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.

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.

Strip rotary tillage seeding technology has been widely used in rice–wheat rotation system, benefiting the economy greatly. The purpose of farming is to create a comfortable seedbed environment for crop growth. Therefore, it is necessary to consider the effects of tillage methods on wheat root configuration and growth trend in the research of strip tillage, instead of just focusing on optimizing the shape of tillage tools, sowing methods, and soil fragmentation. To clarify the effects of different tillage methods on crop root growth trends, a two-year wheat planting experiment was carried out. Strip tillage (ST1 and ST2) and full width tillage treatment (FT) were designed, 3D models of root structure and soil on the surface of the seedbed wall were established, and four quantitative indexes were proposed: soil surface roughness of seedbed wall (SR), difference coefficient of root soil space occupation (P), difference coefficient of root angle expansion trend (PA), and difference coefficient of root length expansion trend (PL). The results showed that the cultivation method directly affects the growth trend of wheat roots. The wheat roots tended to grow along the inter-specific direction under ST treatment on the 14th day, and the average P, PA, and PL were as high as 38, 43, and 55, respectively. The SR produced by ST1 treatment was 70% lower than that of ST2 treatment, suggesting ST1 treatment had more serious restrictions on root growth. P, PA, and PL in ST1 treatment were 38.5%, 14%, and 43% higher than those in ST2 treatment within 14 days, respectively. This paper briefly explains the effects of tillage methods on the growth trend of wheat roots, provides new methods and technologies for the rapid and effective acquisition of soil surface information, solved the problem of the trend of root expansion being difficult to quantify, and provided a new direction for the optimization of tillage methods.

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.