To address the challenges of limited detection range, unstable tracking, and object flickering caused by ground interference from uneven farmland, inaccurate visual masks, and sparse LiDAR point clouds at mid-to-long distances, this study constructs a multi-sensor fusion perception system based on 3D LiDAR and cameras. Furthermore, it proposes an obstacle detection method incorporating cascaded ground filtering, multimodal feature fusion, and temporal tracking. The method first utilizes a cascaded algorithm combining Random Sample Consensus (RANSAC) and Cloth Simulation Filter (CSF) to address the challenge of ground clutter filtering in uneven terrain. To account for the varying reliability of sensor modalities at different distances, a matching strategy based on hybrid cost is developed. This strategy enables a robust association between LiDAR clusters and visual targets by adaptively weighting the precise boundary information from visual masks and the stable positional features from detection bounding boxes. A Kalman filter is subsequently integrated to impose temporal smoothness on the fusion results and eliminate long-range object flickering. Experimental results demonstrate that the proposed method reduces the ground false positive rate to 0.3% and achieves an overall average precision of 91.4%. Regarding mid-to-long distance detection, in contrast to the baseline method where the recall drops sharply beyond 35 m, the proposed method achieves an average recall of 98.2% within the entire 0-45 m range. Furthermore, accuracy (MOTA) and tracking continuity (IDF1) are improved to 92.93% and 98.03%, respectively. Therefore, the proposed method satisfies the requirements for high precision, high recall, and stable environmental perception in complex farmland scenarios.

The extensive cultivation scale of sugar beet seedlings has resulted in the necessity for accurate identification and monitoring of the seedling count, a task which has become crucial and highly challenging in the sugar industry. However, sugar beet seedlings in UAV aerial photography scenarios are mostly small targets with complex backgrounds. Existing general detection models not only have insufficient detection accuracy, but also struggle to balance computational efficiency and resource consumption. To meet the practical needs of field monitoring, this paper proposes the LDH-RTDETR, a sugar beet seedling detection model that balances high accuracy and light weight. This model uses LSNet for feature extraction to reduce size, adds a deformable attention (DAttention) module to capture fine-grained seedling features, and adopts HS-FPN to improve multi-scale feature fusion in the neck network. Experimental results show that the improved model significantly outperforms the original RT-DETR model, with a 3.6% increase in accuracy, a 2.1% increase in mAP50, a recall rate of 86.0%, and a final model size of only 43.3 MB, thus achieving an effective balance between accuracy and model size. This study’s improved model offers an efficient solution for large-area identification and counting of sugar beet seedlings, and is highly significant for advancing the automation of sugar crop field management and agricultural digital transformation.

The precise measurement of agricultural machinery operation area provides the critical data foundation for agricultural subsidy accounting and intelligent decision-making. Addressing the bottlenecks in measurement accuracy and efficiency caused by trajectory noise, fragmented plots, and repetitive operations in practical production, this paper systematically reviewed area calculation technologies based on agricultural machinery trajectories. At the algorithmic level, the principles, technical characteristics and applicable boundaries of the mileage-width method, buffer-grid method and polygon-contour method were analyzed. At the application level, the engineering implementation characteristics of agricultural machinery equipment, UAV platforms, as well as navigation and positioning enterprises were summarized. Analysis indicated that existing algorithms still struggled to balance accuracy, efficiency and adaptability, and a unified evaluation standard was currently lacking. Accordingly, future directions were proposed from three dimensions: Multi-source sensor fusion, algorithm lightweighting and edge computing, as well as intelligent data analysis. This study aimed to provide a reference for the research and application of precise measurement technologies for agricultural machinery operation areas.

