The implementation of scientific cultivation practices on soda saline–alkali land plays a pivotal role in safeguarding food security and promoting sustainable agro-economic development at the regional scale. However, there exists a critical knowledge gap regarding the optimization of tillage strategies for rain-fed maize (Zea mays L.) cultivation across heterogeneous saline–alkali soil matrices. This study selected meadow alkaline soil, saline meadow soil, and mild saline–alkali soil under the typical micro-landscape morphological characteristics of soda saline–alkali soil in the Songnen Plain as experimental plots. Under three tillage methods, namely no tillage (NT), rotary tillage + no tillage (RT), and subsoiling + rotary tillage + no tillage (SRT), the effects of the tillage methods on the soil physical properties at the seedling stage, root development at the V6 stage, and yield at the R6 stage during the process of cultivating maize in different types of soils were analyzed. The research results showed that compared with NT and RT, the SRT treatment better improved the physical properties, such as penetration resistance and the bulk density in micro-spaces (0–40 cm), of different soil types. The SRT treatment had a positive impact on the root development of maize seedlings in saline meadow soil and meadow alkaline soil. In terms of yield, compared with the NT treatment, the SRT treatment in meadow alkaline soil and saline meadow soil had a positive effect on the plant height, root dry weight, 1000–grain weight, and grain yield of maize. The increases in maize grain yield were 27.94% and 13.24%, respectively. Compared with NT, the differences in the effects of the SRT and RT treatments on maize yield in mild saline-alkali soil were the smallest, being 6.98% and 4.77%, respectively. The relevant results provide guidance on tillage methods and a theoretical basis for improving the properties of different types of soda saline–alkali soils and increasing maize yield.

Olives are a crucial woody oil crop, the harvesting of which predominantly relies on manual labor, which is characterized by high costs, low efficiency, and challenges in ensuring optimal harvesting timing. The development of an automated ripeness-detection system with high recognition accuracy is of paramount importance for advancing automated olive-harvesting technologies. Traditional detection methods are constrained by susceptibility to environmental interference, limited robustness, and inadequate generalization capabilities. Concurrently, existing deep learning-based detection models face issues such as insufficient feature extraction for small targets and difficulties in deployment due to their need for large numbers of parameters. To address these limitations, this study proposes a lightweight algorithm for detection and grading of olive ripeness based on an Improved YOLOv11n framework. The proposed approach employs YOLOv11n as the baseline model, replaces its backbone network with EfficientNet-B0, and integrates the Large-Scale Kernel Attention (LSKA) mechanism and the Bidirectional Feature Pyramid Network (BiFPN). Experimental validation demonstrated that the enhanced model achieved detection accuracy comparable to that of the original model, attaining a mean average precision (mAP) of 0.918. Furthermore, the model size was reduced to 3.7 MB, a 39.3% reduction, while the computational complexity (GFLOPs) was decreased by 2.4 and the inference time per image was reduced by 0.2 ms. The proposed model exhibits significant advantages in terms of lightweight design and improved detection efficiency, demonstrating substantial potential for practical deployment. This study provides a valuable reference for the development of automated olive-harvesting technologies.

Given the lack of accurate and reliable discrete element simulation parameters to study the interactions between soft soil and soil casting components after ginseng land cultivation in Northeast China and the design of ginseng land-specific borders, this paper calibrates the relevant model parameters of ginseng soil using the Hertz–Mindlin with JKR contact model in EDEM to standardize the contact parameters between soil particles and between soil and Q235 steel in soft soil after cultivation in ginseng land. Taking the soil particle accumulation angle as the response value, a Box–Behnken design (BBD) was introduced to establish a regression model for the soil accumulation angle; the surface energy, static friction coefficient, rolling friction coefficient, and coefficient of restoration parameters were obtained, respectively, through the optimization of the model, at which time, the simulated value of the soil accumulation angle was 37°, which is a 4% relative error to the actual measured accumulation angle of 35.5°. Taking the sliding friction angle of the soil on the Q235 steel plate as the response value, the regression model of the soil sliding friction angle was obtained based on the BBD. The static friction factor, rolling friction factor, and coefficient of restitution between the soil particles and the Q235 steel were obtained, respectively. Based on the combination of these parameters, the simulated value of the sliding friction angle was 32.2°, which is a 2% relative error to the measured accumulation angle of 31.5°. To verify the accuracy of the optimized simulation parameters, field and simulation tests of soil-throwing components were conducted. The results show that the maximum relative error between the measured value and the simulation value is 5.6% and 3.4%. The error is within an acceptable range, and the simulation test and field test soil-throwing effects are the same, which verifies the accuracy and reliability of the reference soil parameter calibration. The results of the study can be used for discrete element simulation analysis of the interaction between ginseng loam and touchdown components and their structural optimization.

