As a highly productive rice, ratoon rice is widely planted worldwide, but the rolling of rice stubble in mechanical harvesting severely limits its total yield; based on this, some scholars have proposed rolled rice stubble righting machines. However, limited by the uncertainty of the field environment, the machine’s localization accuracy of the target needs to be improved. To address this problem, real-time detection of rolled rice stubble rows is a prerequisite. Therefore, this paper introduces a deep learning method for the first time to achieve this. To this end, we presented a novel approach to improve a model that is used for the simplification of Mask R-CNN, which does not require any modules to be added or replaced on the original model. Firstly, two branches in the second stage were deleted, and the region proposals output from the stage was used directly as the mask generation region, and segmentation performance was substantially improved after a simple optimization of the region proposals. Further, the contribution of the feature map was counted, and the backbone network was simplified accordingly. The resulting SMR-RS model was still able to perform instance segmentation and has better segmentation performance than Mask R-CNN and other state-of-the-art models while significantly reducing the average image processing time and hardware consumption.

The upright state of pot seedlings in the process of rice mechanized throwing operations has an important influence on the growth rate and yield of rice, and pot seedling uprightness is affected by the influence of soil backfilling during trenching. Due to the complexity of the furrow opener–soil–pot seedling interaction mechanism in the rice pot seedling planting process, the soil backfilling process is difficult to observe. In order to improve the uprightness of pot seedling planting, this paper constructs a soil model and a soil–pot seedling model step by step, based on the discrete element method (DEM), as well as a coupled model of the pot seedling planting system to study the process of furrow opener–soil–pot seedling planting, the reliability of which is then verified. The results showed that the simulation results of the constructed soil model and soil–pot seedling model deviated from the actual calibration results by <6%, and the model could accurately simulate the pot seedling throwing process. The simulation analysis of the trenching process revealed that the soil backfilling process during trenching showed a three-stage evolution pattern of “backfilling-covering-stabilizing”; in addition, the forward speed of the machine was 0.8 m/s, and the falling speed of the seedling discharge cylinder was 3.5 m/s, which made it possible for the model to simulate the pot seedling throwing process accurately. In addition, when the pot seedling with a forward speed of 0.8 m/s and a drop speed of 3.5 m/s fell into the trench after 0.15 s of trenching, its lateral and longitudinal uprightness were 67.0 ± 1.2° and 65.2 ± 1.5°, respectively. After optimization of the structure of the trenchers, the width, depth, and length of the main body were 40 mm, 37.87 mm, and 32.32 mm, respectively, and the lateral and longitudinal uprightness of the pot seedlings increased to 70.0 ± 1.0° and 69.4 ± 0.8, respectively. The coupled model bench validation test showed that its reliability error was <5%. The coupled model provides technical support for the design and parameter optimization of rice planting equipment.

Direct-seeded rice (Oryza sativa) (DSR) is gaining increasing acceptance worldwide because it saves both time and labor. A covering treatment is a simple method to improve early plant growth under direct-seeding conditions. Herein, field experiments were conducted to study the effects of different powdered organic covering materials, i.e., organic fertilizer, seedling substrate, nutrient soil A, and nutrient soil B. In addition, the effects of different covering thicknesses were studied, including 5, 10, 15, and 20 mm, on the germination and early growth of DSR varieties. The two rice varieties Wufengyou 286 and Zhongjiazao 17 were used for this study. The seedling substrate at 15 mm and nutrient soil A at 5 mm covering thickness significantly increased the rate of germination of Wufengyou 286 by 43.93% and 40.26%, respectively, compared to the control treatment. Organic fertilizer, seedling substrate, and nutrient soil A at covering thicknesses of 5 mm significantly increased the germination rate by 42.57%, 38.62%, and 40.10%, respectively, compared to the control. Notably, all the treatments increased the emergence rate, with Wufengyou 286 exhibiting the most pronounced effect. The seedling substrate at 10 mm covering thickness significantly increased the rate of emergence of Wufengyou286 by 15.42% compared to the control. Similarly, the seedling substrate at 10 mm covering thickness gave the best results by increasing the rate of emergence of Zhongjiazao 17 by 10.85% compared to the control. On average, the rate of emergence of Wufengyou 286 and Zhongjiazao 17 under the experimental treatment increased by 11.81% and 6.45%, respectively, compared to the control. The appropriate cover treatment enhanced the early growth of DSR by improving its morphological attributes, such as plant height and dry weight, and regulating the physio-biochemical responses, such as the production of antioxidants and photosynthetic pigments. The seedling growth rate of both varieties decreased as the thickness increased. This study elucidated the effects of different covering materials on the early growth of DSR, which can further guide their actual production.