To achieve an unmanned rice farm, in this study, a cotransporter system was developed using a tracked rice harvester and transporter for autonomous harvesting, unloading, and transportation. Additionally, two unloading and transportation modes-harvester waiting for unloading (HWU) and transporter following for unloading (TFU)-were proposed, and a harvesting-unloading-transportation (HUT) strategy was defined. By breaking down the main stages of the collaborative operation, designing module-state machines (MSMs), and constructing state-transition chains, a HUT collaborative operation logic framework suitable for the embedded navigation controller was designed using the concept and method of the finite-state machine (FSM). This method addresses the multiple-stage, nonsequential, and complex processes in HUT collaborative operations. Simulations and field-harvesting experiments were performed to evaluate the applicability of this proposed strategy and system. The experimental results showed that the HUT collaborative operation strategy effectively integrated path planning, path-tracking control, inter-vehicle communication, collaborative operation control, and implementation control. The cotransporter system completed the entire process of harvesting, unloading, and transportation. The fieldharvesting experiment revealed that a harvest efficiency of 0.42 hm2 center dot h-1 was achieved. This study can provide insight into collaborative harvesting and solutions for the harvesting process of unmanned farms. (c) 2024 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

ObjectiveThe objective of this study is to solve the problem of large longitudinal spacing of fruit trees and the inavailability of global navigation satellite system (GNSS) signals in closed-canopy orchard environment. MethodA navigation method based on three-dimensional light detection and ranging (3D LiDAR) was proposed, taking the wheeled spray robot as the research platform and the canopy mango orchard as the experimental environment. For laser point cloud preprocessing, mounting error calibration of the liDAR was initially conducted. Terrain compensation for 3D LiDAR point cloud positions was implemented via an attitude and heading reference system (AHRS). The cloth simulation filter (CSF) was employed to extract ground points. An improved statistical filtering method based on the Euclidean distance of point clouds was used to both remove noise point clouds and retain distant fruit tree point clouds. Based on the scanning characteristics of 3D LiDAR point cloud and the triangular inequality condition, an adaptive distance threshold calculation method with clustering body center constraint was designed, and the obtained body center position was projected to the X-Y plane of the navigation coordinate system to obtain the clustered body center position of the trunk point cloud. Newton’s interpolation method was used to interpolate the body-centered position data, and the random sample consensus (RANSAC) algorithm was used to fit the navigation path, i.e., NIL-RANSAC, after the interpolation was completed. In order to verify the accuracy and reliability of navigation path extraction, two methods, least squares method (LSM) and RANSAC, were used to obtain the navigation path directly and conduct comparative experiments. A linear quadratic regulator (LQR) was used for path following control. ResultUsing CSF in closed-canopy orchard effectively removed weeds and uneven ground point clouds and the treatment time was only 0.03 s. The success rate of Euclidean clustering with the adaptive distance threshold within 15 m was more than 95%. LQR realized path following control, and the maximum lateral deviations of NIL-RANSAC, RANSAC and LSM were 0.26, 0.32 and 0.42 m, respectively, and the standard deviation of NIL-RANSAC was the minimum, being only 0.09 m. The navigation accuracy of the NIL-RANSAC path fitting method was better than those of RANSAC and LSM, and the average time of the complete navigation algorithm was less than 100 ms. ConclusionThe NIL-RANSAC method can meet the requirements of accurate and real-time navigation of closed-canopy orchard environment, and provide a reference for autonomous navigation of orchard ground equipment.

To realize the automatic row-alignment harvesting of cotton without the requirement for preset paths, this study designed the contact sensor and its modeling method, processed and corrected the position and attitude information obtained from the GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit), and constructed a data fusion method based on the Kalman Filter to achieve accurate and stable acquisition of the row-alignment deviation. Furthermore, a navigation control system for the cotton picker was developed, the validation and field harvest experiments were performed to test the applicability of the proposed method and system. The validation experiments showed that the SD (Standard Deviation) of the row-alignment deviation after data fusion was reduced by 32.35 % compared to the contact-sensor direct-out data. The data fusion method not only reduced the measurement noise of the contact sensor but also improved the adaptability for missing detection and lack of plants. The field harvest experiments showed that the MAE (Mean Absolute Error), MAD (Maximum Absolute Deviation), and SD of the automatic row-alignment harvest were 0.035, 0.133, and 0.044 m, respectively. This method exhibits better adaptability and practicability in field environments. These results indicate that the proposed multi-sensor fusion method and automatic row-alignment control system proposed and designed in this study can satisfy the requirements of practical field operations.

