Addressing local sparsity in massive agricultural machinery trajectories: A BiLSTM-GRU approach

Abstract

Trajectory data acquired from GNSS (Global Navigation Satellite System) terminals on agricultural machinery are crucial for identifying agricultural machinery operation modes, evaluating agricultural machinery operational efficiency and exploring agricultural machinery trans-regional harvesting operation characteristics. However, GNSS terminals often experience signal delays due to factors such as weather conditions and environmental obstructions. These delays result in irregular time intervals between trajectory points, leading to local sparsity within the trajectory data, which subsequently reduces the accuracy of applications and analyses based on agricultural machinery trajectories. To address this issue, we propose a novel approach that leverages Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks, along with an attention mechanism, to mitigate the problem of local trajectory sparsity, and experiments were conducted using agricultural machinary trajectory data collected during the 2023 wheat harvest period. The results demonstrate the efficiency of our approach by successfully resolving the local sparsity of agricultural machinery trajectories. Moreover, each newly added trajectory point contains all original attributes (e.g., speed and direction). When integrated into state-of-the-art algorithms (e.g., DT, DBSCAN + rules, GCN) for identifying machinery operation modes, our method improves accuracies by 21.83 %, 26.86 %, and 1.17 %, respectively. Our approach effectively addresses the issue of local trajectory sparsity, thus providing assistance for applications and studies based on massive agricultural machinery trajectories.