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Field–Road Operation Classification of Agricultural Machine GNSS Trajectories Using Spatio-Temporal Neural Network.
- Source :
- Agronomy; May2023, Vol. 13 Issue 5, p1415, 11p
- Publication Year :
- 2023
-
Abstract
- The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]
- Subjects :
- GLOBAL Positioning System
ARTIFICIAL neural networks
AGRICULTURE
WHEAT harvesting
Subjects
Details
- Language :
- English
- ISSN :
- 20734395
- Volume :
- 13
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Agronomy
- Publication Type :
- Academic Journal
- Accession number :
- 163951880
- Full Text :
- https://doi.org/10.3390/agronomy13051415