Convolutional Neural Networks (CNN) are an important means of detection of microdefects on the aluminum surface, and the high complexity and computing power requirements of the CNN model lead to difficulties in deploying them on edge computing platforms as the detection accuracy continues to improve. We have studied a lightweight acceleration method for detecting microdefects on aluminum surfaces on the Zynq-7000 All Programmable SoC (ZYNQ) platform. A lightweight aluminum surface defect detection network (LADFastDet) and high-performance accelerators based on ZYNQ are designed to meet the requirements of precision and speed under limited resources. In the LADFastDet structure, a lightweight inverted residual block is designed by combining depthwise convolution, inverted residual block, and inverted bottleneck. A multiscale feature fusion structure is designed to effectively improve the detection accuracy of LADFastDet, especially small target defects. We design accelerators on ZYNQ through optimization methods such as loop optimization strategy, ping-pong buffering, and multichannel and multiple interfaces data reading and writing to reduce data access latency and thus improve the computing speed. The experimental results show that the LADFastDet model has a mAP of 97.51%, the inference time of the accelerators for a single image is 42.57 ms, and a power consumption of 2.15 W, which achieves a throughput of 24.9 GOPS and an energy efficiency of 11.58 GOPS/W.

In order to enhance the efficiency of agricultural machinery in orchard rows and minimize harm to personnel caused by pesticide spraying, this study developed a GNSS-based (Global Navigation Satellite System) automatic navigation driving system for tracked orchard sprayers. The tracked sprayer was used as a platform for this research. We constructed both a crawler hydraulic platform and spraying working parts based on orchard operation requirements. Additionally, we designed the hydraulic and electrical sub-control process of the crawler platform. By utilizing the motion model of the tracked mobile platform, we designed a linear path tracking control method using position deviation and heading deviation as state quantities. This allows the research platform to automatically initiate and terminate, travel in a straight line between rows, and complete spraying operations. Experimental verification confirmed that the tracked sprayer designed in this study successfully achieves automatic driving. The best automatic driving performance is achieved at a speed of 1.0 m/s. When the sprayer’s speed is 1.2 m/s, the maximum value of the straight-line path tracking accuracy of the platform’s automatic driving is better than 5.6 cm, with a standard deviation of 2.8 cm. This system effectively meets the requirements of automatic operation for an automatic spraying machine, thereby establishing a foundation for the implementation of automatic spraying operations in orchards.

Broken cane and impurities such as top, leaf in harvested raw sugarcane significantly influence the yield of the sugar manufacturing process. It is crucial to determine the breakage and impurity ratios for assessing the quality and price of raw sugarcane in sugar refineries. However, the traditional manual sampling approach for detecting breakage and impurity ratios suffers from subjectivity, low efficiency, and result discrepancies. To address this problem, a novel approach combining an estimation model and semantic segmentation method for breakage and impurity ratios detection was developed. A machine vision-based image acquisition platform was designed, and custom image and mass datasets of cane, broken cane, top, and leaf were created. For cane, broken cane, top, and leaf, normal fitting of mean surface densities based on pixel information and measured mass was conducted. An estimation model for the mass of each class and the breakage and impurity ratios was established using the mean surface density and pixels. Furthermore, the MDSC-DeepLabv3+ model was developed to accurately and efficiently segment pixels of the four classes of objects. This model integrates improved MobileNetv2, atrous spatial pyramid pooling with deepwise separable convolution and strip pooling module, and coordinate attention mechanism to achieve high segmentation accuracy, deployability, and efficiency simultaneously. Experimental results based on the custom image and mass datasets showed that the estimation model achieved high accuracy for breakage and impurity ratios between estimated and measured value with R2 values of 0.976 and 0.968, respectively. MDSC-DeepLabv3+ outperformed the compared models with mPA and mIoU of 97.55% and 94.84%, respectively. Compared to the baseline DeepLabv3+, MDSC-DeepLabv3+ demonstrated significant improvements in mPA and mIoU and reduced Params, FLOPs, and inference time, making it suitable for deployment on edge devices and real-time inference. The average relative errors of breakage and impurity ratios between estimated and measured values were 11.3% and 6.5%, respectively. Overall, this novel approach enables high-precision, efficient, and intelligent detection of breakage and impurity ratios for raw sugarcane.

