In order to solve the problems of high impurity rate and large loss rate in the whole fresh peanut harvesting and production process in hilly areas of China, the method of computational fluid dynamics (CFD) and discrete element (DEM) coupling is used to examine the gas–solid two-phase simulation of the cleaning device of the crawler fresh-peanut-picking combine harvester. In addition, the Box–Behnken test method is used to analyze the influence of different parameters on the impurity content and loss rate of materials in the cleaning process by taking the fan speed, feed amount, and air inlet angle as the test factors, and the optimal combination of working parameters is sought. The simulation results show that when the fan speed is 2905.07 r/min, the feed rate is 0.80 kg/s, the air inlet angle is 43.14°, the impurity content is 7.32%, and the loss rate is 4.78%. Compared with the simulation test results, the impurity content is increased by 0.68%, and the loss rate is increased by 1.24%, which verifies the reliability of the simulation model, and the research results provide some technical support for the improvement of the cleaning device in the later stages.

To address the gap in research on the mechanical properties of peanuts in the harvesting period, the mechanical properties of peanut seedling vines, pods, and peduncles were studied during the peanut harvesting period. The moisture content of peanut pods, peduncles, and seedling vines was measured, yielding values of 36.03%, 66.76%, and 77.95%, respectively. The tensile characteristic parameters between the pods and peduncles, peduncles and peduncles, and the seedling vines and peduncles during the harvesting period were determined using an electronic universal testing machine at three different loading speeds: 10 mm/min, 20 mm/min, and 30 mm/min. The test results indicated that the peduncle–peduncle, peduncle–pod, and peduncle–seedling vine detachment forces were 19.91 N, 17.41 N, and 8.62 N. The mechanical properties of peanuts during the harvesting period differed from those of peanuts that were excavated and sun-dried. Peanut-digging and turning machines should be designed based on the detachment force required to separate the peduncles from the pods, which, at a loading speed of 20 mm/min, is 17 N. This examination of the mechanical properties of peanuts during the harvesting period could have significant practical implications and a lasting influence on enhancing the efficiency and quality of peanut harvesting, refining harvesting machinery design, and advancing agricultural modernization.

In view of the problem that peanut harvesting in hilly areas mainly involves fresh food, and that the peanut-picking purity rate is low and there is high breakage in the peanut-harvesting process, key components such as the picking system of a fresh-peanut-picking crawler combine harvester, the picking tooth, and the concave screen were designed, and ANSYS Workbench 2020 software was used to check the reliability of the picking roller under working conditions in hilly areas. In the process of equipment operation, the picking purity rate and breakage rate were the main evaluation indexes, and the Box–Behnken test method was used to study the speed of the peanut-picking roller, the feeding amount, and the picking gap as the test factors. The results showed that the picking purity rate is 98.95%, with an error margin of 0.98%, compared to the predicted value under the conditions of 342 r/min speed, 0.75 kg/s feeding amount, and 32 mm picking gap. The breakage rate is 4.23% and the error is 0.4% compared with the predicted value, indicating that the optimized model is reliable and predictive. This study provides a theoretical basis for the optimal design of the peanut-picking system of peanut-picking combine harvesters in hilly areas.

To rapidly dry peanut pods and effectively address mold issues resulting from rainy weather, this article provides an analysis of the current research status of global peanut digging, inverting, and harvesting technologies. Based on a two-stage peanut harvesting model, the operating principles of a high-quality peanut digging-inverter are elaborated upon, and the design of key machine components is discussed. The evaluation metrics for the operation of a high-quality peanut digging-inverter include the rate of vine inverting, the soil content rate, the rate of fallen pods, and the rate of buried pods. Utilizing theoretical analysis, the Box–Behnken experimental design is employed to investigate the operating parameters of these machines, with the tractor’s running speed, the linear speed of the inverting roller, and the linear speed of the conveyor chain serving as the experimental factors in a three-factor, three-level experimental study. A regression model is established to analyze the impact of each factor on operational quality and to comprehensively optimize the influencing factors. The experimental results indicate that all factors significantly affect the rate of vine inverting. The tractor’s running speed (X₁) and the linear speed of the inverting roller (X₃) significantly influence the rate of buried pods, while the linear speed of the conveyor chain (X₂) does not have a significant effect on this rate. Similarly, the tractor’s running speed (X₁) and the linear speed of the inverting roller (X₃) significantly affect the rate of fallen pods, whereas the linear speed of the conveyor chain (X₂) does not. Furthermore, the linear speed of the conveyor chain (X₂) and the linear speed of the inverting roller (X₃) significantly impact the soil content rate, while the tractor’s running speed (X₁) does not. The optimal combination of operating parameters is a tractor running speed of 1 m/s, a conveyor chain linear speed of 1.3 m/s, and an inverting roller linear speed of 2.1 m/s. Under these conditions, the high-quality peanut digging-inverter achieves a vine inverting rate of 89.29%, a buried pod rate of 0.31%, a fallen pod rate of 0.74%, and a soil content rate of 8.11%. The experimental values for each evaluation index exhibit relative errors of 1.14%, 6.45%, 1.35%, and 0.13% compared to the optimized values provided by the Design-Expert software. The findings of this research will facilitate the development of hardware conditions for the future intelligent and information-based harvesting of peanuts, thereby establishing a solid foundation for the high-quality initial processing of oilseeds.

Peanut (Arachis hypogaea L.) is an important oil and economic crop, widely cultivated across various regions, with China leading global peanut production. The mechanization of peanut harvesting plays a crucial role in advancing the entire peanut industry. Half-feed peanut combine harvesters, characterized by high operational integration, can complete the harvesting of mature peanuts in a single pass and are widely used in major production areas such as Henan and Shandong in China. This paper summarizes the development history of half-feed peanut combine harvesters in the context of current peanut-planting and harvesting models, outlining their main types and operational characteristics. It also reviews the information technologies applied to these machines, including autonomous driving, harvesting quality detection, and adaptive control of operational parameters, summarizing their main types and working principles. Based on the current demand for intelligent and automated operations, the paper analyzes the main issues and development strategies for half-feed peanut combine harvesters, providing a theoretical basis for their development and the advancement of the peanut industry.

The cutting of leaf stems is a critical step in the mechanized harvesting of tuber mustard (Brassica juncea L.). This study focuses on the calibration of parameters for the discrete element model of mustard leaf stems to visualize the cutting process and facilitate numerical simulations. Intrinsic material properties were measured based on mechanical testing, and EDEM2022 simulation software was utilized to calibrate the model parameters. The Hertz–Mindlin (no-slip) model was employed to simulate the stacking angle of mustard leaf stems, and the contact parameters for the discrete element model were determined using a combination of two-level factorial design, steepest ascent, and CCD (central composite design) tests. The results showed that the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction for the leaf stems were 0.45, 0.457, and 0.167, respectively, while for interactions between the leaf stems and the working parts, these values were 0.45, 0.55, and 0.175, respectively. Based on the Hertz–Mindlin with bonding model, the primary bonding parameters were calculated, and a BBD (Box–Behnken design) test was applied for optimization. The comparison between the simulation and experimental results showed that the relative error in the maximum shear force was within 5%, indicating that the calibrated model can serve as a reliable theoretical reference for the design and optimization of tuber mustard harvesting and cutting equipment.