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For the prompt planting of subsequent crops, most of China’s corn harvest must occur before full maturity, with a grain moisture content above 25%. Harvesting moist corn presents challenges due to significant grain damage during threshing. We conducted a high-speed photography observation test of moist corn threshing. It demonstrated that corn ears, when passing through the threshing cylinder, often break into pieces. Grains on divided corncobs can be threshed more easily and earlier than those on undivided ones, suggesting that pre-dividing corn ears reduces grain damage. An experiment using the Lianchuang 825 variety examined the effect of moisture content and the divided rate of corncobs (DRC) on grain damage. The results showed that as moisture content increased from 25% to 37%, grain damage to undivided ears increased from 3.75% to 37.71%. Dividing corn ears before threshing significantly reduced damage, with an eight-piece division reducing damage by approximately 70% across all moisture levels. Verification with the Jinyu 1233 variety confirmed that a higher DRC consistently reduced damage. This study provides a new approach to reducing damage in moist corn threshing and aids in the development of low-damage threshing devices.

Deep learning and machine vision technology are widely applied to detect the quality of mechanized soybean harvesting. A clean dataset is the foundation for constructing an online detection learning model for the quality of mechanized harvested soybeans. In pursuit of this objective, we established an image dataset for mechanized harvesting of soybeans. The photos were taken on October 9, 2018, at a soybean experimental field of Liangfeng Grain and Cotton Planting Professional Cooperative in Guanyi District, Liangshan, Shandong, China. The dataset contains 40 soybean images of different qualities. By scaling, rotating, flipping, filtering, and adding noise to enhance the data, we expanded the dataset to 800 frames. The dataset consists of three folders, which store images, label maps, and record files for partitioning the dataset into training, validation, and testing sets. In the initial stages, the author devised an online detection model for soybean crushing rate and impurity rate based on machine vision, and research outcomes affirm the efficacy of this dataset. The dataset can help researchers construct a quality prediction model for mechanized harvested soybeans using deep learning techniques.

To conveniently implement the online detection of grain moisture in combined harvesters and the address the influence of the no-load measurement baseline, thereby enhancing detection accuracy and measurement continuity, this study developed a differential grain moisture detection device. For its convenient installation and integration on combined harvesters, a single-pole plate measurement element with a 1.6 mm thick epoxy resin coated with a 2-ounce copper film was designed, and a grain moisture detection device was constructed based on the STM32F103 microprocessor (STMicroelectronics International NV, Geneva, Switzerland). To enhance the device’s interference resistance, a differential amplification measurement circuit integrated with high-frequency excitation was designed using a reference capacitance. To improve the resolution of the measurement circuit, Malab simulations were conducted at different excitation frequencies, ultimately selecting 30 kHz as the system’s excitation signal frequency. To validate the effectiveness of the measurement circuit, validity tests were performed on the constructed sensor, which showed that the sensor’s measurement voltage could effectively distinguish the moisture levels in grains, with a determination coefficient (R²) reaching 0.9978. To address the errors in moisture measurement caused by changes in grain temperature, an interaction experiment of the effect of moisture content and temperature on the measurement voltage was conducted using an integrated temperature sensor, resulting in the construction of a moisture content calculation model. Both the indoor static detection and field testing of the moisture detection device were conducted, indicating that the maximum average error in static measurements was 0.3%, with a maximum relative error of 0.47%, and the average relative error in field tests was ≤0.4%.