Efficient and accurate fault diagnosis plays an essential role in the safe operation of machinery. In respect of fault diagnosis, various data-driven methods based on deep learning have attracted widespread attention for research in recent years. Considering the limitations of feature representation in convolutional structures for fault diagnosis, and the demanding requirements on the quality of data for Transformer structures, an intelligent method of fault diagnosis is proposed in the present study for bearings, namely Efficient Convolutional Transformer (ECTN). Firstly, the time-frequency representation is achieved by means of short-time Fourier transform for the original signal. Secondly, the low-level local features are extracted using an efficient convolution module. Then, the global information is extracted through transformer. Finally, the results of fault diagnosis are obtained by the classifier. Moreover, experiments are conducted on two different bearing datasets to obtain the experimental results showing that the proposed method is effective in combining the advantages of CNN and transformer. In comparison with other single-structure methods of fault diagnosis, the method proposed in this study produces a better diagnostic performance in the context of limited data volume, strong noise, and variable operating conditions.

In autonomous rice harvesting, the use of transporters to assist rice transportation is an effective way to improve efficiency. However, nonlinear systems, such as double Hydraulic Static Transmission (HST)-driven tracked rice harvesters and transporters, make it challenging to control the precise parallel parking alignment during transportation, often leading to rice loss and potential safety hazards. To address this issue, this study established a geometric cotransporter model and used a preset path to decouple a two-dimensional control problem into two sets of one-dimensional controls. Dynamic and kinematic models of the longitudinal drive and steering systems were analyzed. Based on the identified longitudinal drive system transfer function, a predictive model was constructed to predict and compensate for the parking slip caused by system inertia. It was combined with the integrator wind-up protection to improve the pure tracking method and eliminate or reduce system errors in path tracking. A parallel parking alignment control system was designed using these two methods, and comparative experiments were conducted on the road surface. The results demonstrate that the compensated predictor (CP) longitudinal control improves the alignment accuracy compared with the proportional differential (PD) controller, whereas the Improved Pure-Pursuit Control (IPPC) path-tracking slightly enhances the tracking accuracy compared with proportional integral (PI). Moreover, a field-autonomous rice-harvesting cotransporter experiment showed that the longitudinal alignment accuracy of the designed system was less than 0.2 m. The lateral alignment accuracy was less than 0.1 m.

To address the challenges of poor fluidity and low uniformity in conventional sugarcane fertilizer applicators, a novel dual-directional spiral fertilizer applicator has been developed. The working principle of the applicator is explained, and, after analyzing the agronomic requirements for sugarcane, the parameter range for key components of the applicator is determined. The spiral blade’s diameter, pitch, and rotational velocity are chosen as the experimental factors, with the average fertilizer discharge uniformity as the evaluation criterion. Virtual simulation experiments are conducted using the discrete element method and a quadratic regression orthogonal rotating combined design. Regression models for the evaluation criterion and various experimental factors are obtained. Additionally, a dataset created from these experiments was then used to construct an artificial neural network (ANN) prediction model. Response surface methodology (RSM) and the ANN were both used to analyze and predict the outcomes. The results indicate that the artificial neural network outperforms response surface methodology in terms of better fitting capability and higher prediction accuracy. The determination coefficient, mean squared error, and root mean square error are 0.99629, 0.99163, 0.07763, 0.17498, 0.27862, and 0.41831, respectively. When comparing the two models, the optimal parameter combination is determined to be a diameter of 90.1669 mm, a pitch of 59.7407 mm, and a rotational speed of 53.8944 r/min, resulting in an average fertilizer discharge uniformity of 92.0670%. An experiment with these parameters confirmed the simulated findings, revealing a maximum discrepancy of 2.4%. This study offers valuable insights into optimizing spiral fertilizer applicators.

