Search Results (23)

Paddy field leveling is the foundation of high-yield rice cultivation. In response to the current issues of low leveling accuracy and the lack of efficient multi-operation machinery, an Integrated Multi-operation Paddy Field Leveling Machine was designed in this study. This machine can complete soil crushing, stubble burying, mud stirring, and leveling in a single pass. Combined with an adaptive control system based on Global Navigation Satellite System—Real-Time Kinematic (GNSS-RTK) technology, it enables adaptive and precise paddy field leveling operations. To verify the operational performance of the equipment, field tests were conducted. The results showed that the machine achieved an average puddling depth of 14.21 cm, a surface levelness of 2.16 cm, an average stubble burial depth of 8.15 cm, and a vegetation coverage rate of 89.33%, demonstrating satisfactory leveling performance. Furthermore, to clarify the feasibility and superiority of applying this equipment in actual rice production, experiments were conducted to investigate the effects of different field leveling methods on early rice growth, yield, and its components. One-way analysis of variance was employed to examine the differences in agronomic indicators between the different field leveling treatments. The results indicated that using this equipment for paddy field leveling, compared to traditional methods and dry land preparation, can improve the seedling emergence rate, thereby laying a solid population foundation for the formation of effective panicles. It also promoted root growth and development and increased the total dry matter accumulation at maturity, thereby contributing to high yield formation. Over the two-year experimental period, the rice yield remained above 9.8 t·hm⁻². This research provides theoretical support and practical guidance for the further optimization and development of subsequent paddy field preparation equipment, thereby promoting the widespread application of this technology in rice production. Full article

With the advancement of precision agriculture, improving the operational accuracy of agricultural machinery has received increasing attention. The rice transplanter is crucial in this context, as its performance directly affects rice yield. During operation, both the magnitude and stability of the driving wheel slip ratio affect the accuracy of plant spacing, thereby influencing rice yield. However, to date, no control method that can simultaneously stabilize the speed, reduce the slip ratio, and improve the stability of the slip ratio has been proposed for transplanters. To address this issue, this paper proposes a joint speed–slip ratio control method based on an adaptive Student t-kernel maximum correntropy Kalman filter (ASMCKF) and sliding mode control (SMC). First, a Student t-kernel maximum correntropy Kalman filter (SMCKF) is designed to identify the transplanter’s speed, wheel speed, traction force, and rolling resistance in real time, thereby enhancing control system robustness against non-Gaussian heavy-tailed noise in paddy fields. An adaptive kernel bandwidth adjustment method is also introduced for the SMCKF to increase the sensitivity of the cost function to variations in the system state, thereby further improving parameter identification accuracy. Building on this, a joint speed–slip ratio control method is designed based on SMC. Simulation results confirm that the ASMCKF achieves higher identification accuracy than conventional methods when facing non-Gaussian heavy-tailed noise. Field experiment results show that the proposed method can effectively stabilize the transplanter’s speed while significantly reducing the slip ratio and improving the stability of the slip ratio. Full article

Existing rice harvesting models often lack depth or extensibility and are limited in their scope across the agriculture value chain, from crop planting to postharvest handling. A multi-agent system (MAS) offers flexibility and scalability and supports the simulation and modeling of complex real-world scenarios. This paper introduces a novel approach utilizing an MAS to simulate rice harvesting operations (including additional pre- and post-harvesting operations). Initially, a generic MAS was created, and it was then subsequently adapted to the agricultural context of rice farming in Central Japan. The localized MAS consists of agents such as weather, farm, rice centers, fields, crops and multiple agriculture machinery. Additionally, the introduced MAS environment is based on a discrete event simulation that enables communication across various independent agents. The system includes different harvesting schedule policies which determine the harvesting order for multiple paddy fields on specific days. The system was evaluated through two distinct experiments: (i) ‘Model Verification Simulation’, which successfully demonstrated the replication of actual historical farming practices, and (ii) ‘Operational Efficiency Simulation’, which compared the overall farm efficiency under different scheduling policies as well as different environmental conditions (e.g., rainfall). The simulation successfully generated a dataset containing traits and performance indicators that replicate the patterns observed in real-world data, while also approximating the operational behaviors and workflows of actual rice harvesting systems. Future studies could further evaluate the model’s robustness to confirm its practical applicability. Full article

