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/.

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory research. In the present study, to effectively identify agricultural machinery operation mode, a feature deformation network with multi-range feature enhancement was proposed. First, a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory data. Second, to further enrich the representation of trajectories, a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature maps. Then, EfficientNet-B0 was used to extract features of different scales and depths from the feature map, select features highly relevant to the results, and finally accurately predict the mode of each trajectory point. To validate the effectiveness of the proposed method, experiments were conducted to compare the results with those of other methods on a dataset of real agricultural trajectories. On the corn and wheat harvester trajectory datasets, the model achieved accuracies of 96.88% and 96.68%, as well as F1 scores of 93.54% and 94.19%, exhibiting improvements of 8.35% and 9.08% in accuracy and 20.99% and 20.04% in F1 score compared with the current state-of-the-art method.