Soil is a heterogeneous medium that exhibits considerable variability in both spatial and temporal dimensions. Proper management of field variability using variable-rate fertilization (VRF) techniques is essential to maximize crop input–output ratios and resource utilization. Implementing VRF technology on a localized scale is recommended to increase crop yield, decrease input costs, and reduce the negative impact on the surrounding environment. This study assessed the agronomic and environmental viability of implementing VRF during the cultivation of summer maize using an on-the-go detector of soil total nitrogen (STN) to detect STN content in the test fields. A spatial delineation approach was then applied to divide the experimental field into multiple management zones. The amount of fertilizer applied in each zone was determined based on the sensor-detected STN. The analysis of the final yield and economic benefits indicates that plots that adopted VRF treatments attained an average summer maize grain yield of 7275 kg ha−1, outperforming plots that employed uniform-rate fertilization (URF) treatments, which yielded 6713 kg ha−1. Through one-way ANOVA, the yield p values of the two fertilization methods were 6.406 × 10−15, 5.202 × 10−15, 2.497 × 10−15, and 3.199 × 10−15, respectively, indicating that the yield differences between the two fertilization methods were noticeable. This led to an average yield increase of 8.37% ha−1 and a gross profit margin of USD 153 ha−1. In plots in which VRF techniques are utilized, the average nitrogen (N) fertilizer application rate is 627 kg ha−1. In contrast, in plots employing URF methods, the N fertilizer application rate is 750 kg ha−1. The use of N fertilizer was reduced by 16.4%. As a result, there is a reduction in production costs of USD 37.5 ha−1, achieving increased yield while decreasing the amount of applied fertilizer. Moreover, in plots where the VRF method was applied, STN was balanced despite the reduced N application. This observation can be deduced from the variance in summer maize grain yield through various fertilization treatments in a comparative experiment. Future research endeavors should prioritize the resolution of particular constraints by incorporating supplementary soil data, such as phosphorus, potassium, organic matter, and other pertinent variables, to advance and optimize fertilization methodologies.

The pneumatic centralized fertilizer discharge system is an important part of pneumatic fertilization machinery and mainly includes a fertilizer discharge device, an air–fertilizer mixing device and a pneumatic distribution device. In this paper, the mechanical structure, key parameters and research methods of pneumatic centralized discharge devices, air–fertilizer mixing devices and pneumatic distribution devices at home and abroad are briefly analyzed. The advantages and disadvantages of these existing devices are summarized, the existing problems are discussed and improvement methods are put forward. In this paper, the structural adaptability, uniformity and stability of the fertilizer discharge of different types of fertilizer discharge devices, such as external groove wheel types, spiral types and centrifugal types, are comprehensively analyzed. The working principle of air–fertilizer mixing devices using the Venturi effect to achieve the uniform mixing of fertilizer and airflow is expounded. The effects of air–fertilizer mixing devices with different structural forms and parameters on mixing performance and the motion characteristics of air–solid two-phase flows are analyzed. The influence of the internal structure, key parameters and distribution mode of pneumatic distribution devices on the uniformity and accuracy of fertilizer distribution are analyzed. This paper focuses on how to improve the uniformity, stability and consistency of discharge across rows provided by the pneumatic centralized fertilizer discharge system. The research status and progress made regarding the core components of the pneumatic centralized fertilizer discharge system at home and abroad are summarized. Based on different research results, the key factors and methods for improving the uniformity of fertilizer discharge are discussed.

In the context of automatic driving, the analysis of the steering motion characteristics is critical for enhancing the efficiency of crawler harvesters. To address issues such as the low transmission efficiency and the large steering radius encountered by traditional crawler harvesters featuring hydrostatic drives, a driven steering crawler harvester chassis was designed. This involved analysis of the chassis transmission system structure and its steering characteristics under several conditions, including differential steering, differential direction reversal, and unilateral braking steering. The steering parameters were determined based on real-time kinematic positioning–global navigation satellite system (RTK-GNSS) measurements, and they were compared with theoretical predictions based on the crawler harvester steering kinematics. The slip rates and modified models of the crawler chassis for various steering modes were then obtained. The results indicated that the increase in the ratio between the running input and steering input speeds led to larger track steering radii and smaller average rotational angular velocities. Remarkably, the slopes of the linear fits of the tracked chassis steering parameters varied significantly under differential direction reversal and differential steering modes. Compared with the actual results, the correlation coefficient of the tracked chassis steering parameters fitting model is close to 1. The steering parameter model was deemed suitable for actual operational requirements. The results provide a valuable reference for designing navigation and steering models of crawler harvesters operating on different road surfaces.

This study investigates the adaptive cleaning system of a soybean combine harvester, addressing the issue of low adaptability in matching the cleaning parameters of the air-and-screen cleaning device of domestic combine harvesters to varying soybean extract characteristics. This mismatch results in high cleaning loss and impurity rates during soybean machine harvesting. Through cleaning experiments, we examine the impact on soybean machine harvesting, where the cleaning loss rate accounts for approximately 10.08% of the total loss rate. The weight of the cleaning loss rate is lower than that of the impurity rate. Additionally, we establish a linear relationship between cleaning parameters and the corresponding cleaning loss rate and impurity rate. We design an adaptive control strategy workflow chart and integrate the adaptive cleaning system into the soybean combine harvester. Verification tests confirm the effectiveness of the adaptive control function. Comparative analysis reveals a reduction of 0.19% in cleaning loss rate and 0.98% in impurity rate compared to the air-and-screen cleaning device. The adaptive cleaning system significantly improves cleaning quality during soybean machine harvesting and enhances the intelligent capabilities of the air-and-screen cleaning device. The results provide practical insights and theoretical guidance for the development of high-quality, low-loss cleaning technology in soybean machine harvesting in China.