In this study, relevant rice cultivation data were collected through a local survey, and the life cycle assessment (LCA) method was employed to quantify greenhouse gas (GHG) emissions from rice production on consolidated land in the Red River Delta (RRD). Systematic sampling was used in face-to-face interviews with 45 rice farming households in a representative commune of Hai Duong province. Specific GHG emissions were significantly higher in the summer crop (averaged at 11.4 t CO₂-eq/ha or 2.2 t CO₂-eq/t grain) than in the spring crop (6.8 t CO₂-eq/ha or 1.2 t CO₂-eq/t grain). Methane was a dominant GHG emitted from paddy fields, contributing 84% of the total emissions of CO₂-eq in the summer crop and 73% in the spring crop. Fertilizer use and N₂O emissions accounted for 9% of emissions in the summer crop and 16% in the spring crop. Energy consumption for machinery and irrigation added a further 4% and 8%, respectively. Annually, as of 2023, the rice production activities in the RRD release 7.3 Tg of CO₂-eq (100 years), a significant contribution to the national GHG emissions. GHG emissions under alternative scenarios of rice straw management were assessed. This study highlights the role of land consolidation in improving water management, which contributes to lowering emissions. Based on the findings, several mitigation measures could be identified, including improved irrigation practices, optimized fertilizer use, and the promotion of sustainable rice straw management practices. Full article

Farmland boundaries distinguish agricultural areas from non-agricultural areas, providing limits for field operations and navigation paths of agricultural machinery. However, in hilly regions, the irregularity of paddy field boundaries complicates the extraction of boundary information, hindering the widespread use of GNSS-based navigation systems in agricultural machinery. This paper focuses on the paddy field border prior to rice planting and utilizes machine vision and GNSS fusion technology to extract navigation line coordinates. First, the BiSeNet semantic segmentation network was employed to extract paddy field ridges. Second, the camera’s 3D attitude was obtained in real time using an Attitude and Heading Reference System (AHRS). A method and device based on the hydraulic profiling system were proposed to measure the camera’s height relative to the paddy field, providing a dynamic external reference. An improved inverse perspective transformation was applied to generate a bird’s-eye view of the paddy field ridges. Finally, a homogeneous coordinate transformation method was used to extract the navigation line coordinates, with the model and algorithms deployed on the Jetson AGX Xavier platform Field tests demonstrated a real-time segmentation speed of 26.31 fps, pixel segmentation accuracy of 92.43%, and an average intersection ratio of 90.62%. The average distance error of the extracted navigation line was 0.071 m, with a standard deviation of 0.039 m. The coordinate extraction took approximately 100 ms, meeting the accuracy and real-time requirements for navigation line extraction at the rice transplanter’s speed of 0.7 m s⁻¹, providing path information for subsequent autonomous navigation. Full article

Accurate rice row detection is critical for autonomous agricultural machinery navigation in complex paddy environments. Existing methods struggle with terrain unevenness, water reflections, and weed interference. This study aimed to develop a robust rice row detection method by integrating multi-sensor data and leveraging robot travelling prior information. A 3D point cloud acquisition system combining 2D LiDAR, AHRS, and RTK-GNSS was designed. A variable-threshold segmentation method, dynamically adjusted based on real-time posture perception, was proposed to handle terrain variations. Additionally, a clustering algorithm incorporating rice row spacing and robot path constraints was developed to filter noise and classify seedlings. Experiments in dryland with simulated seedlings and real paddy fields demonstrated high accuracy: maximum absolute errors of 59.41 mm (dryland) and 69.36 mm (paddy), with standard deviations of 14.79 mm and 19.18 mm, respectively. The method achieved a 0.6489° mean angular error, outperforming existing algorithms. The fusion of posture-aware thresholding and path-based clustering effectively addresses the challenges in complex rice fields. This work enhances the automation of field management, offering a reliable solution for precision agriculture in unstructured environments. Its technical framework can be adapted to other row crop systems, promoting sustainable mechanization in global rice production. Full article

Accurately and precisely obtaining field crop information is crucial for evaluating the effectiveness of rice transplanter operations. However, the working environment of rice transplanters in paddy fields is complex, and data obtained solely from GPS devices installed on agricultural machinery cannot directly reflect the specific information of seedlings, making it difficult to accurately evaluate the quality of rice transplanter operations. This study proposes a CAD-UNet model for detecting rice seedling rows based on low altitude orthorectified remote sensing images, and uses evaluation indicators such as straightness and parallelism of seedling rows to evaluate the operation quality of the rice transplanter. We have introduced convolutional block attention module (CBAM) and attention gate (AG) modules on the basis of the original UNet network, which can merge multiple feature maps or information flows together, helping the model better select key areas or features of seedling rows in the image, thereby improving the understanding of image content and task execution performance. In addition, in response to the characteristics of dense and diverse shapes of seedling rows, this study attempts to integrate deformable convolutional network version 2 (DCNv2) into the UNet network, replacing the original standard square convolution, making the sampling receptive field closer to the shape of the seedling rows and more suitable for capturing various shapes and scales of seedling row features, further improving the performance and generalization ability of the model. Different semantic segmentation models are trained and tested using low altitude high-resolution images of drones, and compared. The experimental results indicate that CAD-UNet provides excellent results, with precision, recall, and F1-score reaching 91.14%, 87.96%, and 89.52%, respectively, all of which are superior to other models. The evaluation results of the rice transplanter’s operation effectiveness show that the minimum and maximum straightnessof each seedling row are 4.62 and 13.66 cm, respectively, and the minimum and maximum parallelismbetween adjacent seedling rows are 5.16 and 23.34 cm, respectively. These indicators directly reflect the distribution of rice seedlings in the field, proving that the proposed method can quantitatively evaluate the field operation quality of the transplanter. The method proposed in this study can be applied to decision-making models for farmland crop management, which can help improve the efficiency and sustainability of agricultural operations. Full article

