In a simulation model of the process of corn straw crushing, its physical parameters and the model itself influence the accuracy of the numerical calculations of the discrete element method. This study attempts to improve the simulation accuracy of the crushing process and to find the optimal combination of parameters. Based on the Hertz-Mindlin with Bonding contact model, multiple particle replacement and bonding programs written using Visual Studio were imported through the application programming interface (API) of a discrete element method (DEM) model to establish three particle-bonding materials for a numerical simulation of the crushing process. Using results of mechanical corn stalk tests, DEM simulations of impact fracture, compression fracture, and bending fracture were conducted to determine the optimal combination of parameters. The resultant DEM-parameter combination led to simulation errors of 3.83%, 5.95%, and 7.86% in numerical simulations of impact fracture, bending fracture, and compression fracture of corn stalks, respectively. The performance of the corn stalk DEM using the proposed optimal parameter combination was validated using a 9RS-60 corn stalk crusher, revealing that the numerical simulation error was 8.77%. This study can improve the accuracy of the discrete element method in the simulation of the corn straw breaking process.

To improve the accuracy of discrete element simulation in the process of separating potato–soil mixtures, the contact parameters of sandy soil with 3, 6, 9, and 12% water content were calibrated in DEM simulation using EDEM software simulation. The error of the rest angle between them was used as an index, and the approach of performing only one simulation and multiple Box–Behnken response surface analyses was proposed to determine the optimal parameter combinations. Meanwhile, unconfined compression and direct shear tests were conducted to obtain the parameters of polymer bonds for soil with different water content, and a simulation was carried out using EDEM. The test results show that the significant parameters affecting the rest angle are JKR surface energy, soil interparticle recovery coefficient, and rolling friction factor. The numerical simulation of the rest angle was compared with the physical test, and the maximum relative error between them was 4.72%. The bond parameters of soil with different water content and firmness were obtained and compared with the simulation test, the maximum error was 6.53% for the direct shear test and 8.07% for the unconfined compression test, which proved that the bonding parameters are reliable and provide an effective parametric and theoretical basis for the discrete element simulation of soil particles.

The poor adsorption stability of the current air-suction sunflower seed-metering devices under high-speed operation leads to unstable seed adsorption posture, as well as increases in the multiple and miss indices. In order to improve the stability of seed adsorption, an air-suction sunflower seed-metering device based on structure-assisted stable suction posture was designed, the movement mechanism of the seed absorption process and the seed-feeding process is analyzed, following which mathematical models of the processes are established, and the key factors affecting the seeding performance were determined. Furthermore, the shape parameter equation of sunflower seeds is fitted, depending on the dimensions and shape parameters, which provides the design basis for the key components of the seed-metering device. Moreover, in order to optimize the seed-metering devices structure, taking the depth, width, and length of the seed suction grooves as experimental factors, as well as the qualified, multiple, and miss indices as evaluation indices, an orthogonal regression experiment considering three factors and three levels is carried out using the Box–Behnken experimental design. Finally, multiple regression models are established and extreme value theory is used to optimize the parameters of the influencing factors and obtain optimal parameter combinations. The optimal parameter combination is as follows: width of the suction groove, 10.5 mm; depth, 3 mm; and length, 23 mm. Results from verification experiments revealed the corresponding experimental indicators, the qualified index is 91.63%, the multiple index is 5.63%, and the miss index is 2.74%, thus meeting the requirements of effective sunflower seed sowing.

To address the problems of low pickup rates and high rates of wounds in the mechanised harvesting of potatoes under sectional harvesting conditions, a spring-finger potato picker was designed and its overall structure and working principles were described. The kinematic principle of the spring-finger was analysed based on the picking constraints, and the kinematic parameters of the picker were determined. A response surface Box–Behnken Design test design was used to carry out a quadratic orthogonal rotational test, with the speed of the spring-finger, the forward speed of the machine, and the embedded depth as test factors, and the loss rate and the wounded potato rate as evaluation indicators. The test results were optimised and analysed. When the spring-finger speed was 19.57 r/min, the forward speed was 0.61 m/s and the embedded depth was 71.31 mm, the loss rate was 1.18%, and the wounded potato rate was 5.71%. The optimised data were verified, and the results showed that the loss rate of the spring-finger potato picker was 1.48% and the wounded potato rate was 4.98%, meeting the potato picking and harvesting requirements. The research can provide a theoretical basis and design reference for the development and application of sectional potato harvesting machinery.

PH value is a crucial indicator for evaluating silage quality. In this study, taking maize silage as the research object, a quantitative prediction model of pH value change during the secondary fermentation of maize silage was constructed based on computer vision. Firstly, maize silage samples were collected for image acquisition and pH value determination during intermittent and always-aerobic exposure. Secondly, after preprocessing the acquired image with the region of interest (ROI) interception, smoothing, and sharpening, the color and texture features were extracted. In addition, Pearson correlation analysis and RF importance ranking were used to choose useful feature variables. Finally, based on all feature variables and useful feature variables, four regression models were constructed and compared using random forest regression (RFR) and support vector regression (SVR): RFR model 1, RFR model 2, SVR model 1, and SVR model 2. The results showed that—compared with texture features—the correlation between color features and pH value was higher, which could better reflect the dynamic changes in pH value. All four models were highly predictive. The RFR model represented the quantitative analysis relationship between image information and pH value better than the SVR model. RFR model 2 was efficient and accurate, and was the best model for pH prediction, with $R_c^2$, $R_p^2$, RMSEC, RMSEP, and RPD of 0.9891, 0.9425, 0.1758, 0.3651, and 4.2367, respectively. Overall, this study proved the feasibility of using computer vision technology to quantitatively predict pH value during the secondary fermentation of maize silage and provided new insights for monitoring the quality of maize silage.