Natural fibers and their composites have attracted much attention due to the growing energy crisis and environmental awareness. In this work, a natural lignocellulosic fiber was extracted from cow dung waste and its potential use as reinforcing material in resin-based polymer composites was evaluated. For this purpose, cow dung fiber-reinforced composites (CDFC) were fabricated, and their mechanical and morphological properties were systematically investigated and compared with corn stalk fiber composites (CSFC) and sisal fiber composites (SFC). The results showed that the addition of cow dung fibers reduced the density of the polymer composites, increased the water absorption, and enhanced the impact strength and shear strength. The highest impact and shear strengths were obtained at 6 wt.% and 9 wt.% of fiber loading, respectively, which increased by 23.8% and 34.6% compared to the composite without the fibers. Further comparisons revealed that at the same fiber addition level, the CDFC exhibited better mechanical properties than the CSFC; notably, the CDFC-3 (adding 3 wt.% of fiber loading) had an impact strength closer to the SFC-3. Furthermore, an SEM analysis suggested that the cow dung fibers exhibited a rough and crinkly surface with more node structures, and presented good interfacial bonding with the composite matrix. This work revealed that cow dung fibers are a promising candidate as reinforcement for resin-based polymer composites, which promotes an alternative application for cow dung waste resources in the automotive components field.

In precision seeding, the final displacements of the seeds are determined as a working result of the profiling mechanism, opener, seed-metering device, covering apparatus and compacting machine. For a better understanding of the disturbance of seed displacement during soil covering and compaction in the actual working process, experiments and simulations have been performed. In this paper, a type of soybean seeding monomer was taken as the research object, and a soil bin test of soil covering and compacting was executed. The experimental results showed that the traction velocity and the open angle of the covering discs had a significant influence on the changes in the horizontal and vertical displacements of seeds during the soil covering processing. With an increasing traction velocity, the vertical displacements of seeds increased after soil covering; in contrast, the horizontal displacements decreased. When the covering apparatus had a larger open angle it had a smaller disturbance influence on the soil. Therefore, with an increase in the opening angle, the changes in the vertical and horizontal displacements of seeds showed a decreasing trend. Inversely, in the process of compacting, the forward velocity had little effect on the three-dimensional displacement change in the seeds after compacting. The analysis model of the precision seeding unit was established based on the coupling model of the DEM (discrete element method) with MBDs (multi-body dynamics). The process of soil covering and compacting was simulated and analyzed. The comparison between the experimental results and the simulated results showed that the trend was similar, and the two results were close. Thus, the feasibility and applicability of the coupling method were verified. It also provided a new method for the design and optimization of covering and compacting components of a precise seeding monomer.

Due to the complex shape of corn kernels and the large difference in size, in order to establish a more realistic model of corn kernels, this paper tests and analyzes two representative corn varieties planted in Northern China, and each variety is divided into high and low moisture content as the research object. According to its natural shape, the corn kernels are subdivided into four categories: wide horse-tooth shape, narrow horse-tooth shape, pyramid shape and spherical shape. The geometric parameters are tested and analyzed, and the correlation between the characteristic dimensions is obtained. Through the Rocky software, the polyhedral modeling method was used to establish the corn kernel models of different shapes, and the contact parameters and mechanical parameters were measured and calibrated, which were used as the basis for the modeling of corn kernel groups. Finally, through the simulation analysis of the corn kernel accumulation process and the comparison of the test results, the influence of different contact parameters on the simulation test results is explored, and the influence trend of the simulation results is analyzed. The feasibility and accuracy of the corn kernel population model established by the polyhedron method used in this paper are verified, which provides a reference for studying the simulation parameters of corn kernel population modeling.

