Hui Liu

Autonomous headland operations require reliable boundary perception and timely behavioral decisions in unstructured field environments. This study proposes a perception-and-decision framework for autonomous agricultural machinery. A CA-SP-DeepLabV3+ semantic segmentation model is developed by integrating Coordinate Attention, Strip Pooling, and a combined Boundary–Dice loss to enhance the continuity and localization of weak and elongated headland boundaries. To facilitate deployment on resource-constrained computing platforms, structured pruning and knowledge distillation are further applied to compress the segmentation network. A stereo-vision-based three-dimensional boundary reconstruction method is then used to estimate the vehicle-relative distance to the headland boundary. Based on the estimated distance, tractor–implement parameters, operating speed, and selected turn type, the decision module calculates the required headland width and distance thresholds for deceleration and turn preparation. Experimental results show that the compressed segmentation model achieved IoU values of 92.55% and 96.53% for the headland and field classes, respectively, while reducing the number of parameters and FLOPs by 28.48% and 48.43% compared with the uncompressed model. Across four representative headland environments and reference distances ranging from 10 to 30 m, the relative ranging errors ranged from 1.30% to 7.03%, and the total processing latency remained below 45.5 ms under the evaluated conditions. The decision experiments further showed that the proposed method generated headland-width plans and deceleration thresholds consistent with the geometric and kinematic constraints of different tractor–implement combinations and operating conditions. These results support the feasibility of the proposed framework for boundary perception, distance estimation, and behavioral decision support under the evaluated conditions.

To address the challenges posed by the large scale of agricultural machinery trajectory data and the complexity of actual movement trajectories, this paper proposes a two-stage joint clustering method for agricultural machinery trajectory recognition to enhance accuracy and robustness. The first stage involves trajectory clustering, where the spatial distribution characteristics of agricultural machinery trajectories are analyzed, and the position coordinates and the number of neighboring points of trajectory points are extracted as features. The silhouette coefficient method is used to determine the optimal number of clusters k for the K-Means algorithm, thus reducing the data scale. The second stage focuses on trajectory recognition, where a list of Eps and Minpts parameters is generated based on the statistical properties of the trajectory dataset. The Genetic Algorithm is employed for parameter optimization to determine the optimal DBSCAN parameters, enabling precise identification of field operation trajectories and road travel trajectories. Experimental results show that this method achieves mean values of 91.55% for Accuracy, 95.41% for Precision, 89.86% for Recall, and 92.41% for F1-score on a sample dataset of 337 trajectories, representing improvements of 12.8%, 5.13%, 7.79%, and 6.84%, respectively, over the traditional DBSCAN algorithm. Additionally, the Runtime of the two-stage joint clustering method is approximately 30% shorter than that of single-stage clustering. Compared with mainstream deep learning models such as LSTM and Transformer, this method delivers comparable recognition accuracy without the need for labeled data training, significantly reducing recognition costs. The proposed method achieves accurate and robust recognition of agricultural machinery trajectories and holds broad application potential in practical scenarios.

Headland boundary identification and ranging are the key supporting technologies for the automatic driving of intelligent agricultural machinery, and they are also the basis for controlling operational behaviors such as autonomous turning and machine lifting. The complex, unstructured environments of farmland headlands render traditional image feature extraction methods less accurate and adaptable. This study utilizes deep learning and binocular vision technologies to develop a headland boundary identification and ranging system built upon the existing automatic guided tractor test platform. A headland image annotation dataset was constructed, and the MobileNetV3 network, notable for its compact model structure, was employed to achieve binary classification recognition of farmland and headland images. An improved MV3-DeeplabV3+ image segmentation network model, leveraging an attention mechanism, was constructed, achieving a high mean intersection over union (MIoU) value of 92.08% and enabling fast and accurate detection of headland boundaries. Following the detection of headland boundaries, binocular stereo vision technology was employed to measure the boundary distances. Field experiment results indicate that the system’s average relative errors of distance in ranging at distances of 25 m, 20 m, and 15 m are 6.72%, 4.80%, and 4.35%, respectively. This system is capable of meeting the real-time detection requirements for headland boundaries.

The service system of supervision of agricultural machinery subsoiling operation enables acquisition of a large amount of agricultural machinery movement track data. These trajectories include not only farmland operation track data, but also road driving track data. Their spatial distribution characteristics and attribute data are different. In this paper, we make a study of the abnormal trajectory data in data set, and propose an abnormal trajectory recognition algorithm based on DBSCAN clustering. According to the attribute data of agricultural machinery trajectory, the trajectory is divided to form different types of motion trajectory, then to judge the spatial distribution of different types of agricultural machinery tracks. If the attribute data of the tracks are inconsistent with their spatial distribution, it will be judged as abnormal tracks. The experimental results show that both the accuracy of the algorithm and the recall rate is 98.61%, which can identify the abnormal tracks of agricultural machinery.