Chen Ying, Kuang Kaiming, Wu Caicong
College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing, China.
PeerJ Comput Sci. 2024 Mar 28;10:e1945. doi: 10.7717/peerj-cs.1945. eCollection 2024.
Field-road classification, which automatically identifies in-field activities and out-of-field activities in global navigation satellite system (GNSS) recordings, is an important step for the performance evaluation of agricultural machinery. Although several field-road classification methods based only on GNSS recordings have been proposed, there is a trade-off between time consumption and accuracy performance for such methods. To obtain an optimal balance, it is important to choose a suitable field-road classification method for each trajectory based on its GNSS trajectory quality. In this article, a trajectory classification task was proposed, which classifies the quality of GNSS trajectories into three categories (high-quality, medium-quality, or low-quality). Then, a trajectory classification (TC) model was developed to automatically assign a quality category to each input trajectory, utilizing global and local features specific to agricultural machinery. Finally, a novel field-road classification method is proposed, wherein the selection of field-road classification methods depends on the trajectory quality category predicted by the TC model. The comprehensive experiments show that the proposed trajectory classification method achieved 86.84% accuracy, which consistently outperformed current trajectory classification methods by about 2.6%, and the proposed field-road classification method has obtained a balance between efficiency and effectiveness, , sufficient efficiency with a tolerable accuracy loss. This is the first attempt to examine the balance problem between efficiency and effectiveness in existing field-road classification methods and to propose a trajectory classification specific to these methods.
田间道路分类可在全球导航卫星系统（GNSS）记录中自动识别田间活动和田间外活动，是评估农业机械性能的重要一步。尽管已经提出了几种仅基于GNSS记录的田间道路分类方法，但这些方法在时间消耗和准确性之间存在权衡。为了实现最佳平衡，根据GNSS轨迹质量为每个轨迹选择合适的田间道路分类方法很重要。在本文中，提出了一种轨迹分类任务，将GNSS轨迹质量分为三类（高质量、中等质量或低质量）。然后，开发了一种轨迹分类（TC）模型，利用农业机械特有的全局和局部特征自动为每个输入轨迹分配一个质量类别。最后，提出了一种新颖的田间道路分类方法，其中田间道路分类方法的选择取决于TC模型预测的轨迹质量类别。综合实验表明，所提出的轨迹分类方法准确率达到86.84%，始终比当前轨迹分类方法高出约2.6%，并且所提出的田间道路分类方法在效率和有效性之间取得了平衡，即在可容忍的精度损失下具有足够的效率。这是首次尝试研究现有田间道路分类方法中效率和有效性之间的平衡问题，并针对这些方法提出一种轨迹分类方法。
