The discrete element method (DEM) for modeling the behavior of particulate material is highly dependent on the use of appropriate and accurate parameters. In this study, a seed metering DEM simulation was used to measure, calibrate, and verify the physical and interactional parameters of rapeseed. The coefficients of restitution and static friction between rapeseeds and three common materials (aluminum alloy, acrylic, and high-density polyethylene) were measured using free drop and sliding ramp tests, respectively. The angle of repose was determined using a hollow cylinder experiment, which was duplicated using a DEM simulation, to examine the effects of static and rolling friction coefficients on the angle of repose. Response surface optimization was performed to determine the optimized model parameters using a Box–Behnken design test. A metering device was made with three materials, and rapeseed seeding was simulated at different working speeds to verify the calibrated parameters. The validation results showed that the relative errors between the seed metering model and experiments for the single qualified seeding, missed seeding, and multiple seeding rates were −0.15%, 3.29%, and 5.37%, respectively. The results suggest that the determined physical and interactional parameters of rapeseed can be used as references for future DEM simulations.

To explore the internal driving force of the growth of rice yield per unit area in China, a model based on varying-coefficient production function is proposed in this study, which comes from the idea that the constant elasticity parameters in the Cobb-Douglas production function can be extended to functional forms. Applying such model to economic growth analysis, on the one hand, the dynamic contribution rate of each input factor can be measured, and, on the other hand, the contribution rate of the input factor can be decomposed into net factor contribution rate and interaction factor contribution rate, thus expanding the explanatory ability of growth rate equation. Using such model, the output elasticity of capital and labor in China’s rice yield growth are calculated from 1978 to 2020, and the dynamic characteristics of the contribution rate of capital, labor and generalized technological progress are analyzed. Next, the capital contribution rate is decomposed according to the composition of the total capital. The results show that: (1) The capital elasticity and labor elasticity are indeed not constant in different years. In China, from 1978 to 2020 the value of capital elasticity was between 0.3209 to 0.3589, with a mean of 0.3437, and the value of labor elasticity was between −0.1759 to −0.1640, with a mean of −0.1730. (2) Natural disasters do affect capital elasticity and labor elasticity in rice production. (3) When the annual proportion of crop disasters increases, the contribution rate of interaction between capital and natural disaster (KDR) value is negative, whereas the contribution rate of interaction between labor and natural disaster (LDR) value is positive. (4) Compared with 1978, the generalized technological progress contribution rate (GTPR) of the rice yield growth in China from 1979 to 2020 shows a declining trend in fluctuations, whereas the total capital contribution rate (TKR) shows a rising trend in fluctuations and the total labor contribution rate (TLR) is relatively stable in the same period. Since 2000, capital investment has become the main factor for the rice yield growth per unit area in China, of which machinery, chemical fertilizer, seed and pesticide are the four most important input factors.

In seedling cultivation of hybrid rice, fast estimation of seedling density is of great significance for classifying seedling cultivation. This research presents an improved YOLOv8 model for estimating seedling density at the needle leaf stage. Firstly, the auxiliary frame technology was used to address the problem of locating the detection area of seedlings. Secondly, the Standard Convolution (SConv) layers in the neck network were replaced by the Group Shuffle Convolution (GSConv) layer to lightweight the model. A dynamic head module was added to the head network to enhance the capability of the model to identify seedlings. The CIoU loss function was replaced by the EIoU loss function, enhancing the convergence speed of the model. The results showed that the improved model achieved an average precision of 96.4%; the parameters and floating-point computations (FLOPs) were 7.2 M and 2.4 G. In contrast with the original model, the parameters and FLOPs were reduced by 0.9 M and 0.6 G, and the average precision was improved by 1.9%. Compared with state-of-the-art models such as YOLOv7 et al., the improved YOLOv8 achieved preferred comprehensive performance. Finally, a fast estimation system for hybrid rice seedling density was developed using a smartphone and the improved YOLOv8. The average inference time for each image was 8.5 ms, and the average relative error of detection was 4.98%. The fast estimation system realized portable real-time detection of seedling density, providing technical support for classifying seedling cultivation of hybrid rice.