The navigation field of agricultural machinery has entered the intelligent stage, but the navigation control performance of paddy field agricultural machinery represented by rice transplanters is not stable in complex environments. Therefore, this study proposes a method to identify navigation deviation patterns based on Deep Belief Network (DBN) and designs an adaptive preview distance control method based on a driver preview model for each deviation pattern. Among them, the deviation pattern identification method is a two-stage algorithm. First, determine whether the current navigation status is abnormal. Then, the classification was refined for different abnormal states. The adaptive control method is divided into two levels. The main regulator calculates the dynamic preview distance according to the current state variable; the sub-regulator calculates the preview distance adjustment value according to the abnormal state degree. In the performance test of the identification method, all the models show excellent stability and accuracy, and the identification speed of the algorithm meets the high frequency of the rice transplanter navigation system. In the performance test of the control algorithm, compared with the static preview distance, the adaptive preview distance control method proposed in this study can effectively suppress the disturbance deviation of the rice transplanter navigation. Full article

Aggregate area expansion is one of the important productivity impacts of agricultural mechanization. This study aims to explore potential new paths to aggregate area expansion through new forms of agricultural mechanization and estimate the relevant effects. Targeting the rapidly developing machinery rice transplanting (MRT) and the attendant centralized rice seedling cultivation (CRSC) in rural China, this article identifies a fresh path for the adoption of machinery technology to increase aggregate crop cultivation area. By analyzing two typical cases from Jiangxi Province, we unmask the mechanism through which MRT and CRSC promote aggregate area. The results indicate that, compared with the traditional method, CRSC makes technological progress in various aspects and significantly improves the supply efficiency of seedlings and the space utilization efficiency of seedling fields. This, in turn, reduces the required seedling area per unit of paddy field and thus substitutes a lot of traditional seedling fields with few modern ones. Under the rotation cropping system, CRSC releases the farming time of the potential previous crops in the saved traditional seedling fields and then increases cropping intensity and aggregate area. In the micro case, the substitution of the traditional method with CRSC can save 0.04 hectares of seedling field by serving 1 hectare of paddy field. The macro simulation results show that CRSC can, at most, increase aggregate crop cultivation area by 1.95 million hectares nationwide, and this is equivalent to an increase of 6.21 million tons of grain and 1.86 million tons of rapeseed. Full article

This study aims to analyze the fatigue life of a PTO (power take-off) gearbox used in a paddy field power chassis. The analysis considers factors such as stress concentration, dimensions, surface quality, and load characteristics affecting fatigue life. A finite element simulation was conducted using the Ansys 2022 software to identify the critical point of the PTO shell. The modified nominal stress fatigue analysis method, incorporating a stress adjustment coefficient, was employed to derive the modified S-N curve. Combined with the measured load data of the PTO bench operation, the load data and the 3D model of the PTO shell were imported into the fatigue analysis software n-code to analyze the fatigue life of the PTO gearbox of a paddy field power chassis and compare it with the prediction results from the traditional stress field strength method. The findings indicate that the optimized stress adjustment coefficient method predicts a fatigue life (31,699 h) closer to the actual operational life (20,000 h) compared to the traditional method (39,151 h). This research contributes to the advancement of the analytical techniques for predicting fatigue life in critical components of agricultural machinery. Full article

In response to challenges such as inadequate driving stability and power in traditional weeding machinery, we designed and investigated a hydraulic chassis tailored for paddy field operations. Utilizing SolidWorks and RecurDyn V9R4 software, we obtained linear driving and steering curves to model and simulate the dynamics of the mower chassis. Through the AMESim software, we further modeled and simulated the hydraulic chassis system, focusing on the hydraulic characteristics of the components relevant to its operation. Subsequently, we developed a hydraulic-driven paddy weeder and conducted tests to evaluate the linear deviation and paddy slip rates. Our findings indicate that the designed hydraulic weeder chassis exhibits commendable dynamic performance and driving stability, with the actual average deviation and paddy slip rates measured at 2.61% and 3.59%, respectively. These results underscore the efficacy of our approach in addressing the challenges inherent in traditional weeding machinery and highlight the potential of hydraulic systems in enhancing agricultural operations. Full article

