In the agricultural domain, agricultural machinery trajectory operation mode identification is essential for spatiotemporal trajectory processing. Its goal is to classify machinery trajectories into road travel or field operations by extracting latent spatiotemporal features. However, conventional models lack effective feature enhancement and ignore the varying importance of trajectory points, thereby weakening feature representation and reducing identification accuracy. To overcome these challenges, a self-supervised learning-based GAT is proposed for identifying agricultural machinery trajectory operation modes. First, to enhance trajectory feature representation, a multi-dimensional feature enhancement module is introduced based on statistical methods. To mitigate the impact of redundant features on model performance, a bidirectional feature fusion module is subsequently proposed that captures both interpoint and intrapoint dependencies, thereby enhancing spatiotemporal representations and suppressing irrelevant information. Next, to capture the importance of trajectory points, a graph attention network (GAT) with a masked attention mechanism is introduced. Finally, to reduce the dependence on labeled data and improve the model’s feature learning capability, self-supervised learning is used as a pretraining step for the GAT. To evaluate SSL-GAT, experiments are conducted on two real-world paddy and wheat harvester datasets. For the paddy dataset, SSL-GAT reaches 95.92% accuracy and a 92.42% F1-score, exceeding those of GAN-BiLSTM by 4.67% and 5.24%, respectively. On the wheat dataset, it achieves 93.92% accuracy and a 90.72% F1-score, with gains of 5.58% and 4.49% over GAN-BiLSTM. These results collectively demonstrate that our SSL-GAT model achieves superior performance, establishing it as a new state-of-the-art model in agricultural machinery trajectory operation mode identification.

Leveraging extensive trajectory data to analyze the operation modes of agricultural machinery for gathering precise spatial information is an important fundamental task for subsequent agricultural machinery trajectory research. However, complex algorithm models hinder nonspecialized researchers from further processing agricultural machinery trajectory data. In the present application note, ChatGPT is taken as an example and a complete prompt guide for large language models (LLMs) is provided for autonomously identifying the operation mode of agricultural machinery. This guide provides low-cost workflows for processing agricultural machinery trajectory data when computer science or data science expertise is lacking. It even possesses the capability to utilize newly learned algorithms such as the random forest model, which has not been previously explored in the literature for operation mode identification, to accomplish the task. To the best of our knowledge, this is the first attempt to apply LLMs to identifying agricultural machinery operation modes based on trajectory data. The complete prompt guide is publicly available at https://github.com/kakushuu/prompt-guide/.