Aiming at the demand by unmanned farms for unmanned operation in the entire process of field management, an unmanned plant protection robot for field management was developed based on a platform comprising a traditional high-clearance spray rod sprayer, integrated unmanned driving technology, image recognition technology, intelligent control technology, and precision operation technology. According to the agricultural machinery operation mode, agricultural machinery path planning, linear path tracking, and header path tracking algorithms were developed. Based on the overall structure and working principle of the chassis, the robot control system, steering control system, and operation control system were set. Based on the YOLOv5 image recognition algorithm, the crop–weed recognition model was developed. After 6000 rounds of training, the accuracy, recall, and mean average precision of the model were 87.7%, 84.5%, and 79.3%, respectively. Finally, a field experiment was carried out with the unmanned plant protection robot equipped with a complete system. Results show that the average lateral error of the robot is 0.036 m, the maximum lateral error is 0.2 m, the average root mean square error is 0.053 m, the average velocity error is 0.034 m/s, and the average root mean square error of velocity is 0.045 m/s when the robot works in a straight line. In weeding operations, the area ratio of weedy zones to field is 25%, which saves 75% of the herbicide compared to that dispensed in full spraying mode. The unmanned plant protection robot designed in this study effectively achieves machinery’s autonomous operation, providing valuable insights for research in unmanned farming and autonomous agricultural machinery.

Potato grading is related to weight. Three-dimensional (3D) reconstruction can provide highly accurate volume measurements of potatoes, which can help farmers to analyze their phenotypic characteristics and grade them. Considering their low cost and the required accuracy, a monocular camera and line laser were used to build a potato phenotype determination scanning device. The system obtains coordinates along the surface of a potato, collects laser light reflected from the surface in real time, and completes the coordinate calculation of the original points using the triangulation method. However, the original point clouds lose large areas of point clouds at the top and bottom of the potato. Point cloud repair is carried out by interpolation of points. In addition, the surface point cloud is smoothed. Finally, the generated point cloud is used for 3D reconstruction and volume calculation. In a volume error analysis test, potatoes are divided into calibration and verification groups. First, linear regression is used to relate the real and measured potato volume, and then the density of the potato is calculated. The volume and mass of potatoes in the verification group are measured by the device, and the standard volume and mass are measured manually. The results show that the average relative error in measured volume is -0.08%, and the average relative error in estimated mass is 0.48%. These results indicate that the combination of a line laser and a single camera provides accurate measurements of potato volume that can be used for yield estimation and potato grading.

In the production of summer maize, the problems of excessive fertilizer input and low fertilizer utilization rate are common, resulting in the waste of resources and environmental pollution. In order to explore the optimal fertilization mode of summer maize, a field experiment with eight treatments was designed in which sowing and fertilization were carried out by different machines. The effects of the two fertilization methods, i.e., hole fertilization and strip fertilization; two fertilization positions, i.e., side-position fertilization and positive-position fertilization; and two fertilization depths, i.e., 10 cm and 15 cm, on the summer maize yield, plant nitrogen, phosphorus, and potassium nutrient accumulation, and aboveground biomass were studied. The results show that different fertilization modes had significant effects on summer maize yield, nitrogen, phosphorus, and potassium nutrient accumulation, and aboveground biomass. In general, when the strip fertilization method was used, 15 cm deep positive-position fertilization was superior. When the hole fertilization method was used, 15 cm deep side-position fertilization was superior.

