In the process of orchard mechanization, passability serves as a crucial criterion for evaluating the effectiveness of the chassis. To address the adaptability of hilly and mountainous multifunctional work machines to complex terrain, a theoretical analysis was conducted to assess the chassis’ performance under three key working conditions: climbing, crossing obstacles, and crossing trenches. Using kinematics, the theoretical maximum climbing angle, maximum obstacle height, and maximum trench width were calculated to be 35.8°, 170.4 mm, and 427 mm, respectively. Additionally, the passability of the chassis model was simulated under these working conditions in different soil environments using RecurDyn dynamics software. Post-processing techniques were employed to extract time characteristic curves for parameters such as center-of-mass velocity, pitch angle, offset, lateral inclination angle, and longitudinal displacement, providing valuable insights into how these parameters changed during chassis movement. The results revealed that the maximum gradient for slope climbing was 30°, the maximum height for obstacle crossing was 150 mm, and the maximum width for trench crossing was 400 mm. The prototype was then tested under these theoretical and simulated conditions in the field, and its ability to smoothly traverse slopes with a 35° angle in first gear, climb vertical obstacles up to a height of 200 mm, and pass through trenches with a width of 430 mm was demonstrated. The crawler chassis exhibited stable performance within the design parameters, aligning closely with the simulated and theoretical expectations. Overall, this study provides valuable theoretical insights for the structural design of multipurpose chassis suitable for orchards in hilly and mountainous regions.

To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-used watermelon peel crusher and seed-used watermelon peel model were constructed, and the model’s parameters were calibrated. Subsequently, the discrete element method (EDEM2022) was employed to investigate the effects of spindle speed (MSS), the number of hammers (NCB), and feeding volume (FQ) on the pulverizing process. Multivariate nonlinear regression prediction models were developed for the percentage of pulverized particle size less than 8 mm (P_sv), pulverizing efficiency (G_e), and power density (P_pd), followed by the analysis of influencing factors and prediction models using ANOVA. The multiobjective optimization of the prediction model utilizing the improved hybrid metacellular genetic algorithm CellDE resulted in solutions of 90.02%, 89.57%, and 8.35 × 10⁻³ t/(h-kw) for P_sv-opt, G_e-opt, and P_pd-opt, respectively. The corresponding optimal interaction values of MSS, NCB, and FQ were determined to be 1500 r/min, 108, and 150 kg/min. Finally, a prototype test was conducted by combining the optimal factor interaction values, yielding statistically calculated values of 96.63%, 92.37%, and 7.76 × 10⁻³ t/(h-kw) for P_sv-pr, G_e-pr, and P_pd-pr, respectively. The results indicate that the optimized values of P_sv-opt, G_e-opt, and P_pd-opt models have an error of less than 8% compared to the statistically calculated values of the prototype test and outperform the values of P_sv-ori, G_e-ori, and P_pd-ori obtained under the original parameters.

In order to enhance the comprehensive processing quality and production efficiency of seed melons, a seed melon crushing and seed-extraction separator has been developed and designed. Aiming at the issues of high impurity rate and scratch rate of melon seeds in the process of seed–flesh separation, the structure and parameters of the seed–flesh separation device were optimized in this study by simulation analysis and field testing. The simulation model of melon seed, melon flesh, and the seed–flesh separation device based on the discrete element method (DEM) was established, and the simulation parameters were calibrated. Subsequently, the melon seed impurity rate (G₁) and the melon seed scratch rate (G₂) were used as the evaluation indexes. The single-factor simulation test was carried out on the separation roller speed (A). The spacing between the scraper and the screen (B), the separation roller scraper inclination angle (C), and the influence rules of each factor on the separation effect of the seed–flesh were obtained. Finally, the three-factor and three-level orthogonal test was carried out. Using the method of ANOVA and multi-objective optimization, the optimal working parameters of the device were obtained as A-117.53 r/min, B-5 mm, and C-10°, at which time the optimal evaluation indexes were G₁-5.59% and G₂-2.85%. The prototype test was carried out with the optimization results. The values of G₁ and G₂ were measured at 5.71% and 2.91%, respectively, and the relative errors with the simulation values were 2.15% and 2.11%, respectively, which were basically the same between the simulation model and the prototype test. The results indicate that the designed separation roller speed, spacing between the scraper and screen, and separation roller scraper inclination angle can meet the requirements of seed–flesh separation in the seed melon crushing and seed-extraction separator. The results of the DEM study can provide a reference for the optimal design of the seed–flesh separation device.