To accurately control the longitudinal relative position of a harvester and transport vehicle, such that grain in the harvester can be accurately unloaded into the transport vehicle's granary, this study established a master–slave collaborative harvesting system. Furthermore, this study proposed a calculation method for the longitudinal deviation of two vehicles, analyzed the structure and mathematical model of the hydraulic stepless transmission, identified the proposed stem transfer function of the hydraulic stepless transmission speed, analyzed incremental proportional–integral–derivative (PID), and developed self-adjusting–single-neuron PID control methods. Simulations and field experiments were performed to test the applicability of the proposed system. The experimental results indicated that the maximum overshoot of the longitudinal deviation between the harvester and the transport vehicle did not exceed 0.25 m, the steady-state mean absolute deviation did not exceed 0.08 m, the steady-state maximum deviation did not exceed 0.26 m, and the steady-state standard deviation did not exceed 0.09 m, when the harvester speed was 0.6, 0.8, and 1 m/s. Field-harvesting application experiments showed that the settling time during the alignment process was 13.2 s, and the steady-state maximum deviation was 0.253 m. The grain can be accurately unloaded into the granary of the transport vehicle. These results indicate that the control method and the master–slave collaborative harvesting system proposed in this study can meet the needs of precise collaborative unloading between the harvester and transport vehicle.

The distributed capacitance inside the quartz flexible accelerometer (QFA) coupled the high frequency voltage excitation signal in the differential capacitance detection circuit to the torquer coil, and superimposes the torquer driving current to form the driving noise. In this study, the values of the distributed capacitance inside the QFA were simulated. According to the formation mechanism of the QFA driving noise, the equivalent circuit model of the driving noise is established, and the driving noise characteristics of the detection circuit with single excitation and double excitation source are analyzed. The theoretical and experimental results show that the electric field coupled driving noise transmission system is a first-order system with high-pass characteristics. The driving noise of the single excitation detection circuit is larger than that of the dual excitation detection circuit (DEDC), and the DEDC can reduce the driving noise by 39.77% when the QFA shell is grounded. The equivalent acceleration of the electric field coupled driving noise is between tens of μg to hundreds of mg, which is one of the important noise sources that affect the measurement accuracy of the QFA. A measure was proposed to suppress the high-frequency driving noise by adding a low-pass filter after the sampling output of the driving current, which can reduce the driving noise to 1.85 μg and effectively reduce the influence of the driving noise on the measurement accuracy of the QFA.

Smart farming uses advanced tools and technologies such as intelligent agricultural machines, high-precision sensors, navigation systems, and sophisticated computer systems to increase the economic benefits of agriculture and reduce the associated human effort. With the increasing demands of individualized farming operations, the internet of things is a crucial technique for acquiring, monitoring, processing, and managing the agricultural resource data of precision agriculture and ecological monitoring domains. Here, an internet of things-based system scheme integrating the most recent technologies for designing a management platform for agricultural machines equipped with automatic navigation systems is proposed. Various agricultural machinery cyber-models and their corresponding sensor nodes were constructed in a pre-production phase. Three key enabling technologies—multi-optimization of agricultural machinery scheduling, development of physical architecture and software, and integration of the controller-area-network with a mobile network—were addressed to support the system scheme. A demonstrative prototype system was developed and a case study was used to validate the feasibility and effectiveness of the proposed approach.

