative errors of the optimization results are less than 5%. These results indicated that the mathematical model can be used to predict and evaluate straw movement. Therefore, it is feasible to enhance the straw incorporation performance by a reasonable setting of operation parameters, which may provide a comprehensive strategy to improve the working quality of tillage tools.

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.

Aiming at the problems of high missed-pickup rate and crushed rate caused by different types of peanut harvester pickup drums, by tracking a field comparative experiment from 2020 to 2021, the missed-pickup rate, missed-pickup quality distribution, crushed rate and quality distribution rule of pods with different degrees of crushing observed in two peanut varieties with different pickup drums were studied. Three kinds of pod-pickup drums were set up: axial flow (T1), tangential flow (T2) and tangential–axial flow (T3), and they were installed on the pickup harvester to form three kinds of pickup and harvest test stands. At the same time, a control test stand (CK) without pod-pickup drums was set up. Peanut varieties “Shanhua 9” and “Huayu22” and 10 water content levels were used. Using Origin2018 software, the missed-pickup rate, missed-pickup quality distribution, crushed rate and the quality distribution of pods with different degrees of crushing were statistically analyzed for the two peanut varieties harvested by different pickup drums. The results showed that the average missed-pickup rate was T1, T2 and T3, in order from small to large. The order of pickup quality from more to less was small pods, medium pods and large pods, which indicated that small pods were more likely to be missed. The average pod crushed rates for T1, T2 and T3 for Shanhua 9 were 2.06%, 2.19% and 2.45%, and those of T1, T2 and T3 for Huayu22 were 1.74%, 1.91% and 2.23%, so the order of pod crushed rates from small to large was T1, T2 and T3. At the same time, the crushed rate of T1, T2 and T3 to the two varieties decreased obviously with the decrease in water content. After the T1 test, the proportion of medium and severely crushed pods was the least, followed by T2, and the highest was T3. At the same time, the higher the water content, the higher the proportion of medium and severely crushed pods, and the proportion of medium and severely crushed pods after the Huayu22 test was less than that of the Shanhua 9 test.

The drying process is crucial for maintaining the quality of peanut pods and mitigating the risk of mold proliferation. The aim of this study was to investigate the kinetic characteristics of a synergistic intermittent microwave and a hot-air-drying technique, with an emphasis on enhancing efficiency and reducing energy consumption. A comprehensive analysis was performed on key parameters—including moisture content, moisture ratio, drying rate, and effective moisture diffusion coefficient—to elucidate moisture migration during the drying process. The findings indicated that higher microwave power significantly accelerates the drying rate while substantially shortening the drying time for peanut pods. The effective moisture diffusion coefficient was found to range from 0.987 × 10⁻⁹ to 1.227 × 10⁻⁹ m²/s, with the Verma model demonstrating superior accuracy in predicting drying behavior. The experiment achieved a minimum specific energy consumption of 12,535 kJ/kg and a maximum thermal efficiency of 18.1% at a microwave power density of 0.8 W/g combined with a temperature of 40 °C. However, the observed thermal efficiency was lower than that in previous studies, mainly due to the suboptimal regulation of experimental parameters. Future research should focus on optimizing these parameters and further exploring the impact of this drying method on energy consumption to achieve more efficient and sustainable peanut drying.