Aiming at solving the problem of a wide variety of crop planting and addressing the concept of precision agriculture, a pneumatic universal seed-metering device suitable for corn and soybean was designed. According to the physical size of the above two crops crop planting, a seeding plate, a hole, and a guide tube were designed. The pressure distribution inside the seeding plate was studied, when the pressure, the diameter of the hole, and the rotation speed of the metering plate changed. Through the coupling simulation method of DEM and CFD, the effects of the air suction hole diameter, the air pressure intensity, and the seeding plate speed on the seeding performance were explored. The results showed that when the air suction hole diameter was 5.9 mm, the air pressure intensity was 3.5 kPa, and the seeding plate speed was 23.8 r/min, and the performance of corn seeding was the best, among which the seeding qualification index was 95.35%, the replay index was 1.45%, and the missed seeding index was 3.23%. When the air suction hole diameter was 6.1 mm, the air pressure intensity was 3.5 kPa, and the rotation speed of the seed plate was 24 r/min, the performance of soybean sowing was the best, in which the sowing qualification index was 95.76%, the reseeding index was 3.47%, and the missed sowing index was 0.77%. The bench verification test and the comparative test were carried out. The results showed that the seed-metering device had good seeding performance and could be applied to the general seeding operation of corn and soybean.

In this paper, the Multisphere (MS) models of three varieties of Cyperus esculentus seeds are modeled based on DEM. In addition, for comparison, other particle models based on automatic filing in EDEM software are also introduced. Then, the direct shear test, piling test, bulk density test, and rotating hub test are used to verify the feasibility of particle models of Cyperus esculentus seeds that we proposed. By comparing the simulated results and experimental results, combined with the CPU computation time, the proposed particle models achieved better simulation accuracy with fewer filing spheres. According to simulation results, some limitation was present when using one single verification test; varieties of verification tests used could improve the verification reliability, and a more appropriate particle model could be selected. Additionally, the issue of multicontact points in the MS model was studied. The Hertz Mindlin (no slip) (HM) model and Hertz Mindlin new restitution (HMNR) model were both considered in simulations for comparison. The rotating hub test and particle–wall impact test were used, and the influences of multiple contact points on the motion behavior of individual particles and particle assemblies were analyzed. Simulation results showed that the multiple contact points affected the motion behavior of individual particles; in contrast, the influence of multiple contact points on the motion behavior of the particle assembly was insignificant. Moreover, the relationships between moisture content of seeds and Young’s modulus, Young’s modulus, and the number of contact points were also considered. Young’s modulus decreased with increasing moisture content. The number of contact points increased with a decreasing Young’s modulus.

Soil nutrients are essential for plant growth, and it is crucial to accurately detect their levels. However, current methods for detecting soil nutrients still have low accuracy and high costs. In this study, we propose a multi-source fusion feature approach that combines multispectral image technology with electronic nose gas response technology to achieve rapid, high-precision, and cost-effective detection of soil nutrients, including soil organic matter (SOM), total nitrogen (TN), available potassium (AK), and available phosphorus (AP). To begin, a multispectral camera was used to collect spectral images and extract seven index features. Additionally, an electronic nose was used to collect soil pyrolysis gases to obtain the response curve and extract seven response features. These two sets of features were effectively fused to form a 106 × 98 fusion feature space. Secondly, principal component analysis (PCA) and Pearson correlation coefficient (PCC) algorithms were used to optimize and reduce the dimensionality of the fused feature space. Finally, a predictive model of the relationship between the feature space and the nutrient content of the soil samples was established using the random forest (RF) and partial least squares regression (PLSR) algorithms. The predictive performance of the model was used to evaluate the accuracy of soil nutrient detection. The results showed that the PLSR modeling of the optimized feature space of PCA achieved high accuracy in predicting the levels of main soil nutrients, with R² values for SOM, TN, AK, and AP of 0.96, 0.95, 0.84, and 0.73, and the RMSE values of 0.56, 0.07, 3.21, and 3.70, respectively. Compared to using only electronic nose gas response technology, the soil nutrient detection with multi-source data features method in this study not only improved the accuracy for SOM and TN but also for metal elements AK and AP in soil. The spectral index features proposed in this study were able to compensate for the limitations of the electronic nose response features, and modeling with the fusion feature space resulted in an accurate prediction of SOM, TN, AK, and AP levels.