The identification method of rice seedling rows based on machine vision is affected by environmental factors that decrease the accuracy and the robustness of the rice seedling row identification algorithm (e.g., ambient light transformation, similarity of weed and rice features, and lack of seedlings in rice rows). To solve the problem of the above environmental factors, a Gaussian Heatmap-based method is proposed for rice seedling row identification in this study. The proposed method is a CNN model that comprises the High-Resolution Convolution Module of the feature extraction model and the Gaussian Heatmap of the regression module of key points. The CNN model is guided using Gaussian Heatmap generated by the continuity of rice row growth and the distribution characteristics of rice in rice rows to learn the distribution characteristics of rice seedling rows in the training process, and the positions of the coordinates of the respective key point are accurately returned through the regression module. For the three rice scenarios (including normal scene, missing seedling scene and weed scene), the PCK and average pixel offset of the model were 94.33%, 91.48%, 94.36% and 3.09, 3.13 and 3.05 pixels, respectively, for the proposed method, and the forward inference speed of the model reached 22 FPS, which can meet the real-time requirements and accuracy of agricultural machinery in field management.

The accurate extraction of navigation path is very important for the automatic navigation of agricultural robots. Aiming at the complex orchard environment and the problem that the existing navigation path extraction algorithms are too complex and narrow application range, a visual navigation path extraction method based on neural network and pixel scanning was proposed in this paper. This method trained the semantic segmentation network based on Segnet and Unet on the basis of orchard road condition data sets. According to the edge of the orchard road condition mask area, the navigation path was fitted by the designed scanning method, filtering algorithm and weighted average method. The experimental results showed that the segmentation accuracy of neural network under low light, ordinary light and strong light was 96.00%, 92.00% and 92.00% respectively. The average pixel error was 9.5 pixel and the average distance error was 5.03 cm. In the actual orchard environment, the orchard road was generally 3.0 m, the average distance error accounted for 1.67%. Therefore, this method improves the accuracy of orchard visual navigation path extraction, meeting the operation requirements of tracked robots in orchard, and provides an effective reference for visual navigation task.

To achieve high-frequency and effective inter-vehicle communication between harvesters and transport vehicles during cooperative harvesting, a protocol for wireless communication was designed by analyzing actual communication requirements. Two different wireless communication modes (radio and 4G) were selected for the hardware design; then, a Kalman Filter was designed based on real-time Dead-reckoning and inter-vehicle Communication data after delay Compensation (KFDCC). Finally, the relative longitudinal deviation between two vehicles was obtained and updated steadily at a 10 Hz frequency. By using the relative longitudinal deviation of two vehicles, calculated after aligning the UTC stamp with the local GNSS data from the harvester and transport vehicle as a comparative metric, accuracy evaluation experiments were conducted regarding radio and 4G. The maximum absolute errors of the KFDCC output value were 0.03783 and 0.07381 m, respectively, and the mean square errors were 0.00392 and 0.01317 m, respectively. Compared with systems without the KFDCC method, the mean square errors were reduced by 88.76% and 90.60%, respectively. The KFDCC method can also effectively solve the problems of data delay, packet loss, blockage, error, and so on, in wireless communication, and has short-time breakpoint endurance capabilities. Field experiments showed that the proposed method can provide accurate data support for the dynamic alignment and unloading processes of harvesters and transport vehicles, and it can also provide algorithmic support for real-time communication data fusion between different wireless communication modes. Overall, the inter-vehicle communication mode and data-processing method designed in this paper have good effects and adaptability, and they can guarantee that the whole process of autonomous harvesting operates properly.

在人工智能产业和物联网技术迅猛发展的情况下,社会对学生软件应用能力的要求日益提高.提高学生的软件编程水平不仅要求教师积极主动地提高课堂教学水平,还要有强大的教学平台支撑,满足学生课后大量编程实践的需求.针对此类课程实践性强的特点,搭建了程序设计课程在线学习Moodle云平台,并基于Coderunner设计了程序设计在线评测系统,引入多样化的程序设计实践及考核方式,满足了学生在线编程实践及评测的需求,并介绍了基于Coderunner开展程序设计教学实践的具体感受和体会.