Studies on the discrete element method (DEM) parameters of Morchella seeds are limited due to their high moisture content and weak inter-particle adhesion. However, accurate DEM simulations are crucial for the design of agricultural machinery. Physical experiments were conducted to measure the fundamental properties of Morchella seeds, and a DEM model was established using the Hertz–Mindlin with JKR contact model. Subsequently, Plackett–Burman, steepest ascent, and Box–Behnken experiments were employed. They were used to analyze the significance of key contact parameters. A second-order polynomial regression model for the repose angle was developed, and significant contact parameters were optimized and calibrated. The results showed that the seed-to-seed rolling friction coefficient, seed-to-seed surface energy, and seed-to-steel rolling friction coefficient significantly impacted the repose angle. The simulation results using the optimized contact parameters closely matched the repose angle measured in physical experiments. The relative error was only 0.16%, validating the accuracy of the parameter calibration.

Using the discrete element method to simulate the interaction between cotton stalks and machinery is an effective approach for analyzing the cotton stalk defibration mechanism and optimizing the structural parameters of cotton stalk defibration equipment. To further improve the accuracy of studies on the interaction between cotton stalk defibration devices and cotton stalks, cotton stalks from typical arid regions in southern Xinjiang were selected as the research object, and a discrete element parameter calibration study was conducted based on the discrete element method. Considering the differences in cotton stalk diameters, two discrete element models of cotton stalks with diameters of 8.5 mm and 10.5 mm were established. Plackett–Burman screening tests and Box–Behnken tests were employed to calibrate and optimize the discrete element contact parameters for cotton stalk models with different diameters. Optimization was carried out using the load responses obtained from mechanical tests of cotton stalks as the target values, and the optimal parameter combinations of the cotton stalk discrete element models were determined. Finally, the calibrated parameters were validated through tensile tests, uniaxial compression tests, and bending tests of cotton stalks. The simulation results show that the relative errors between the simulated and measured maximum loads for the 8.5 mm- and 10.5 mm-diameter cotton stalk models were 1.21% and 0.08%, respectively, indicating good agreement. These results verify the accuracy and reliability of the established cotton stalk discrete element models and provide data support and a theoretical basis for numerical simulation of the defibration process of cotton stalks with different diameters and for the structural optimization of cotton stalk defibration devices.

In seeding operations, fermented seeds with weak surface energy tend to experience problems such as adhesion, seed leakage, and clogging on the seeder’s end. To improve seed distribution uniformity, a three-stage spiral seeding device with “seed filling, transport, and dispersing” functions was designed. Kinematic and dynamic models of fermented seeds under spiral conveying conditions were developed to analyze the motion and force characteristics of the seeds under various working states. A discrete element method (DEM) and multi-body dynamics (MBD) coupled simulation model was established, along with a comprehensive evaluation system for seed distribution uniformity and seeding effectiveness. The influence of spiral speed, pitch, and forward speed of the seeding operation on seed dispersion uniformity and operational efficiency was studied, and optimal structural and operational parameters for the spiral seeder were selected. A prototype of the spiral seeding device was fabricated, and field seeding tests were conducted. The results showed that when the seeder’s rotational speed was 12 r/min, the pitch was 63 mm, and the forward speed of seeder was 3.5 km/h, the seed distribution uniformity index reached 84.53%, with excellent seeding effectiveness and a comprehensive evaluation index of 92.26%. The field application demonstrated good performance, significantly improving both the operational reliability and efficiency of the spiral seeding device.