Quality control of fresh Lycium barbarum during storage presents significant challenges, particularly regarding the unclear relationship between quality characteristics and storage conditions. This study analyzes the changes in qualitative and structural characteristics, including fruit hardness, soluble solid content (SSC), titratable acidity (TA), and vitamin C (Vc), under various storage conditions (temperature, duration, and initial maturity). We employed optimized Latin hypercubic sampling to develop radial basis function neural networks (RBFNNs) and Elman neural networks to establish predictive models for the quality characteristics of fresh wolfberry. Additionally, we applied the Particle Swarm Optimization (PSO) algorithm to determine the optimal solution for the constructed models. The results indicate a significant variation in how different storage conditions affect the quality characteristics. The established RBFNN predictive model exhibited the highest accuracy for TA and Vc during the storage of fresh wolfberry (R² = 0.99, RMSE = 0.21 for TA; R² = 0.99, RMSE = 0.19 for Vc), while the predictive performance for hardness and SSC was slightly lower (R² = 0.98, RMSE = 385.78 for hardness; R² = 0.94, RMSE = 2.611 for SSC). Multi-objective optimization led to the conclusion that the optimal storage conditions involve harvesting Lycium barbarum fruits at an initial maturity of 60% or greater and storing them for approximately 10 days at a temperature of 10 °C. Under these conditions, the fruit hardness was observed to be 15 N, with SSC at 17.5%, TA at 1.22%, and Vc at 18.5 mg/100 g. The validity of the prediction model was confirmed through multi-batch experimental verification. This study provides theoretical insights for predicting nutritional quality and informing storage condition decisions for other fresh fruits, including wolfberries.

This study aimed to analyze the relationships between the electrical properties of fresh seed melon pulp and storage conditions, thereby providing a rapid electrical method for quality evaluation. The electrical parameters of fresh seed melon were measured using the parallel-plate electrode method at different storage temperatures of 4 °C, 8 °C, 12 °C, 16 °C, and 20 °C, and storage times of 0, 2, 4, 6, and 8 h. The relationships between electrical parameters and quality attributes at different frequencies were further investigated. An adaptive neuro-fuzzy inference system (ANFIS) model was established to predict the quality characteristics of fresh seed melon, with electrical parameters used as input variables and quality characteristics used as output variables. Eight membership function models were constructed and compared to select the optimal prediction model. The results showed that, with increasing frequency, the impedance (Z), capacitance (Cp), and resistance (Rp) of fresh seed melon decreased, while conductance (G) and reactance (X) increased at different storage temperatures. At different storage times, Z, quality factor (Q), Cp, and Rp decreased, while G and X increased with increasing frequency. The variation ranges of Z, Cp, Rp, G, and X gradually decreased at higher frequencies. Significant correlations between electrical parameters and quality characteristics were observed at the characteristic test frequency of 163.28 kHz. The ANFIS results showed that gauss2mf was the optimal model for predicting cohesiveness (Co, R² = 0.9491), pimf was the optimal model for predicting chewiness (Ch, R² = 0.9595), and gbellmf was the optimal model for predicting resilience (Re, R² = 0.9596). These results indicate that the combination of electrical properties and the ANFIS model has potential for evaluating the quality characteristics of fresh seed melon under the present experimental conditions. This study provides a theoretical basis for quality detection and storage preservation of fresh seed melon and offers a reference for the development of rapid electrical quality-evaluation techniques for seed melon pulp.