This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test bed for systematic examination of these effects. Time-domain analysis of the vibration data revealed a predominantly vertical vibration direction, with notably higher levels in sandy loam soil compared to clay loam. A correlation was observed between increased engine size and rotary ploughing speeds, as well as forward speed and elevated vibration amplitudes. Frequency domain analysis pinpointed the primary vibration frequency of the machinery within the 0–170 Hz range, remaining consistent across different operating conditions. Crucially, bench test results indicated that seeding accuracy and dispersion were significantly influenced by vibration frequencies, particularly within the 70–130 Hz range, where a decrease in accuracy and increase in dispersion were noted. A regression model suggested a complex, non-linear relationship between seeding performance and vibration frequency. These insights highlight the necessity for a robust mechanism to effectively address these vibrational impacts. This study paves the way for enhancing the operational efficiency of the rice precision hole seeder, aiming to achieve the design goals of minimized vibrations in the paddy power chassis. Full article

Weeds in paddy fields can seriously reduce rice yield. An intra-row weeding device with double-layer elastic rods was designed, considering the differences in mechanical properties between rice and weeds, which can press weeds into the soil and avoid damaging rice. The elastic force of the elastic rods can be adjusted by changing the position of the regulating mechanism to adapt to different weeding conditions. A measurement experiment was conducted to determine the variation rule of elastic force. The quadratic orthogonal rotation combination discrete element simulation experiment, which used weeding depth and weeding speed as experimental factors, and the amount of soil disturbance and the force of the inner and outer elastic rod in the horizontal and vertical directions as experimental indicators, was conducted to study the interaction between the weeding device and the soil. The optimal weeding parameters were obtained: the weeding depth was 15 mm, the weeding speed was 0.9 m/s. The field experiment, which used the various parameters of the weeding device as experimental factors and the weeding rate and damaging seedling rate as experimental indicators, was conducted to determine the weeding effect. The experimental results showed that the optimal position of the regulating mechanism was 270 mm, with a weeding rate of 80.65% and a damaging seedling rate of 3.36%. The weeding rate can be increased by at least 11.18% by adjusting the regulating mechanism to a suitable position under the same weeding conditions. This study can provide a reference for research on weeding machinery for organic rice. Full article

The parameters of the discrete element simulation model for rice field soils serve as valuable data references for investigating the dynamic characteristics of the walking wheel of high-speed precision seeding machinery in paddy fields. The research specifically targets clay loam soil from a paddy field in South China. Calibration of essential soil parameters was achieved using EDEM_2022 software (and subsequent versions) discrete element simulation software, employing the Edinburgh Elasto-Plastic Adhesion (EEPA) nonlinear elastic-plastic contact model. The tillage layer and plough sub-base layer underwent calibration through slump and uniaxial compression tests, respectively. Influential contact parameters affecting slump and axial pressure were identified through a Plackett–Burman test. The optimal contact parameter combinations for the discrete element model of the tillage layer and plough sub-base layer were determined via a quadratic rotational orthogonal test. The accuracy of the discrete element simulation model’s parameters for paddy field soils was further validated through a comparative analysis of the simulation test’s cone penetration and the field soil trench test. Results indicate that the Coefficient of Restitution, surface energy, Contact Plasticity Ratio, and Tensile Exp significantly influence slump (p < 0.05). Additionally, the Coefficient of Restitution, Contact Plasticity Ratio, coefficient of rolling friction, and Tangential Stiff Multiplier significantly impact axial pressure (p < 0.05). Optimal contact parameters for the plough layer were achieved with a particle recovery coefficient of 0.49, a surface energy of 18.52 J/m², a plastic deformation ratio of 0.45, and a tensile strength of 3.74. For the plough subsoil layer, optimal contact parameters were a particle recovery coefficient of 0.47, a coefficient of interparticle kinetic friction of 0.32, a plastic deformation ratio of 0.49, and a tangential stiffness factor of 0.31. Results from the cone penetration test reveal no significant disparity in compactness between the actual experiment and the simulation test. The calibrated discrete element model’s contact parameters have been verified as accurate and reliable. The findings of this study offer valuable data references for understanding the dynamic characteristics of the walking wheel of the entire machinery in high-speed precision seeding in paddy fields. Full article

The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines. Full article