Wheat, one of the most important food crops in the world, is usually harvested mechanically by combine harvesters. The impurity rate is one of the most important indicators of the quality of wheat obtained by mechanized harvesting. To realize the online detection of the impurity rate in the mechanized harvesting process of wheat, a vision system based on the DeepLabV3+ model of deep learning for identifying and segmenting wheat grains and impurities was designed in this study. The DeepLabV3+ model construction considered the four backbones of MobileNetV2, Xception-65, ResNet-50, and ResNet-101 for training. The optimal DeepLabV3+ model was determined through the accuracy rate, comprehensive evaluation index, and average intersection ratio. On this basis, an online detection method of measuring the wheat impurity rate in mechanized harvesting based on image information was constructed. The model realized the online detection of the wheat impurity rate. The test results showed that ResNet-50 had the best recognition and segmentation performance; the accuracy rate of grain identification was 86.86%; the comprehensive evaluation index was 83.63%; the intersection ratio was 0.7186; the accuracy rate of impurity identification was 89.91%; the comprehensive evaluation index was 87.18%; the intersection ratio was 0.7717; and the average intersection ratio was 0.7457. In terms of speed, ResNet-50 had a fast segmentation speed of 256 ms per image. Therefore, in this study, ResNet-50 was selected as the backbone network for DeepLabV3+ to carry out the identification and segmentation of mechanically harvested wheat grains and impurity components. Based on the manual inspection results, the maximum absolute error of the device impurity rate detection in the bench test was 0.2%, and the largest relative error was 17.34%; the maximum absolute error of the device impurity rate detection in the field test was 0.06%; and the largest relative error was 13.78%. This study provides a real-time method for impurity rate measurement in wheat mechanized harvesting.

A pneumatic fertilization distributor used for fertilizing in a fertilizer applicator is a key component of the applicator. The parameters of a pneumatic fertilization distributor affect the uniformity and accuracy of the fertilization of a fertilizer applicator. To obtain the optimal design parameters of a pneumatic fertilization distributor, a fluidstructure coupling simulation test and a bench test were carried out in the Intelligent Agricultural Machinery Laboratory of the Nanjing Institute of Agricultural Mechanization from March 2021 to July 2022. The curvature–diameter ratios of the elbow, bellow length, and air velocity were selected as the experimental factors, and the variation coefficient of the fertilizer discharge at each discharge outlet within 0.5–3 s was selected as the experimental index. A five-level quadratic regression orthogonal rotation combined test was carried out. The results showed that: (1) all three factors had a significant impact on the uniformity of the fertilizer discharge. The reasonable ranges of the curvature–diameter ratio, bellow length, and air velocity were 0.5–1.5, 350–550 mm, and 25–35 m/s, respectively. (2) The order of the influence of the three factors on the uniformity of the fertilizer discharge in descending order was as follows: the curvature–diameter ratio of the elbow, the bellow length, and the air velocity. When the bellow length was 460 mm, the curvature–diameter ratio was 0.6, and the inlet air velocity was 28 m/s. The uniformity of the fertilizer discharge was optimal. A pneumatic conveying system was redesigned according to the optimal parameters, and a bench test was carried out. The results showed that at different speeds, the coefficient of variation of each row’s displacement was not greater than 5%, and the simulation test results were consistent with the bench test results.

Inaccurate and untimely adjustments of cleaning parameters during the operation of wheat combine harvesters lead to high cleaning losses and impurity rates. For this reason, a self-made 4 L-2.5 threshing and cleaning experiment table was employed for cleaning parameter optimization experiments for wheat combine harvesters in this paper. The influence of the cleaning parameters on the cleaning loss and impurity rates was analyzed, and the optimum combination of cleaning parameters was predicted and verified. The contribution hierarchy of cleaning parameters to cleaning loss rate is as follows: crank speed of shale shaker > opening of chaffer > operation speed > fan speed > throttle opening. Meanwhile, the contribution hierarchy of cleaning parameters to impurity rate is as follows: operation speed > fan speed > throttle opening > crank speed of shale shaker > opening of chaffer. The predicted optimum combination of cleaning parameters, i.e., when the cleaning loss and impurity rates are both at a minimum and the feed quantity is at the maximum, is as follows: operating speed—2.2 m/s; opening of chaffer—26 mm, throttle opening—20°; fan speed—1100 r/min; and Crank speed of shale shaker—350 r/min. With these settings, the cleaning loss rate was 1.5% and the impurity rate was 1.9%. In the validation experiment, the average cleaning loss rate was found to be 1.47%, the average impurity rate was 1.96%, and the relative error of the predicted values was 0.03% and 0.06%, respectively. Compared with the cleaning index of combine harvesters with commonly used parameters, the cleaning loss rate was reduced by 0.12% and the impurity rate was reduced by 0.19%.