Paddy field leveling is an essential step before rice transplanting. During the operation of a paddy field grader, a common issue is the wrapping of rice straw around the blades, resulting in a low rice straw burial rate. This study focused on analyzing the operating parameters of a disc spring–tooth-combined paddy field grader. A soil–straw mechanism simulation model was created using EDEM 2021 software to simulate the field operation status. Firstly, the single-factor test was carried out, with the working speed, the working depth of the disc cutter roller, and the rotation speed of the cutter roller as the factors and the straw-buried rate (SBR) and the machine forward resistance (MFR) as the test indexes, and the parameter range was optimized. The parameters were optimized by the response surface method (RSM) and machine learning algorithms. The results indicated that the genetic algorithm–back propagation (GA-BP) neural network outperformed other optimization models in terms of prediction accuracy and stability. By utilizing the GA-BP regression model and RSM model for regression fitting, two sets of optimal parameter combinations were obtained. Verification experiments were carried out using two sets of parameter combinations. Taking the average of the experimental results, the simulation results showed that the straw burial rate was 93.47% and the forward resistance was 6487 N for the parameter combinations of RSM, and the straw burial rate was 94.86% and the forward resistance was 6352 N for the parameter combinations of GA-BP; the field experiments showed that the straw burial rate was 92.86% and the forward resistance was 6518 N for the parameter combinations of RSM, and the straw burial rate was 95.17% and the forward resistance was 6249 N for the parameter combinations of GA-BP. The results demonstrated that the GA-BP prediction model exhibited better predictive capabilities compared to the traditional RSM, providing more accurate predictions of the paddy field grader’s field operation performance.

To apply the discrete element method (DEM) to simulate the interaction process between soda saline–alkali soil and subsoiling component in Northeast China, establishing the soda saline–alkali soil particle model and selecting more accurate simulation parameters are important. In this paper, we studied the soda saline–alkali soil of the Songnen Plain in China. First, we studied the geometric shape of soda saline–alkali soil particles and proposed a modeling method for single soil particles based on the multisphere combination method. Considering the cohesion of soda saline–alkali soil particles, the Hertz–Mindlin with JKR (JKR) model was used as the contact model between soil particles. Then, the calibration method was used to obtain simulation parameters of soils that are difficult to obtain experimentally. We conducted soil angle of repose (AoR) tests, the Plackett–Burman (PB) tests, and steepest ascent (SA) tests in turn to perform a sensitivity analysis for microscopic contact parameters and select the parameters that have a significant effect on the response value (static AoR), i.e., soil surface energy, soil–soil static friction coefficient, and soil–soil rolling friction coefficient. Then, the optimal combination of simulation parameters was obtained via the Box–Behnken (BB) tests, using ANOVA to optimize the multiple regression equation. Finally, the optimal parameter combination was verified by the AoR test and the direct shear (DS) test. The results showed that the parameters had good adaptability for the AoR test. However, the simulation results of the DS test were significantly different from the experimental values. Therefore, the contact model needs to be further modified by adding Bonding bonds between soil particles based on the JKR model and further correcting for Rayleigh time step, shear modulus, and surface energy. By comparing the simulation and the experimental results, it was found that the simulation results obtained from both the DS test and AoR test had relatively small errors relative to physical tests, the two trends are the same, and the values are similar. This verified the feasibility and effectiveness of the soda saline–alkali soil particle modeling method and parameter selection proposed in this paper.

To improve the crushing efficiency and crushing pass rate of high-moisture corn ears (HMCEs), a multi-stage crushing scheme is proposed in this paper. A two-stage crushing device for HMCEs is designed, and the ear crushing process is analyzed. Firstly, a simulation model for HMCEs was established in EDEM software (2018), and the accuracy of the model was verified by the shear test. Subsequently, single-factor simulation experiments were conducted, with the crushing rate serving as the evaluation index. The optimal working parameter ranges for the HMCE device were identified as a primary crushing roller speed of 1200–1600 revolutions per minute (r/min), a secondary crushing roller clearance of 1.5–2.5 mm, and a secondary crushing roller speed of 2750–3750 r/min. A Box–Behnken experiment was conducted to establish a multiple regression equation. With the objective of maximizing the qualified crushing pass rate, the optimal combination of parameters was revealed: a primary crushing roller speed of 1500 r/min, a secondary crushing roller clearance of 2.5 mm, and a secondary crushing roller speed of 3280 r/min. The pass rate of corn cob crushing in the simulation test was 98.2%. The physical tests, using the optimized parameter combination, yielded a qualified crushing rate of 97.5%, which deviates by 0.7% from the simulation results, satisfying the requirement of a qualified crushing rate exceeding 95%. The experimental outcomes validate the rationality of the proposed crushing scheme and the accuracy of the model, providing a theoretical foundation for subsequent research endeavors.