To address the issues of missed seeding and seed stem jamming—factors that contribute to unstable performance in Pennisetum hydridum planting device, this study optimize the key components of the pre-cut stem planting device and evaluated its operational performance, thereby improving planting quality. Based on the results of preliminary experiments and discrete element method (DEM) simulation analysis, the critical components of the seed-metering device and their key influencing parameters were identified. Single-factor experiments were conducted to determine the appropriate parameter range. These were followed by a quadratic regression orthogonal rotation combination design for parameter optimization and the development of a response model for influencing factors. Finally, validation tests were performed to assess the effectiveness of the optimized parameters. The parameter optimization revealed that the optimal working parameter combination for the seed-metering device was: seed-cleaning rubber roller rotation speed (A) at 62.2 r/min, seed-filling rubber roller rotation speed (B) at 23 r/min, and conveyor belt operating speed (C) at 0.045 m/s. Under these conditions, the theoretically qualified seeding rate (Y₁) reached 92.5%, the missed seeding rate (Y₂) was 2.13%, and the reseeding rate (Y₃) was 5.37%. Validation experiment confirmed that Y₁ = 91.8%, Y₂ = 2.5%, Y₃ = 5.7%, with a relative error of 0.76%. The single-factor experimental results indicated that as A and B increased, Y₁ initially rose and then declined; as C increased, Y₁ gradually decreased. The interaction factor analysis of the orthogonal experimental results showed that the order of influence on seeding quality was: A > B > C. The verification tests confirmed the reliability of the optimized parameter scheme, providing both theoretical basis and technical support for improving the operational performance of the Pennisetum hydridum seed-metering equipment.

The hygroscopic fertilizer particles were characterized based on discrete element simulation, and its accuracy was verified by the simulation of fertilizer discharge. Firstly, the repose angle of the fertilizer particles served as the key metric for assessment. A mathematical model characterizing the relationship between fertilizer moisture content and the repose angle was established, R ² =0.9935; and a mathematical representation of the repose angle for fertilizer moisture content was established, R ² =0.9933. Then, the moisture absorbing fertilizer particles was developed utilizing the integrated Hertz Mindlin with JKR model within EDEM system to account for particle adhesion; A robust correlation model between the repose angle of hygroscopic fertilizers and the discrete element method was established, p <0.0001; Finally, a correlation model between moisture content and significant parameters within the discrete element method was established, drawing upon the models for moisture content repose angle and repose angle discrete element parameters. The moisture-absorbing fertilizer particles model at moisture contents of 2% and 6% were resolved, with a relative error of the repose angle not exceeding 1.42% and a single circle fertilizer discharge error below 8.32%. The results show that the moisture content discrete element significant parameter correlation model can reliably and precisely forecast the discrete element parameters of various moisture content hygroscopic fertilizers. The hygroscopic fertilizer particle model can represent surface interaction characteristics of hygroscopic fertilizer granules. which has guiding significance for the design of precision fertilizer discharge technology device.

The discrete element computer simulation method is an effective tool that enables the study of the interaction mechanism between the fertilizer discharge device. However, the lack of accurate fertilizer models for hygroscopic fertilizer particles (HFP) has limited the application and development of the discrete element method in research precision fertilizer discharge device. Taking HFP as the research object, this research aims to establish the discrete element model of different moisture hygroscopic fertilizer particles, and to develop a method for predicting the discrete element parameters of HFP based on moisture content. The Hertz–Mindlin with JKR discrete element model was selected as the contact model for the HFP. The repose angle of HFP was used as the test index to select nine discrete element models for the HFP. Firstly, a mathematical model characterizing the relationship between fertilizer moisture content and the repose angle was established. Subsequently, the Plackett–Burman test identified the surface energy of hygroscopic fertilizer particles (HFP), the restitution coefficient between fertilizer and PC board, and the shear modulus as significant factors influencing the test index. The value range of the above parameters were determined by the steepest ascent test results. The Box–Behnken test obtained the regression model between the significant factors and the test index. The optimal combination of parameters of 2%, 4%, and 6% moisture contents of HFP were predicted based on the regression model and the HFP repose angle. The parameters were optimized using the repose angle error as the target. In order to further verify the accuracy of the HFP discrete element model, a fertilizer discharging simulation test was conducted. The results show that, compared with the actual fertilizer discharge amount, the simulation fertilizer discharge amount error of different moisture HFP was below 8.32%. The collective results indicated this method could reliably and precisely establish the discrete element model of various moisture content HFP. This model can be applied to the analysis of hygroscopic fertilizer discharging processes and the design of precision fertilizer discharge technology devices.