Aiming to address the problems of low detection accuracy and poor stability due to the weak anti-interference ability of the bridge circuit and operational amplifier circuit, and the influence on the capacitance of the bulk density and temperature of the straw bale, an on-line detection device for the moisture content of straw bales in a square baler was developed based on the capacitance method. The device integrates a capacitance sensor, pressure sensor, and temperature sensor. The optimal structure size of the capacitor plate was determined through the simulation test of the capacitor sensor plate structure. A moisture content monitoring system based on the MATLAB language is built, and the moisture content detection model was constructed based on the backpropagation neural network (BPNN) algorithm. Finally, a test bench for a square baling machine was designed, and a performance verification test of the moisture content detection device was carried out. The simulation results of the capacitor plate show that when the length, width, and spacing of the capacitor plate are 148.6, 47.7, and 5.1 mm, respectively, the detection sensitivity of the capacitor plate is the highest. The modeling results show that the R2, RMSE, and RPD of the BPNN model are 0.986, 0.008998, and 5.99, respectively, with solid data fitting ability and high prediction accuracy. The bench test results show that for the samples having moisture content between 13.1 and 28.04%, the coefficient of determination R2 of the fitting curve between the predicted value of moisture content and the actual value is 0.949. The relative error range of the predicted value of moisture content is −6.51–8.66%, and the absolute error range is −1.63–1.72%. The on-line detection device for moisture content of straw bales has good accuracy and stability.

In order to further decrease the energy consumption of desiccant wheel dehumidification, the drying medium circulation characteristics of a system combining heat pump drying with desiccant wheel dehumidification were investigated. Moreover, the critical dehumidification conversion mechanism was studied. The analysis of the heat pump hot air circulation system demonstrated that the heat pump system has the best dehumidification efficiency. Through the analysis of the system combining heat pump drying with desiccant wheel dehumidification, the critical conversion point was determined. The critical dehumidification mechanism was further verified using an online temperature and humidity measurement system. To investigate the effect of the critical point on energy consumption and drying quality and develop a drying model, response surface experiments were performed based on the effects of regeneration temperature, drying temperature, and conversion point relative humidity on rehydration, color difference, and specific moisture extraction rate (SMER). The optimal conversion point humidity was determined to be about 46% RH, which was slightly different from the test optimization value of 45.6% RH. In addition, comprehensive optimization and experimental verification of the influencing factors were conducted. The results demonstrated that the R2 values of the three models were greater than 0.98, and the experimental factors had a significant effect on drying quality and energy consumption. When the regeneration temperature was 96°C, the drying temperature was 53°C, the relative humidity of the conversion point was 46%, the color difference was 46.3, the rehydration ratio was 5.75, and the SMER was 1.62 kg/kW·h.

With the rapid advancement of agricultural mechanization, intelligent header technology has emerged as a pivotal element in optimizing the efficiency and quality of grain combine harvesters. This paper offers a comprehensive analysis of the current state of intelligent header technology, with a particular emphasis on the structure, working principles, contour-following mechanisms, and height control technologies. By integrating cutting-edge sensor technologies, advanced control algorithms, and optimized mechanical designs, intelligent headers can achieve precise control over height and posture, thereby significantly reducing crop losses and enhancing both harvesting efficiency and quality. Despite substantial progress, challenges remain in areas such as response speed, real-time performance, height measurement accuracy, and control algorithm effectiveness. Future research will likely concentrate on improving control system performance, refining component and system designs, and incorporating emerging technologies to better accommodate diverse crops and complex terrains. This paper also provides a critical evaluation of current limitations in intelligent header research and projects future trends, offering valuable theoretical and practical insights for optimizing header structures, minimizing losses, and enhancing intelligent functionalities. The ultimate aim is to drive continuous innovation and advancement in header technology for grain combine harvesters. [ABSTRACT FROM AUTHOR]