Conventional ridging and mulching machines struggle to perform effectively in yellow sand substrates due to their loose texture, high collapsibility, and strong fluidity, which compromise ridge stability and operational quality. To address these challenges, this study proposes the development of an integrated rotary tillage, ridging, and film-mulching machine specifically designed to meet the agronomic requirements of tomato cultivation in greenhouse environments with yellow sand substrate. Based on theoretical analysis and parameter calculations, a soil transportation model was established, and the key structural parameters—such as blade arrangement and helical shaft geometry—were determined. A discrete element method (DEM) simulation was employed to construct a contact model for the yellow sand–slag mixed substrate. A combination of single-factor experiments and Box–Behnken response surface methodology was used to investigate the effects of forward speed, shaft rotational speed, and tillage depth on ridge stability and operational performance. The simulation results indicated that a forward speed of 0.82 m·s⁻¹, shaft speed of 260 rpm, and tillage depth of 150 mm yielded the highest ridge stability, with an average of 95.7%. Field trials demonstrated that the ridge top width, base width, height, and spacing were 598.6 mm, 802.3 mm, 202.4 mm, and 1002.8 mm, respectively, with an average ridge stability of 94.3%, differing by only 1.4 percentage points from the simulated results. However, a quantitative traction/energy comparison with conventional equipment was not collected in this study, and we report this as a limitation. The energy consumption is estimated based on power usage and effective field capacity (EFC) under similar operating conditions. Soil firmness reached 152.1 kPa, fully satisfying the agronomic requirements for tomato cultivation. The proposed machine significantly improves operational adaptability and ridge stability in yellow sand substrate conditions, providing robust equipment support for efficient greenhouse farming.

Precision plug-tray sowing requires consistent hole geometry and seed placement, both of which depend on substrate moisture. This study developed a sequential discrete element method (DEM) framework that transfers the substrate particle configuration after punch retraction directly to seed dropping, enabling joint evaluation of hole quality and seed positioning. Equivalent spheres represented the substrate without explicit fibers or liquid bridges; effective contact parameters were calibrated using angle-of-repose (AoR) tests at five dry-basis moisture levels. Moisture content, punch cone half-angle, and pressing speed were jointly optimized by minimizing hole-depth deviation, equivalent hole-diameter deviation, hole-opening roundness deviation, and seed-center offset. For the selected calibration sets, simulated AoR differed from the measured targets by 0.62–1.13%. Using this parameterization, response-surface optimization estimated an optimum of 72.83% moisture, 46.30° cone half-angle, and 104.17 mm s⁻¹ speed. Physical tests at the practical combination of 73%, 45°, and 100 mm s⁻¹ gave respective response values of 0.94 mm, 0.45 mm, 0.100, and 0.57 mm, with EDEM-to-experiment errors of 2.13–8.89%. Comparisons at three selected combinations support joint process evaluation for the tested substrate and tray within the investigated ranges.

Night-time low temperature remains a major constraint on thermal stability, crop safety and energy-efficient operation in winter solar greenhouses, especially when heat release and auxiliary heating are triggered only after the indoor temperature has approached a low temperature threshold. This study developed a temperature prediction-based active–passive heat storage and release system integrating Internet of Things monitoring, liquid neural network (LNN)-based multi-horizon temperature forecasting, heat storage and release circulation, and decision-making control. The LNN achieved the best forecasting performance among the tested models, with MAE/RMSE values of 0.620/0.775, 0.683/0.854 and 0.758/0.948 °C for 12 h, 24 h and 48 h forecasts, respectively, and was embedded into the system for prediction-assisted operation. A continuous 30-day winter test was conducted in two consecutive stages: heat storage and release without predictive control (HS-NPC, days 1–15) and with prediction-assisted operation (HS-PC, days 16–30). During the consecutive-stage winter test, HS-PC showed higher daily minimum indoor temperature and night-time mean temperature than HS-NPC by 1.92 °C and 4.29 °C, respectively, while reducing daily exposure below 13 °C and 10 °C by 55.0% and 98.2%. Stage-based equivalent input-energy evaluation indicated reductions of 17.1% and 34.3% for HS-NPC and HS-PC relative to the corresponding TG reference periods, respectively. Because HS-NPC and HS-PC were tested in consecutive weather windows rather than in fully synchronized parallel experiments, these improvements should be interpreted as stage-based operational benefits supported by the TG reference and outdoor environmental statistics, rather than as completely weather-independent causal effects. These results indicate that integrating temperature forecasting with heat storage and release regulation can improve low-temperature buffering and energy-saving operation in winter solar greenhouses, while further synchronized or weather-normalized validation is still needed.