TY  - EJOU
AU  - Hao, Fengqi 
AU  - Hou, Yawen 
AU  - Gao, Conghui 
AU  - Bai, Jinqiang 
AU  - Liu, Gang 
AU  - Kong, Hoiio 
AU  - Dong, Xiangjun 

TI  - AgroGeoDB-Net: A DBSCAN-Guided Augmentation and Geometric-Similarity Regularised Framework for GNSS Field–Road Classification in Precision Agriculture
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 87
IS  - 3
SN  - 1546-2226

AB  - Field–road classification, a fine-grained form of agricultural machinery operation-mode identification, aims to use Global Navigation Satellite System (GNSS) trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads. Existing methods struggle with highly imbalanced class distributions, noisy measurements, and intricate spatiotemporal dependencies. This paper presents AgroGeoDB-Net, a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations: (i) a Density-Aware Local Interpolator (DALI), which balances the minority road class via density-aware interpolation while preserving road-segment structure; and (ii) a geometry-aware training objective that couples a DBSCAN-weighted focal loss with a density-regularised KL divergence, ensuring that both classification and latent representations reflect local trajectory density. The workflow first converts each enriched GNSS point into a 14-dimensional motion–spatial descriptor, projects it into a compact latent space through a variational auto-encoder, and then applies a residual BiLSTM to model bidirectional temporal dependencies before a linear classifier produces point-wise field–road predictions. Experiments on wheat, corn, and paddy datasets show overall accuracies of 98.62%, 95.46%, and 93.35%, with consistently stronger road class and overall performance than existing methods. Ablation studies further confirm that both the residual shortcut and DALI contribute positively, with DALI providing the greatest benefit for the minority road class. Tests on the unseen Harvester and Tractor datasets also demonstrate strong generalisation to previously unseen datasets. Taken together, the results show that AgroGeoDB-Net delivers reliable and scalable field–road classification from GNSS trajectories.
KW  - Field-road classification; imbalanced dataset; spatiotemporal features; deep learning

DO  - 10.32604/cmc.2026.077252