Studying the effect of variable fertilization during the elongation stage on winter wheat production in the rice-wheat rotation area is critical for evaluating the application effect and economic benefits of variable fertilization technology. The variable fertilization experiment of wheat was carried out in Jiangsu province by using the self-developed fertilizer applicator. Three fertilization methods were used to conduct a comparative analysis of the fertilization amount, population structure, and yield of winter wheat during the elongation stage. On this basis, the economic feasibility of variable fertilization during the elongation stage was evaluated. The experimental results showed that the control accuracy of variable-rate fertilization with fertilization equipment was greater than 95%. After variable fertilization, the coefficient of variation of Normalized Difference Vegetation Index (NDVI) values in the winter wheat crop canopy spectral data remained between 0.076 and 0.125, and the Christensen uniformity coefficient remained between 0.901 and 0.940. Compared with the traditional empirical balance method for quantitative fertilization of plots, the real-time variable fertilization plot used 13.6 kg/ha less fertilizer during the elongation stage. The findings validate that implementing variable fertilization can help reduce nitrogen fertilizer input, improve nitrogen fertilizer utilization efficiency, reduce environmental pollution, and enhance the sustainability of agricultural production. [ABSTRACT FROM AUTHOR]

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Building aerogels-based adsorbents with high adsorption capacity, selectivity, and structure designing through green and simple ways was urgent issue for the removal of Pb2+ from contaminated water. In this study, for the first time, chitosan was applied as assistant agents for the liquid-phase exfoliation of WS2 nanosheets, and the composite aerogels (CS/WS2) with oriented microchannel structures were obtained via directional co-assembly processes. Owing to the potential H-bond between CS and WS2, as well as the sulfur-rich surfaces, CS/WS2 aerogels exhibited promising Pb2+ adsorption performance (~275.9 mg/g) than many other chitosan-based adsorbents. Besides, the adsorption of Pb2+ by CS/WS2 aerogels were also rapid (reached an equilibrium at ~120 min), selective (~54% of maximal Pb2+ uptake), and sustainable (maintained ~90% of the maximal uptake). This work paved a simple and green way to construct WS2 nanosheets-based aerogels with delicate micro-structures and favorable Pb2+ adsorption performances. [ABSTRACT FROM AUTHOR]

Apparent viscosity is an important parameter for glucose syrup production, which is greatly affected by particle size and concentration of samples. In order to analyze the factors influencing the apparent viscosity, the particle size distribution, steady shear flow behavior, temperature, and time sweep test of native degerminated maize grits (NDMG), extruded degerminated maize grits (EDMG), and enzymatically extruded degerminated maize grits (EEDMG) with different particle sizes (passed through 20, 40, 60, 80, and 100 mesh sieves) and concentrations (water-sample ratios: 2:1, 3:1, 5:1, 10:1, and 20:1) were investigated. All samples passed through different mesh sieves showed a gradient and a relatively concentrated distribution, and slurries had typical shear-thinning properties. Apparent viscosity increased with increasing particle size and concentration. The lowest apparent viscosity was attained from the samples obtained at 100+ mesh and 20:1 water-sample ratio. Moreover, the sample with a 20:1 water-sample ratio showed the most stable apparent viscosity in the temperature sweep test. In the time sweep test, the power law equation with high determination coefficients (7-2=0.9446, 0.9382) and low root mean square error (RMSE=0.0002) had the best fit to the experimental data of the EDMG and EEDMG samples passed through 100+ mesh. Overall, the lower apparent viscosities of the EDMG and EEDMG samples obtained at 100+ mesh and 20:1 watersample ratio can improve the activity and accessibility of enzymes for glucose syrup production. This study provides critical insight into decreased apparent viscosity and expands the uses of EDMG and EEDMG in the glucose syrup sector. [ABSTRACT FROM AUTHOR]

Automatic guidance of agricultural vehicles requires automatic execution of operation commands received from the navigation controller by using electronically controlled mechanisms for wheel steering, speed changing and work implementing. Automatic steering contributes as a prerequisite technique in automatic and semi-automatic agricultural navigation. This research aimed to develop an electric automatic steering system that was compact in its structure and integrated into original steering mechanism in a simply and convenient way for aftermarket modification. A brushless motor and reducer assembly was utilized to provide an adequate steering torque instead of manual maneuver. A rapid assembling approach was proposed by passing the steering shaft through the hollow output shaft. A digital proportional-integral-differential (PID) algorithm was implemented to calculate the rotation speeds and directions by comparing the desired angle and the actual angle, which was implemented in a printed circuit board with a microcontroller unit (MCU) and interface chips. An unmanned wheeled tractor was applied as test platform to integrate the newly developed electric automatic steering system. Tests were conducted to evaluate its performance in terms of stability and responsiveness. An autonomous navigation system guided the tractor along target paths in the field by sending steering commands to the electric automatic steering system. The results show that the steering angle error was less than 0.81° when desired steering angle was less than 10°. The lateral error difference was no more than 4.76 cm when repeating following the same target path, which indicated that the electric automatic steering system responded accurately and robustly to steering commands. [ABSTRACT FROM AUTHOR]

Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field. Working quality depends significantly on the driving skills of the operator. Automatic guidance has been introduced into agriculture to achieve high-accuracy path tracking during the last decades, which contributes considerably to straight-line navigation. The objective of this research was to develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles. Three wheel-type vehicles were used as the test platform featuring automatic steering, hydrostatic transmission and speed control, which included a rice transplanter, a high-clearance sprayer and a tractor. A dual-antenna RTK-GNSS receiver was attached to the vehicles to provide spatial information on both positioning and heading by using the RTX service from Trimble. A path planning method was proposed to create a straight-line reference path by giving two points, and the target path was determined according to the vehicle initial status and working assignment. Headland turning was comprehensively taken into account by listing different turn patterns in order to realize autonomous navigation at the headland. The navigation controller hardware was fabricated for program execution, data processing and information communication with peripherals. A human-machine interface was designed for the operator to complete basic setting, path planning and navigation control by providing controls. Field experiments were conducted to evaluate the performance and versatility of the newly developed autonomous navigation controller in guiding agricultural vehicles to follow straight paths and turn at the headland. Results showed that an appropriate turn pattern was automatically executed when finishing straight-line navigation. The lateral error in straight-line tracking was no more than 6 cm, 6 cm and 5 cm for the rice transplanter, the high-clearance sprayer and the tractor, respectively. And the maximum lateral RMS error was 3.10 cm, 4.75 cm, 2.21 cm in terms of straight-line tracking, which indicated that the newly developed autonomous navigation controller was versatile and of high robustness in guiding various agricultural vehicles. [ABSTRACT FROM AUTHOR]

Rice transplanting requires the operator manipulate the rice transplanter in straight trajectories. Various markers are proposed to help experienced drivers in keeping straightforward and parallel to the previous path, which are extremely boring in terms of large-scale fields. The objective of this research was to develop an autonomous navigation system that automatically guided a rice transplanter working along predetermined paths in the field. The rice transplanter used in this research was commercially available and originally manually-operated. An automatic manipulating system was developed instead of manual functions including steering, stop, going forward and reverse. A sensor fusion algorithm was adopted to integrate measurements of the Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) and Inertial Measurement Unit (IMU), and calculate the absolute moving direction under the UTM coordinate system. A headland turning control method was proposed to ensure a robust turning process considering that the rice transplanter featured a small turning radius and a relatively large slip rate at extreme steering angles. Experiments were designed and conducted to verify the performance of the newly developed autonomous navigation system. Results showed that both lateral and heading errors were less than 8 cm and 3 degrees, respectively, in terms of following straight paths. And headland turns were robustly executed according to the required pattern. [ABSTRACT FROM AUTHOR]

Precision seeding requires that the corn drill drop seeds into the soil by a specific in-row spacing while its travelling speed fluctuates due to unevenness of the field ground. This paper presents a low-cost precision seeding control system for a conventional corn drill with mechanical metering devices of finger-pickup type. A median filtering method was implemented in the control system to process measurements from a rotary encoder in order to acquire stable values of the corn drill travelling speed. The metering unit was driven by an electric motor controlled by the metering ECU according to the actual travelling speed and the desired in-row spacing in real-time. A user interface was programmed to communicate with ECUs for configuring parameters and displaying operating information during working. The newly-developed precision seeding control system was first calibrated in terms of speed measurement and control for two ECUs. Experiments were conducted on a seeding test platform in the laboratory for evaluating its accuracy in dropping seeds by giving different in-row spacing under different travelling speeds of the conveyor sticky belt. Results showed that the average spacing error was less than 2 cm and the maximum RMS error was 0.78 cm for all spacing values including 25 cm, 30 cm and 35 cm under the travelling speed of 1.0-8.0 km/h. These indicated that the low-cost precision seeding control system worked in a both accurate and stable way. [ABSTRACT FROM AUTHOR]